From 7f987270feffd812dc4c1f1886279da159df82be Mon Sep 17 00:00:00 2001 From: "Ryan C. Cooper" Date: Thu, 16 Jan 2020 15:30:51 -0500 Subject: [PATCH] added project and finalized notes --- README.html | 85 + README.md | 72 + .../athletes-checkpoint.csv | 11539 --------- .../compiled_data-checkpoint.csv | 314 + ...mperature_anomaly-1880-2016-checkpoint.csv | 142 + ...nergy-consumption-by-region-checkpoint.csv | 5066 ++++ data/.~lock.transistor_data.csv# | 1 + data/archery.jpg | Bin 0 -> 112565 bytes data/athletes.csv | 11539 --------- ...d_global_temperature_anomaly-1880-2016.csv | 142 + data/primary-energy-consumption-by-region.csv | 5066 ++++ data/transistor_data.csv | 187 + data/transistors-per-microprocessor.csv | 25 + images/robot_design.png | Bin 0 -> 34145 bytes images/robot_design.svg | 1397 ++ .../01_Cheers_Stats_Beers-checkpoint.ipynb | 415 +- .../02_Seeing_Stats-checkpoint.ipynb | 185 +- ...Regression_with_Real_Data-checkpoint.ipynb | 974 + .../03_stats_and_montecarlo-checkpoint.ipynb | 19908 ---------------- ...Regression_with_Real_Data-checkpoint.ipynb | 1246 - .../04_Stats_and_Montecarlo-checkpoint.ipynb | 958 + notebooks/01_Cheers_Stats_Beers.ipynb | 198 +- notebooks/02_Seeing_Stats.ipynb | 185 +- .../03_Linear_Regression_with_Real_Data.ipynb | 974 + notebooks/03_stats_and_montecarlo.ipynb | 19908 ---------------- .../04_Linear_Regression_with_Real_Data.ipynb | 1094 - notebooks/04_Stats_and_Montecarlo.ipynb | 958 + notebooks/octave-workspace | Bin 54 -> 0 bytes .../02_Analyze-data_project-checkpoint.ipynb | 140 + .../process_target_practice-checkpoint.ipynb | 5984 +++++ project/02_Analyze-data_project.ipynb | 140 + project/process_target_practice.ipynb | 5984 +++++ 32 files changed, 29110 insertions(+), 65716 deletions(-) create mode 100644 README.html create mode 100644 README.md delete mode 100644 data/.ipynb_checkpoints/athletes-checkpoint.csv create mode 100644 data/.ipynb_checkpoints/compiled_data-checkpoint.csv create mode 100644 data/.ipynb_checkpoints/land_global_temperature_anomaly-1880-2016-checkpoint.csv create mode 100644 data/.ipynb_checkpoints/primary-energy-consumption-by-region-checkpoint.csv create mode 100644 data/.~lock.transistor_data.csv# create mode 100644 data/archery.jpg delete mode 100644 data/athletes.csv create mode 100644 data/land_global_temperature_anomaly-1880-2016.csv create mode 100644 data/primary-energy-consumption-by-region.csv create mode 100644 data/transistor_data.csv create mode 100644 data/transistors-per-microprocessor.csv create mode 100644 images/robot_design.png create mode 100644 images/robot_design.svg create mode 100644 notebooks/.ipynb_checkpoints/03_Linear_Regression_with_Real_Data-checkpoint.ipynb delete mode 100644 notebooks/.ipynb_checkpoints/03_stats_and_montecarlo-checkpoint.ipynb delete mode 100644 notebooks/.ipynb_checkpoints/04_Linear_Regression_with_Real_Data-checkpoint.ipynb create mode 100644 notebooks/.ipynb_checkpoints/04_Stats_and_Montecarlo-checkpoint.ipynb create mode 100644 notebooks/03_Linear_Regression_with_Real_Data.ipynb delete mode 100644 notebooks/03_stats_and_montecarlo.ipynb delete mode 100644 notebooks/04_Linear_Regression_with_Real_Data.ipynb create mode 100644 notebooks/04_Stats_and_Montecarlo.ipynb delete mode 100644 notebooks/octave-workspace create mode 100644 project/.ipynb_checkpoints/02_Analyze-data_project-checkpoint.ipynb create mode 100644 project/.ipynb_checkpoints/process_target_practice-checkpoint.ipynb create mode 100644 project/02_Analyze-data_project.ipynb create mode 100644 project/process_target_practice.ipynb diff --git a/README.html b/README.html new file mode 100644 index 0000000..f43c53b --- /dev/null +++ b/README.html @@ -0,0 +1,85 @@ + + + + + + + README + + + + + +

Computational Mechanics 02 - Analyze Data

+

Learning some statistics and data processing skills in Python

+

Welcome to Computational Mechanics Module #2 - Analyze Data

+

There are four modules and one final project. The modules will get us started on our exploration of computational mechanics using Python, listed below each module are the learning objectives.

+
    +
  1. 01_Cheers_Stats_Beers
    2. +
+ +
    +
  1. 02_Seeing_Stats
    2. +
+ +
    +
  1. 03_Linear_Regression_with_Real_Data
    2. +
+ +
    +
  1. 04_Stats_and_Montecarlo
    2. +
+ +

+The computer only generates pseudo-random numbers. For further information and truly random numbers check www.random.org

+

Computational Mechanics Project #02 - Create specifications for a spitballingrobot

+

On the first day of class, we threw \(2"\times~2"\) dampened paper (spitballs) at a target on the whiteboard. Now, we are going to analyze the accuracy of the class with some cool Python tools and design a robot that has the same accuracy and precision as the class.

+

The goal of this project is to determine the precision of necessary components for a robot that can reproduce the class throwing distibution. We have generated pseudo random numbers using numpy.random, but the class target practice is an example of truly random distributions. If we repeated the exercise, there is a vanishingly small probability that we would hit the same points on the target, and there are no deterministic models that could take into account all of the factors that affected each hit on the board.

+

+ + diff --git a/README.md b/README.md new file mode 100644 index 0000000..2db3add --- /dev/null +++ b/README.md @@ -0,0 +1,72 @@ +# Computational Mechanics 02 - Analyze Data +## Learning some statistics and data processing skills in Python + +Welcome to Computational Mechanics Module #2 - Analyze Data + +There are four modules and one final project. The modules will get us started on our exploration of computational +mechanics using Python, listed below each module are the learning objectives. + + +1. [01_Cheers_Stats_Beers](./notebooks/01_Cheers_Stats_Beers.ipynb) + * Read data from a `csv` file using `pandas`. + * The concepts of Data Frame and Series in `pandas`. + * Clean null (NaN) values from a Series using `pandas`. + * Convert a `panda`s Series into a `numpy` array. + * Compute maximum and minimum, and range. + * Revise concept of mean value. + * Compute the variance and standard deviation. + * Use the mean and standard deviation to understand how the data is distributed + * Plot frequency distribution diagrams (histograms). + * Normal distribution and 3-sigma rule. + +2. [02_Seeing_Stats](./notebooks/02_Seeing_Stats.ipynb) + * You should always plot your data. + * The concepts of quantitative and categorical data. + * Plotting histograms directly on columns of dataframes, using `pandas`. + * Computing variance and standard deviation using NumPy built-in functions. + * The concept of median, and how to compute it with NumPy. + * Making box plots using `pyplot`. + * Five statistics of a box plot: the quartiles Q1, Q2 (median) and Q3 (and + * interquartile range Q3$-$Q1), upper and lower extremes. + * Visualizing categorical data with bar plots. + * Visualizing multiple data with scatter plots and bubble charts. + * `pandas` is awesome! + +3. [03_Linear_Regression_with_Real_Data](./notebooks/03_Linear_Regression_with_Real_Data.ipynb) + * Making our plots more beautiful + * Defining and calling custom Python functions + * Applying linear regression to data + * NumPy built-ins for linear regression + * The Earth is warming up!!! + +4. [04_Stats_and_Montecarlo](./notebooks/04_Stats_and_Montecarlo.ipynb) + - How to generate "random" numbers in Python+ + - The definition of a Monte Carlo model + - How to calculate $\pi$ with Monte Carlo + - How to take the integral of a function with Monte Carlo + - How to propagate uncertainty in a model with Monte Carlo + - **Bonus**: use Sympy to do calculus and algebra for us! _no need for + - Wolfram, sorry Stephen_ + - How to generate a normal distribution using uniformly random numbers + + +The computer only generates pseudo-random numbers. For further + information **and** truly random numbers check + [www.random.org](https://www.random.org/randomness/) + +## [Computational Mechanics Project #02 - Create specifications for a spitballing robot](./project/02_Analyze-data_project.ipynb) + +On the first day of class, we threw $2"\times~2"$ dampened paper (spitballs) at +a target on the whiteboard. Now, we are going to analyze the accuracy of the +class with some cool Python tools and design a robot that has the same accuracy +and precision as the class. + +The goal of this project is to determine the precision of necessary components +for a robot that can reproduce the class throwing distibution. We have generated +pseudo random numbers using `numpy.random`, but the class target practice is an +example of truly random distributions. If we repeated the exercise, there is a +vanishingly small probability that we would hit the same points on the target, +and there are no deterministic models that could take into account all of the +factors that affected each hit on the board. + + + diff --git a/data/.ipynb_checkpoints/athletes-checkpoint.csv b/data/.ipynb_checkpoints/athletes-checkpoint.csv deleted file mode 100644 index a53ac54..0000000 --- a/data/.ipynb_checkpoints/athletes-checkpoint.csv +++ /dev/null @@ -1,11539 +0,0 @@ -id,name,nationality,sex,date_of_birth,height,weight,sport,gold,silver,bronze -736041664,A Jesus Garcia,ESP,male,1969-10-17,1.72,64.0,athletics,0,0,0 -532037425,A Lam Shin,KOR,female,1986-09-23,1.68,56.0,fencing,0,0,0 -435962603,Aaron Brown,CAN,male,1992-05-27,1.98,79.0,athletics,0,0,1 -521041435,Aaron Cook,MDA,male,1991-01-02,1.83,80.0,taekwondo,0,0,0 -33922579,Aaron Gate,NZL,male,1990-11-26,1.81,71.0,cycling,0,0,0 -173071782,Aaron Royle,AUS,male,1990-01-26,1.8,67.0,triathlon,0,0,0 -266237702,Aaron Russell,USA,male,1993-06-04,2.05,98.0,volleyball,0,0,1 -382571888,Aaron Younger,AUS,male,1991-09-25,1.93,100.0,aquatics,0,0,0 -87689776,Aauri Lorena Bokesa,ESP,female,1988-12-14,1.8,62.0,athletics,0,0,0 -997877719,Ababel Yeshaneh,ETH,female,1991-07-22,1.65,54.0,athletics,0,0,0 -343694681,Abadi Hadis,ETH,male,1997-11-06,1.7,63.0,athletics,0,0,0 -591319906,Abbas Abubakar Abbas,BRN,male,1996-05-17,1.75,66.0,athletics,0,0,0 -258556239,Abbas Qali,IOA,male,1992-10-11,,,aquatics,0,0,0 -376068084,Abbey D'Agostino,USA,female,1992-05-25,1.61,49.0,athletics,0,0,0 -162792594,Abbey Weitzeil,USA,female,1996-12-03,1.78,68.0,aquatics,1,1,0 -521036704,Abbie Brown,GBR,female,1996-04-10,1.76,71.0,rugby sevens,0,0,0 -149397772,Abbos Rakhmonov,UZB,male,1998-07-07,1.61,57.0,wrestling,0,0,0 -256673338,Abbubaker Mobara,RSA,male,1994-02-18,1.75,64.0,football,0,0,0 -337369662,Abby Erceg,NZL,female,1989-11-20,1.75,68.0,football,0,0,0 -334169879,Abd Elhalim Mohamed Abou,EGY,male,1989-06-03,2.1,88.0,volleyball,0,0,0 -215053268,Abdalaati Iguider,MAR,male,1987-03-25,1.73,57.0,athletics,0,0,0 -763711985,Abdalelah Haroun,QAT,male,1997-01-01,1.85,80.0,athletics,0,0,0 -924593601,Abdalla Targan,SUD,male,1996-09-28,1.77,65.0,athletics,0,0,0 -578032534,Abdel Aziz Mehelba,EGY,male,1988-12-10,1.76,80.0,shooting,0,0,0 -890222258,Abdelati El Guesse,MAR,male,1993-02-27,1.9,72.0,athletics,0,0,0 -803161695,Abdelaziz Merzougui,ESP,male,1991-08-30,1.75,67.0,athletics,0,0,0 -189931373,Abdelaziz Mohamed Ahmed,SUD,male,1994-10-12,1.81,72.0,aquatics,0,0,0 -677622742,Abdelghani Demmou,ALG,male,1989-01-29,1.85,75.0,football,0,0,0 -349871091,Abdelhafid Benchabla,ALG,male,1986-09-26,1.86,,boxing,0,0,0 -904808208,Abdelhakim Amokrane,ALG,male,1994-05-10,1.86,70.0,football,0,0,0 -23564778,Abdelkader Chadi,ALG,male,1986-12-12,1.78,,boxing,0,0,0 -133974151,Abdelkadir Salhi,ALG,male,1993-03-19,1.85,79.0,football,0,0,0 -189886442,Abdelkebir 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Khadre Mbaye Niane,SEN,male,1988-08-20,1.9,90.0,aquatics,0,0,0 -259675127,Abdoulkarim Fawziya,CMR,female,1989-03-01,1.8,67.0,volleyball,0,0,0 -469953606,Abdoullah Bamoussa,ITA,male,1986-06-08,,,athletics,0,0,0 -962468808,Abdoulrazak Issoufou Alfaga,NIG,male,1994-12-26,2.07,98.0,taekwondo,0,1,0 -958967643,Abdul Khalili,SWE,male,1992-06-07,1.81,71.0,football,0,0,0 -325809293,Abdul Omar,GHA,male,1993-10-03,,,boxing,0,0,0 -152408417,Abdul Wahab Zahiri,AFG,male,1992-05-27,1.75,68.0,athletics,0,0,0 -262868423,Abdulaziz Alshatti,IOA,male,1990-10-30,,,fencing,0,0,0 -101781750,Abdulkadir Abdullayev,AZE,male,1988-07-17,1.88,,boxing,0,0,0 -5763609,Abdullah Abkar Mohammed,KSA,male,1997-01-01,1.72,73.0,athletics,0,0,0 -969824503,Abdullah Alrashidi,IOA,male,1963-08-21,1.83,84.0,shooting,0,0,1 -897549624,Abdullah Hel Baki,BAN,male,1989-08-01,,,shooting,0,0,0 -153457,Abdullahi Shehu,NGR,male,1993-03-12,1.7,,football,0,0,1 -803368584,Abdullo Tangriev,UZB,male,1981-03-28,1.9,132.0,judo,0,0,0 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Hanzlickova,CZE,female,1994-05-04,1.71,67.0,wrestling,0,0,0 -325622113,Adelina Bogus,ROU,female,1988-09-04,1.86,75.0,rowing,0,0,1 -518639420,Adelina Pastor,ROU,female,1993-05-05,1.68,56.0,athletics,0,0,0 -457663778,Adelinde Cornelissen,NED,female,1979-07-07,1.68,55.0,equestrian,0,0,0 -259002430,Adeline Maria Gray,USA,female,1991-01-15,1.73,77.0,wrestling,0,0,0 -303297765,Adem Boudjemline,ALG,male,1994-02-28,1.8,85.0,wrestling,0,0,0 -993710181,Adenizia da Silva,BRA,female,1986-12-18,1.87,65.0,volleyball,0,0,0 -878847288,Adham Ahmed Saleh Kahk,EGY,male,1993-06-27,1.6,66.0,wrestling,0,0,0 -75289002,Adilbek Niyazymbetov,KAZ,male,1989-05-19,1.8,,boxing,0,1,0 -930618704,Adilson da Silva,BRA,male,1972-01-24,1.7,79.0,golf,0,0,0 -890593128,Aditi Ashok,IND,female,1998-03-29,1.73,57.0,golf,0,0,0 -456381770,Adlan Abdurashidov,RUS,male,1990-07-31,1.72,,boxing,0,0,0 -256936407,Adnan Maric,SWE,male,1997-02-17,1.83,78.0,football,0,0,0 -637015297,Adrian Andres Puentes Perez,CUB,male,1988-07-03,1.71,76.0,archery,0,0,0 -787208883,Adrian Baches,BRA,male,1990-04-07,1.84,83.0,aquatics,0,0,0 -941631433,Adrian Blocki,POL,male,1990-04-11,1.74,62.0,athletics,0,0,0 -839462979,Adrian Chacon,CUB,male,1988-12-10,1.87,90.0,athletics,0,0,0 -284555275,Adrian Crisan,ROU,male,1980-05-07,1.86,85.0,table tennis,0,0,0 -366608701,Adrian Dziolko,POL,male,1990-02-22,1.89,85.0,badminton,0,0,0 -679510102,Adrian Eduardo Goide Arredondo,CUB,male,1998-06-26,1.91,80.0,volleyball,0,0,0 -242818896,Adrian Edward Zielinski,POL,male,1989-03-28,1.7,94.0,weightlifting,0,0,0 -400838338,Adrian Gavira Collado,ESP,male,1987-09-17,1.93,89.0,volleyball,0,0,0 -557196811,Adrian Gomboc,SLO,male,1995-01-20,1.7,69.0,judo,0,0,0 -653186701,Adrian Griffith,BAH,male,1984-11-11,1.78,75.0,athletics,0,0,0 -217850967,Adrian Ignacio Carambula Raurich,ITA,male,1988-03-16,1.82,83.0,volleyball,0,0,0 -370799805,Adrian Juhasz,HUN,male,1989-11-18,1.88,89.0,rowing,0,0,0 -714030791,Adrian Oquendo,CUB,male,1990-02-23,1.89,87.0,rowing,0,0,0 -915726586,Adrian Portela,ARG,male,1986-03-08,1.85,95.0,handball,0,0,0 -80367651,Adriana Aparecida da Silva,BRA,female,1981-07-22,1.66,52.0,athletics,0,0,0 -593328448,Adriana Araujo,BRA,female,1981-11-04,1.67,,boxing,0,0,0 -770039735,Adriana Diaz,PUR,female,2000-10-31,1.6,50.0,table tennis,0,0,0 -228337858,Adriana Martin,ESP,female,1996-04-12,1.62,65.0,archery,0,0,0 -529241363,Adriana Moises,BRA,female,1978-12-06,1.7,65.0,basketball,0,0,0 -439382128,Adrien Bart,FRA,male,1991-09-04,1.85,84.0,canoe,0,0,0 -326016199,Adrien Dipanda,FRA,male,1988-05-03,2.02,105.0,handball,0,1,0 -664737236,Adrien Niyonshuti,RWA,male,1987-01-02,1.65,68.0,cycling,0,0,0 -447910544,Adrienne Martelli,USA,female,1987-12-03,1.86,77.0,rowing,0,0,0 -835610998,Adzo Rebecca Kpossi,TOG,female,1999-01-25,1.58,53.0,aquatics,0,0,0 -26797739,Ae-Ri Noort,NED,female,1983-01-10,1.6,49.0,rowing,0,0,0 -577601340,Afa Ismail,MDV,female,1993-11-01,,,athletics,0,0,0 -16830155,Afaf Elhodhod,EGY,female,1996-10-01,1.63,75.0,shooting,0,0,0 -508610852,Afef Ben Ismail,TUN,female,1994-03-17,1.73,60.0,canoe,0,0,0 -477738986,Africa Zamorano Sanz,ESP,female,1998-01-11,1.7,58.0,aquatics,0,0,0 -998446972,Afrodite Zegers,NED,female,1991-12-02,1.61,58.0,sailing,0,0,0 -42409524,Agata Forkasiewicz,POL,female,1994-01-13,1.68,54.0,athletics,0,0,0 -28097741,Agatha Bednarczuk,BRA,female,1983-06-22,1.82,70.0,volleyball,0,1,0 -23462736,Ageze Guadie,ISR,male,1989-09-11,1.75,56.0,athletics,0,0,0 -971796358,Aglaia Pezzato,ITA,female,1994-04-22,1.75,60.0,aquatics,0,0,0 -810258362,Agnes Osazuwa,NGR,female,1989-11-26,1.62,65.0,athletics,0,0,0 -107504792,Agnes Raharolahy,FRA,female,1992-11-07,1.67,58.0,athletics,0,0,0 -320335499,Agnese Pastare,LAT,female,1988-10-27,1.79,68.0,athletics,0,0,0 -220410298,Agnete Kirk Thinggaard,DEN,female,1983-05-18,1.72,62.0,equestrian,0,0,0 -726686017,Agnieszka Dygacz,POL,female,1985-07-18,1.6,47.0,athletics,0,0,0 -517578131,Agnieszka Jadwiga Wieszczek-Kordus,POL,female,1983-03-22,1.75,69.0,wrestling,0,0,0 -120505360,Agnieszka Jerzyk,POL,female,1988-01-15,1.7,59.0,triathlon,0,0,0 -665715516,Agnieszka Kobus,POL,female,1990-08-28,1.77,68.0,rowing,0,0,1 -935702937,Agnieszka Nagay,POL,female,1981-02-20,1.69,61.0,shooting,0,0,0 -220520929,Agnieszka Radwanska,POL,female,1989-03-06,1.73,59.0,tennis,0,0,0 -527420548,Agnieszka Skrzypulec,POL,female,1989-06-03,1.66,53.0,sailing,0,0,0 -983479171,Agnieszka Szwarnog,POL,female,1986-12-28,1.65,51.0,athletics,0,0,0 -638298176,Agustin Mazzilli,ARG,male,1989-06-20,1.73,77.0,hockey,1,0,0 -833236523,Agustin Vidal,ARG,male,1987-07-08,1.94,100.0,handball,0,0,0 -794585269,Agustina Albertarrio,ARG,female,1993-01-01,1.65,55.0,hockey,0,0,0 -441542780,Agustina Habif,ARG,female,1992-03-08,1.65,55.0,hockey,0,0,0 -213448220,Ahmad Abughaush,JOR,male,1996-02-01,1.78,68.0,taekwondo,1,0,0 -443237540,Ahmad Alafasi,IOA,male,1983-01-10,,,shooting,0,0,0 -942902378,Ahmad Amsyar Azman,MAS,male,1992-08-28,1.62,62.0,aquatics,0,0,0 -684631479,Ahmad Hazer,LIB,male,1989-09-04,1.88,80.0,athletics,0,0,0 -67793782,Ahmed Abdelaal,EGY,male,1989-06-08,1.88,89.0,volleyball,0,0,0 -935849614,Ahmed Abdelhay,EGY,male,1984-08-19,1.97,87.0,volleyball,0,0,0 -786273274,Ahmed Abelrahman,EGY,male,1996-05-26,1.65,60.0,judo,0,0,0 -672789779,Ahmed Afifi,EGY,male,1988-03-30,1.94,92.0,volleyball,0,0,0 -873200853,Ahmed Akram,EGY,male,1996-10-20,1.88,80.0,aquatics,0,0,0 -862822782,Ahmed Ali,SUD,male,1993-11-15,1.73,75.0,athletics,0,0,0 -9418987,Ahmed Attellesey,LBA,male,1995-07-30,,,aquatics,0,0,0 -180751684,Ahmed Bader Magour,QAT,male,1996-03-03,1.9,90.0,athletics,0,0,0 -364294388,Ahmed Darwish,EGY,male,1981-07-02,1.72,80.0,shooting,0,0,0 -23970897,Ahmed El Kotb,EGY,male,1991-07-23,1.97,80.0,volleyball,0,0,0 -837684987,Ahmed El-Nemr,EGY,male,1978-11-21,1.83,84.0,archery,0,0,0 -635571670,Ahmed Elahmar,EGY,male,1984-01-27,1.81,79.0,handball,0,0,0 -64299896,Ahmed Gebrel,PLE,male,1991-01-22,,,aquatics,0,0,0 -215275770,Ahmed Goumar,NIG,male,1988-02-22,1.8,73.0,judo,0,0,0 -663592491,Ahmed Ibrahim,IRQ,male,1992-02-25,1.84,80.0,football,0,0,0 -916701520,Ahmed Kamar,EGY,male,1986-09-19,1.75,60.0,shooting,0,0,0 -114527133,Ahmed Mathlouthi,TUN,male,1989-12-18,1.9,90.0,aquatics,0,0,0 -205324342,Ahmed Mohamed,EGY,male,1996-04-08,1.7,73.0,shooting,0,0,0 -162169475,Ahmed Mohamed,EGY,male,1988-04-27,1.85,144.0,weightlifting,0,0,0 -739968078,Ahmed Mohamed Ibrahim Saad,EGY,male,1989-02-25,1.65,85.0,wrestling,0,0,0 -575397765,Ahmed Ragab,EGY,male,1991-09-29,1.74,81.0,sailing,0,0,0 -101574216,Ahmed Saad,EGY,male,1986-11-01,1.6,62.0,weightlifting,0,0,0 -105465611,Ahmed Shaban,EGY,male,1979-03-08,1.9,95.0,shooting,0,0,0 -585852107,Ahmet Li,TUR,male,1991-01-12,1.73,67.0,table tennis,0,0,0 -696999235,Ahmet Orken,TUR,male,1993-03-12,1.77,69.0,cycling,0,0,0 -47988332,Ahreum Na,KOR,female,1990-03-24,1.63,55.0,cycling,0,0,0 -138385356,Ahymara Espinoza,VEN,female,1985-05-28,1.8,99.0,athletics,0,0,0 -730862558,Ai Fukuhara,JPN,female,1988-11-01,1.56,48.0,table tennis,0,0,1 -488478701,Ai Kondo Yoshida,JPN,female,1980-11-05,1.61,58.0,sailing,0,0,0 -975475720,Ai Ueda,JPN,female,1983-10-26,1.55,44.0,triathlon,0,0,0 -544308054,Ai Wen Yu,TPE,female,1995-12-27,1.57,52.0,shooting,0,0,0 -696719880,Aiaal Lazarev,KGZ,male,1986-03-19,1.95,125.0,wrestling,0,0,0 -634178292,Aichen Wang,CHN,male,1985-03-28,1.85,75.0,sailing,0,0,0 -537808926,Aida Fall,SEN,female,1986-11-10,1.93,95.0,basketball,0,0,0 -155951731,Aida Mohamed,HUN,female,1976-03-12,1.62,58.0,fencing,0,0,0 -531077009,Aida Roman,MEX,female,1988-05-21,1.69,64.0,archery,0,0,0 -750356217,Aida Shanaeva,RUS,female,1986-04-23,1.73,63.0,fencing,0,0,0 -248085477,Aidan Roach,AUS,male,1990-09-07,1.87,88.0,aquatics,0,0,0 -743883365,Aika Hakoyama,JPN,female,1991-07-27,1.76,62.0,aquatics,0,0,1 -110469224,Aikaterini Nikolaidou,GRE,female,1992-10-22,1.8,70.0,rowing,0,0,0 -823094336,Aikaterini-Maria Kontochristopoulou,GRE,female,1997-06-10,1.68,58.0,fencing,0,0,0 -574808106,Aiko Hayashi,JPN,female,1993-11-17,1.66,56.0,aquatics,0,0,1 -676958658,Aiko Sugihara,JPN,female,1999-09-19,1.46,35.0,gymnastics,0,0,0 -420613340,Aileen Reid,IRL,female,1982-06-15,1.69,53.0,triathlon,0,0,0 -172676108,Ailen Valente,ARG,female,1996-03-26,1.67,58.0,gymnastics,0,0,0 -971329341,Ailly Luciano,NED,female,1991-03-25,1.71,69.0,handball,0,0,0 -356290857,Ailun Guo,CHN,male,1993-11-14,1.92,85.0,basketball,0,0,0 -273034565,Aimee Fisher,NZL,female,1995-01-24,1.83,99.0,canoe,0,0,0 -266091485,Aimee Willmott,GBR,female,1993-02-26,1.71,66.0,aquatics,0,0,0 -912490592,Aina Cid I Centelles,ESP,female,1994-09-01,1.7,60.0,rowing,0,0,0 -674464910,Ainhoa Hernandez,ESP,female,1994-04-27,1.8,88.0,handball,0,0,0 -14183642,Ainhoa Murua,ESP,female,1978-07-18,1.6,48.0,triathlon,0,0,0 -871394696,Ainur Yesbergenova,KAZ,female,1998-02-11,1.64,49.0,taekwondo,0,0,0 -635098552,Airi Hatakeyama,JPN,female,1994-08-16,1.71,46.0,gymnastics,0,0,0 -965051843,Airine Palsyte,LTU,female,1992-07-13,1.87,63.0,athletics,0,0,0 -840071617,Aisen Chen,CHN,male,1995-10-22,1.68,60.0,aquatics,2,0,0 -704825548,Aisha Praught,JAM,female,1989-12-14,1.62,50.0,athletics,0,0,0 -704391947,Aislin Jones,AUS,female,2000-02-08,1.57,51.0,shooting,0,0,0 -273177958,Aisuluu Tynybekova,KGZ,female,1993-05-04,1.7,58.0,wrestling,0,0,0 -191963338,Aitor Martinez Rodriguez,ESP,male,1993-08-22,1.83,78.0,aquatics,0,0,0 -678408880,Aivi Luik,AUS,female,1985-03-18,1.63,58.0,football,0,0,0 -234338084,Ajee Wilson,USA,female,1994-05-08,1.73,61.0,athletics,0,0,0 -374861025,Ajla del Ponte,SUI,female,1996-07-15,1.68,56.0,athletics,0,0,0 -504635619,Ajna Kesely,HUN,female,2001-09-10,1.65,54.0,aquatics,0,0,0 -680600926,Akalani Baravilala,USA,female,1991-07-12,1.68,74.0,rugby sevens,0,0,0 -213555250,Akane Kuroki,JPN,female,1978-08-13,1.65,50.0,equestrian,0,0,0 -58865414,Akane Shibata,JPN,female,1988-04-30,1.53,50.0,hockey,0,0,0 -677730191,Akane Yamaguchi,JPN,female,1997-06-06,1.56,55.0,badminton,0,0,0 -265984930,Akani Simbine,RSA,male,1993-09-21,1.76,74.0,athletics,0,0,0 -285797294,Akashdeep Singh,IND,male,1994-12-02,1.78,70.0,hockey,0,0,0 -769580282,Akeem Haynes,CAN,male,1992-03-11,1.68,71.0,athletics,0,0,1 -194243947,Akela Jones,BAR,female,1995-04-22,1.86,77.0,athletics,0,0,0 -58705332,Aki Mitsuhashi,JPN,female,1989-09-12,1.66,59.0,hockey,0,0,0 -921789441,Aki Yazawa,JPN,female,1991-11-05,1.56,50.0,canoe,0,0,0 -327133724,Akihiko Nakamura,JPN,male,1990-10-23,1.8,73.0,athletics,0,0,0 -346354435,Akiko Sato,JPN,female,1984-02-09,1.62,58.0,shooting,0,0,0 -850163240,Akira Ioane,NZL,male,1995-06-16,1.94,113.0,rugby sevens,0,0,0 -816408353,Akira Yanase,JPN,male,1988-08-11,1.91,95.0,aquatics,0,0,0 -959689266,Akossiwa Claire Ayivon,TOG,female,1996-08-11,,,rowing,0,0,0 -152032519,Akua Obeng-Akrofi,GHA,female,1996-07-10,,,athletics,0,0,0 -593452734,Alaa Ali,IRQ,male,1996-06-03,1.78,68.0,football,0,0,0 -747058984,Alaaeldin Abouelkassem,EGY,male,1990-11-25,1.88,87.0,fencing,0,0,0 -249203070,Alade Aminu,NGR,male,1987-09-14,2.1,102.0,basketball,0,0,0 -158571661,Alain Sign,GBR,male,1986-02-03,1.81,84.0,sailing,0,0,0 -471888446,Alan Campbell,GBR,male,1983-05-09,1.91,96.0,rowing,0,0,0 -275659106,Alan Hatherly,RSA,male,1996-03-15,1.78,62.0,cycling,0,0,0 -488926827,Alan Sinclair,GBR,male,1985-10-16,1.92,96.0,rowing,0,0,0 -386179468,Alan Sothern,IRL,male,1987-07-28,1.78,76.0,hockey,0,0,0 -68100583,Alana Barber,NZL,female,1987-07-08,1.63,52.0,athletics,0,0,0 -662496867,Alana Boyd,AUS,female,1984-05-10,1.71,59.0,athletics,0,0,0 -812220330,Alanna Kennedy,AUS,female,1995-01-21,1.76,63.0,football,0,0,0 -923852920,Alastair Brogdon,GBR,male,1987-11-10,1.83,76.0,hockey,0,0,0 -533114803,Alba Torrens,ESP,female,1989-08-30,1.91,68.0,basketball,0,1,0 -551413911,Albachir Mouctar,NIG,male,1995-05-01,1.78,76.0,aquatics,0,0,0 -92096398,Albane Valenzuela,SUI,female,1997-12-17,1.74,58.0,golf,0,0,0 -930879907,Albert Espanol Lifante,ESP,male,1985-10-29,1.89,86.0,aquatics,0,0,0 -596935178,Albert Gaun,RUS,male,1992-06-21,1.85,80.0,taekwondo,0,0,0 -70764024,Albert Hermoso Farras,ESP,male,1978-08-28,1.68,65.0,equestrian,0,0,0 -172435291,Albert Kibichii Rop,BRN,male,1992-07-17,1.77,63.0,athletics,0,0,0 -389597142,Albert Puig Garrich,ESP,male,1994-04-01,1.84,78.0,aquatics,0,0,0 -184326688,Albert Ramon Ramirez,VEN,male,1992-05-07,1.82,,boxing,0,0,0 -100190722,Albert Ramos-Vinolas,ESP,male,1988-01-17,1.88,80.0,tennis,0,0,0 -366742302,Albert Saritov,ROU,male,1985-07-08,1.88,100.0,wrestling,0,0,1 -670559048,Albert Selimov,AZE,male,1986-04-05,1.71,,boxing,0,0,0 -124573287,Albert Subirats,VEN,male,1986-09-25,1.9,85.0,aquatics,0,0,0 -635016740,Alberth Bravo,VEN,male,1987-08-29,1.99,88.0,athletics,0,0,0 -502881474,Alberth Elis,HON,male,1996-02-12,1.84,77.0,football,0,0,0 -583503806,Albertina Cruz Kassoma,ANG,female,1996-06-12,1.92,105.0,handball,0,0,0 -804217525,Alberto Alvarez,MEX,male,1991-03-08,1.91,78.0,athletics,0,0,0 -331799449,Alberto Ezequiel Melian,ARG,male,1990-01-02,1.7,,boxing,0,0,0 -532248667,Alberto Fernandez,ESP,male,1983-06-16,1.8,115.0,shooting,0,0,0 -566773050,Alberto Ignacio Palmetta,ARG,male,1990-04-05,1.75,,boxing,0,0,0 -220269839,Alberto Munarriz Egana,ESP,male,1994-05-19,1.98,105.0,aquatics,0,0,0 -861268031,Alberto Ricchetti,ITA,male,1985-05-26,1.86,88.0,canoe,0,0,0 -663206046,Albin Lagergren,SWE,male,1992-09-11,1.86,94.0,handball,0,0,0 -966601149,Albina Shakirova,RUS,female,1987-03-30,1.65,50.0,shooting,0,0,0 -74286490,Aldemir da Silva Junior,BRA,male,1992-06-08,1.93,80.0,athletics,0,0,0 -46123715,Aldo Montano,ITA,male,1978-11-18,1.84,82.0,fencing,0,0,0 -343089125,Alec Potts,AUS,male,1996-02-09,1.85,77.0,archery,0,0,1 -210525666,Aleixo-Platini Menga,GER,male,1987-09-29,1.82,82.0,athletics,0,0,0 -969294495,Alejandra Betancur,COL,female,1987-06-13,1.6,82.0,rugby sevens,0,0,0 -388913856,Alejandra Garza Garza,MEX,female,1991-08-01,1.65,75.0,weightlifting,0,0,0 -883610308,Alejandra Jhonay Benitez Romero,VEN,female,1980-07-07,1.69,62.0,fencing,0,0,0 -694204413,Alejandra Llaneza,MEX,female,1988-05-31,1.58,48.0,golf,0,0,0 -400375379,Alejandra Orozco,MEX,female,1997-04-19,1.57,56.0,aquatics,0,0,0 -151538357,Alejandra Ortega,MEX,female,1994-07-08,1.65,57.0,athletics,0,0,0 -281414786,Alejandra Quereda,ESP,female,1992-07-24,1.75,54.0,gymnastics,0,1,0 -471263119,Alejandra Teran,MEX,female,1991-01-20,1.76,73.0,fencing,0,0,0 -929131268,Alejandra Valencia,MEX,female,1994-10-17,1.75,74.0,archery,0,0,0 -419155357,Alejandra Zavala Vazquez,MEX,female,1984-06-16,1.65,85.0,shooting,0,0,0 -440339638,Alejandro Enrique Valdes Tobier,CUB,male,1988-11-18,1.65,65.0,wrestling,0,0,0 -691394107,Alejandro Foglia Costa,URU,male,1984-01-30,1.9,96.0,sailing,0,0,0 -813391088,Alejandro Valverde Belmonte,ESP,male,1980-04-25,1.77,63.0,cycling,0,0,0 -288835407,Aleksandar Aleksandrov,AZE,male,1990-04-09,1.89,93.0,rowing,0,0,0 -299839075,Aleksandar IVOVIC,MNE,male,1986-02-24,1.97,107.0,aquatics,0,0,0 -371740701,Aleksandar Karakasevic,SRB,male,1975-12-09,1.78,85.0,table tennis,0,0,0 -150583899,Aleksandar Kukolj,SRB,male,1991-09-09,1.94,94.0,judo,0,0,0 -156829148,Aleksandar Nikolov,BUL,male,1992-06-18,1.85,75.0,aquatics,0,0,0 -39610779,Aleksandar RADOVIC,MNE,male,1987-02-24,1.91,98.0,aquatics,0,0,0 -150706476,Aleksandr Krasnykh,RUS,male,1995-06-19,1.88,80.0,aquatics,0,0,0 -461398245,Aleksandr Lipatov,RUS,male,1981-06-10,1.84,77.0,canoe,0,0,0 -825626228,Aleksandr Markov,RUS,male,1985-05-26,1.79,70.0,equestrian,0,0,0 -26501082,Aleksandr Popkov,RUS,male,1994-12-27,1.85,80.0,aquatics,0,0,0 -442737224,Aleksandr Sadovnikov,RUS,male,1996-09-21,1.93,85.0,aquatics,0,0,0 -928101165,Aleksandra Cotti,ITA,female,1988-12-13,1.67,65.0,aquatics,0,1,0 -15347644,Aleksandra Crvendakic,SRB,female,1996-03-17,1.87,76.0,basketball,0,0,1 -398269007,Aleksandra Jarmolinska,POL,female,1990-09-06,1.6,67.0,shooting,0,0,0 -344407164,Aleksandra Krunic,SRB,female,1993-03-15,1.63,55.0,tennis,0,0,0 -598990857,Aleksandra Patskevich,RUS,female,1988-11-04,1.69,49.0,aquatics,1,0,0 -832551080,Aleksandra Romanova,KAZ,female,1990-12-26,1.68,58.0,athletics,0,0,0 -778823226,Aleksandra Socha,POL,female,1982-03-30,1.74,66.0,fencing,0,0,0 -146965989,Aleksandra Urbanczyk,POL,female,1987-11-13,1.72,64.0,aquatics,0,0,0 -265023366,Aleksandrs Samoilovs,LAT,male,1985-04-06,1.96,98.0,volleyball,0,0,0 -282139882,Aleksei Brianskii,RUS,male,1997-09-14,1.92,82.0,aquatics,0,0,0 -646633611,Aleksei Kurbatov,RUS,male,1994-05-09,,,cycling,0,0,0 -200955001,Aleksejs Rumjancevs,LAT,male,1986-02-13,1.85,90.0,canoe,0,0,0 -285964258,Aleksejs Saramotins,LAT,male,1982-04-08,1.85,75.0,cycling,0,0,0 -169040459,Aleksey Mochalov,UZB,male,1990-02-13,1.86,90.0,canoe,0,0,0 -191984367,Aleksi Ojala,FIN,male,1992-12-09,1.8,62.0,athletics,0,0,0 -462274321,Alemu Bekele,BRN,male,1990-03-23,1.62,59.0,athletics,0,0,0 -571628094,Alen Zasieiev,UKR,male,1988-10-10,1.85,125.0,wrestling,0,0,0 -871020123,Alena Abramchuk,BLR,female,1988-02-14,1.82,95.0,athletics,0,0,0 -603507285,Alena Amialiusik,BLR,female,1989-02-06,1.69,52.0,cycling,0,0,0 -390806588,Alena Furman,BLR,female,1991-05-08,1.79,65.0,rowing,0,0,0 -84230211,Alena Sharp,CAN,female,1981-03-07,1.68,69.0,golf,0,0,0 -30712954,Alena Sobaleva,BLR,female,1993-05-11,1.78,87.0,athletics,0,0,0 -906750380,Alesha Widdall,USA,female,1990-01-03,1.73,65.0,hockey,0,0,0 -779596907,Alessandra Aguilar,ESP,female,1978-07-01,1.65,50.0,athletics,0,0,0 -236415837,Alessandra Patelli,ITA,female,1991-11-17,1.8,70.0,rowing,0,0,0 -820764186,Alessandra Perilli,SMR,female,1988-04-01,1.69,73.0,shooting,0,0,0 -630246233,Alessandro De Marchi,ITA,male,1986-05-19,1.81,66.0,cycling,0,0,0 -24174970,Alessandro Fabian,ITA,male,1988-01-07,1.86,77.0,triathlon,0,0,0 -213338722,Alessandro Nora,ITA,male,1987-05-24,1.91,85.0,aquatics,0,0,1 -346410285,Alessandro Velotto,ITA,male,1995-02-12,1.86,85.0,aquatics,0,0,1 -5076328,Alessia Gennari,ITA,female,1991-11-03,1.84,68.0,volleyball,0,0,0 -559742871,Alessia Maurelli,ITA,female,1996-08-22,1.68,48.0,gymnastics,0,0,0 -488353513,Alessia Orro,ITA,female,1998-07-18,1.8,74.0,volleyball,0,0,0 -767623428,Alessia Polieri,ITA,female,1994-10-21,1.63,55.0,aquatics,0,0,0 -389711690,Alessia Trost,ITA,female,1993-03-08,1.88,70.0,athletics,0,0,0 -636505415,Aletta Jorritsma,NED,female,1989-05-17,1.86,76.0,rowing,0,0,0 -746309995,Alev Kelter,USA,female,1991-03-21,1.68,74.0,rugby sevens,0,0,0 -178787882,Alex Abrines,ESP,male,1993-08-01,1.98,93.0,basketball,0,0,1 -527236860,Alex Amankwah,GHA,male,1992-03-02,,,athletics,0,0,0 -24122998,Alex Beddoes,COK,male,1995-07-09,1.81,74.0,athletics,0,0,0 -334218519,Alex Bowen,USA,male,1993-09-04,1.96,102.0,aquatics,0,0,0 -962040149,Alex Casasayas,ESP,male,1988-02-17,1.83,78.0,hockey,0,0,0 -794799982,Alex Cejka,GER,male,1970-12-02,1.68,65.0,golf,0,0,0 -566695148,Alex Garcia,BRA,male,1980-03-04,1.92,102.0,basketball,0,0,0 -204746498,Alex Garcia Mendoza,CUB,male,1993-06-02,1.86,110.0,judo,0,0,0 -544745828,Alex Gregory,GBR,male,1984-03-11,1.98,97.0,rowing,1,0,0 -851889356,Alex Hua Tian,CHN,male,1989-10-25,1.9,70.0,equestrian,0,0,0 -212901658,Alex Kennedy,NZL,male,1992-10-13,1.96,96.0,rowing,0,0,0 -967635529,Alex Maloney,NZL,female,1992-03-19,1.58,56.0,sailing,0,1,0 -723568012,Alex Morgan,USA,female,1989-07-02,1.7,59.0,football,0,0,0 -531765497,Alex Obert,USA,male,1991-12-18,1.96,105.0,aquatics,0,0,0 -557501936,Alex Ranghieri,ITA,male,1987-06-18,2.0,94.0,volleyball,0,0,0 -240862321,Alex Roelse,USA,male,1995-01-10,2.04,115.0,aquatics,0,0,0 -107240268,Alex Rose,SAM,male,1991-11-17,,,athletics,0,0,0 -58596600,Alex Sobers,BAR,male,1998-11-13,1.88,66.0,aquatics,0,0,0 -703205900,Alex William Pombo Silva,BRA,male,1988-07-21,1.74,73.0,judo,0,0,0 -636907352,Alex Wright,IRL,male,1990-12-19,1.75,62.0,athletics,0,0,0 -893073673,Alex di Giorgio,ITA,male,1990-07-28,1.85,75.0,aquatics,0,0,0 -644656107,Alexa Moreno,MEX,female,1994-08-08,1.47,45.0,gymnastics,0,0,0 -898510239,Alexander Belonogoff,AUS,male,1990-04-17,1.87,90.0,rowing,0,1,0 -583622968,Alexander Brouwer,NED,male,1989-11-03,1.98,90.0,volleyball,0,0,1 -771200886,Alexander Bryukhankov,RUS,male,1987-04-12,1.84,79.0,triathlon,0,0,0 -525300567,Alexander Bury,BLR,male,1987-09-14,1.84,94.0,tennis,0,0,0 -750329482,Alexander Choupenitch,CZE,male,1994-05-02,1.96,89.0,fencing,0,0,0 -737791847,Alexander Edmondson,AUS,male,1993-12-22,1.84,76.0,cycling,0,1,0 -3104691,Alexander Fransson,SWE,male,1994-04-02,1.8,75.0,football,0,0,0 -100528515,Alexander Gehbauer,AUT,male,1990-04-24,1.85,70.0,cycling,0,0,0 -515896035,Alexander Hartmann,AUS,male,1993-03-07,1.98,91.0,athletics,0,0,0 -986385043,Alexander Hendrickx,BEL,male,1993-08-06,1.85,82.0,hockey,0,0,0 -711926626,Alexander Hill,AUS,male,1993-03-11,1.93,91.0,rowing,0,1,0 -45190859,Alexander Horst,AUT,male,1982-12-20,1.86,88.0,volleyball,0,0,0 -724710281,Alexander Huber,AUT,male,1985-07-25,1.79,64.0,volleyball,0,0,0 -604407494,Alexander John,GER,male,1986-05-03,1.85,84.0,athletics,0,0,0 -300967214,Alexander Karwoski,USA,male,1990-09-16,1.94,90.0,rowing,0,0,0 -41863727,Alexander Kosenkow,GER,male,1977-03-14,1.78,74.0,athletics,0,0,0 -5256036,Alexander Leksell,SWE,male,1997-02-14,1.76,78.0,football,0,0,0 -37893138,Alexander Lerionka Sampao,KEN,male,1996-12-31,,67.0,athletics,0,0,0 -911821683,Alexander Lesun,RUS,male,1988-07-01,1.85,74.0,modern pentathlon,1,0,0 -703140166,Alexander Lloyd,AUS,male,1990-12-17,1.89,89.0,rowing,0,0,0 -994257957,Alexander Massialas,USA,male,1994-04-20,1.88,81.0,fencing,0,1,1 -517168133,Alexander Milosevic,SWE,male,1992-01-30,1.92,77.0,football,0,0,0 -589365060,Alexander Molerio Quintana,CUB,male,1991-09-19,1.69,75.0,shooting,0,0,0 -536913332,Alexander Naddour,USA,male,1991-03-04,1.71,70.0,gymnastics,0,0,1 -110525640,Alexander Peya,AUT,male,1980-06-27,1.82,80.0,tennis,0,0,0 -658069337,Alexander Russo,BRA,male,1994-07-26,1.73,61.0,athletics,0,0,0 -648973939,Alexander Schmirl,AUT,male,1989-09-19,1.76,95.0,shooting,0,0,0 -961318209,Alexander Shatilov,ISR,male,1987-03-22,1.83,77.0,gymnastics,0,0,0 -909129710,Alexander Shibaev,RUS,male,1990-09-09,1.92,80.0,table tennis,0,0,0 -452039868,Alexander Sigurbjornsson,ESP,male,1988-12-13,1.8,80.0,rowing,0,0,0 -484197906,Alexander Sukhorukov,RUS,male,1988-02-22,1.96,93.0,aquatics,0,0,0 -120799596,Alexander Volkov,RUS,male,1985-02-14,2.1,90.0,volleyball,0,0,0 -745034260,Alexandr Spac,MDA,male,1989-11-21,1.64,77.0,weightlifting,0,0,0 -188141447,Alexandr Yemelyanov,KAZ,male,1984-01-01,1.84,97.0,canoe,0,0,0 -928138606,Alexandr Zaichikov,KAZ,male,1992-08-17,1.8,105.0,weightlifting,0,0,1 -878621949,Alexandra Burghardt,GER,female,1994-04-28,1.81,70.0,athletics,0,0,0 -566681185,Alexandra Danson,GBR,female,1985-05-21,1.67,56.0,hockey,1,0,0 -612116227,Alexandra Hagan,AUS,female,1991-03-21,1.82,75.0,rowing,0,0,0 -383231190,Alexandra Keresztesi,ARG,female,1983-04-26,1.83,72.0,canoe,0,0,0 -191014767,Alexandra Lacrabere,FRA,female,1987-04-27,1.77,73.0,handball,0,1,0 -486083635,Alexandra Longova,SVK,female,1994-02-07,1.73,51.0,archery,0,0,0 -30769207,Alexandra Martinez,BRA,female,1981-09-16,1.77,68.0,handball,0,0,0 -921114489,Alexandra Mirca,MDA,female,1993-10-11,1.68,70.0,archery,0,0,0 -338753895,Alexandra Nemich,KAZ,female,1995-01-03,1.68,46.0,aquatics,0,0,0 -599952871,Alexandra Oquendo,PUR,female,1984-02-03,1.89,75.0,volleyball,0,0,0 -125810149,Alexandra Popp,GER,female,1991-04-06,1.74,65.0,football,1,0,0 -891965491,Alexandra Privalova,BLR,female,1987-10-29,1.78,66.0,table tennis,0,0,0 -71010173,Alexandra Raisman,USA,female,1994-05-25,1.58,52.0,gymnastics,1,2,0 -256577556,Alexandra Razarenova,RUS,female,1990-07-17,1.68,56.0,triathlon,0,0,0 -692519892,Alexandra Tavernier,FRA,female,1993-12-13,1.7,82.0,athletics,0,0,0 -443690040,Alexandra Touretski,SUI,female,1994-09-20,1.7,58.0,aquatics,0,0,0 -475551107,Alexandra Wenk,GER,female,1995-02-07,1.8,63.0,aquatics,0,0,0 -872947130,Alexandra Wester,GER,female,1994-03-21,1.8,64.0,athletics,0,0,0 -753747706,Alexandre Ayache,FRA,male,1982-09-20,1.8,80.0,equestrian,0,0,0 -356907302,Alexandre Bouzaid,SEN,male,1981-06-29,1.68,75.0,fencing,0,0,0 -538814395,Alexandre Camarasa,FRA,male,1987-06-10,1.93,100.0,aquatics,0,0,0 -333303277,Alexandre Camargo,BRA,male,1999-04-25,1.77,75.0,fencing,0,0,0 -718560734,Alexandre Haldemann,SUI,male,1995-03-08,1.86,81.0,aquatics,0,0,0 -203028845,Alexandre Iddir,FRA,male,1991-02-21,1.84,90.0,judo,0,0,0 -391830541,Alexandre de Paeuw,BEL,male,1988-10-07,1.78,75.0,hockey,0,0,0 -350839117,Alexandres Gounas,GRE,male,1989-10-03,1.79,73.0,aquatics,0,0,0 -72024697,Alexandrina Cabral,ESP,female,1986-05-05,1.75,62.0,handball,0,0,0 -17092453,Alexandro Pozzer,BRA,male,1988-12-21,1.92,107.0,handball,0,0,0 -809372786,Alexandros Papamichail,GRE,male,1988-09-18,1.78,63.0,athletics,0,0,0 -992947913,Alexei Klimov,RUS,male,1975-08-27,1.82,88.0,shooting,0,0,0 -621322055,Alexei Sancov,MDA,male,1999-10-15,1.88,85.0,aquatics,0,0,0 -434694198,Alexej Prochorow,GER,male,1990-03-30,1.91,138.0,weightlifting,0,0,0 -271488726,Alexey Alipov,RUS,male,1975-08-07,1.74,80.0,shooting,0,0,0 -251192385,Alexey Cheremisinov,RUS,male,1985-07-09,1.83,75.0,fencing,1,0,0 -318060996,Alexey Denisenko,RUS,male,1993-08-30,1.85,68.0,taekwondo,0,1,0 -221070420,Alexey Dergunov,KAZ,male,1984-09-16,1.87,95.0,canoe,0,0,0 -333787984,Alexey Verbov,RUS,male,1982-01-31,1.83,79.0,volleyball,0,0,0 -527521403,Alexey Yakimenko,RUS,male,1983-10-31,1.84,80.0,fencing,0,0,0 -448880966,Alexi Pappas,GRE,female,1990-03-28,,,athletics,0,0,0 -524151751,Alexis Gonzalez,ARG,male,1981-07-21,1.84,85.0,volleyball,0,0,0 -491625914,Alexis Raynaud,FRA,male,1994-08-19,1.72,78.0,shooting,0,0,1 -166278707,Alexis Santos,POR,male,1992-03-23,1.85,74.0,aquatics,0,0,0 -697622072,Alexis Soto,ARG,male,1993-10-20,1.64,66.0,football,0,0,0 -194257222,Alexis Vuillermoz,FRA,male,1988-06-01,1.73,60.0,cycling,0,0,0 -860974311,Alexus Laird,SEY,female,1993-03-11,1.72,61.0,aquatics,0,0,0 -108252301,Alfonso Antonio Leyva Yepez,MEX,male,1993-01-06,1.81,85.0,wrestling,0,0,0 -231869553,Alfonso Benavides Lopez de Ayala,ESP,male,1991-03-09,1.82,86.0,canoe,0,0,0 -475919800,Alfred Kipketer,KEN,male,1996-12-28,1.67,60.0,athletics,0,0,0 -450635887,Alfredo Campo,ECU,male,1993-03-02,1.92,90.0,cycling,0,0,0 -736830151,Alfredo Talavera,MEX,male,1982-09-18,1.88,84.0,football,0,0,0 -233231940,Alhussein Gambour,LBA,male,1989-08-21,1.75,68.0,rowing,0,0,0 -604740736,Ali Adnan,IRQ,male,1993-12-19,1.8,76.0,football,0,0,0 -461686848,Ali Elghrari,LBA,male,1997-01-31,,,archery,0,0,0 -668278260,Ali Eren Demirezen,TUR,male,1990-04-02,1.89,,boxing,0,0,0 -737778764,Ali Faez,IRQ,male,1994-09-09,1.84,75.0,football,0,0,0 -800154411,Ali Hashemi,IRI,male,1991-11-01,1.78,93.0,weightlifting,0,0,0 -931441337,Ali Hisny,IRQ,male,1994-05-23,1.77,70.0,football,0,0,0 -670890480,Ali Kaya,TUR,male,1994-04-20,1.75,54.0,athletics,0,0,0 -732766543,Ali Khalafalla,EGY,male,1996-05-13,1.82,78.0,aquatics,0,0,0 -702507964,Ali Khamis Khamis,BRN,male,1995-06-30,1.82,70.0,athletics,0,0,0 -969053827,Ali Krieger,USA,female,1984-07-28,1.68,63.0,football,0,0,0 -716391442,Ali Messaoudi,ALG,male,1995-10-13,1.78,63.0,athletics,0,0,0 -768503351,Ali Nouisri,TUN,male,1994-01-20,1.84,75.0,cycling,0,0,0 -853472045,Ali Pakdaman,IRI,male,1990-08-23,1.93,94.0,fencing,0,0,0 -89809268,Ali Riley,NZL,female,1987-10-30,1.63,57.0,football,0,0,0 -472145214,Ali Suljic,SWE,male,1997-09-18,1.89,80.0,football,0,0,0 -429169523,Ali Yousef Al Rumaihi,QAT,male,1981-08-26,1.77,86.0,equestrian,0,0,0 -220820586,Ali Zein Mohamed,EGY,male,1990-12-14,1.9,83.0,handball,0,0,0 -76451177,Alia Atkinson,JAM,female,1988-12-11,1.73,71.0,aquatics,0,0,0 -432850961,Alia Saeed Mohammed,UAE,female,1993-11-13,1.58,54.0,athletics,0,0,0 -502227727,Aliaksandr Bersanau,BLR,male,1992-09-01,1.76,94.0,weightlifting,0,0,0 -23998076,Aliaksandr Buikevich,BLR,male,1984-11-19,1.91,80.0,fencing,0,0,0 -564017885,Aliaksandr Liakhovich,BLR,male,1989-07-04,1.72,65.0,athletics,0,0,0 -68967384,Aliaksandra Herasimenia,BLR,female,1985-12-31,1.74,58.0,aquatics,0,0,1 -775336377,Aliaksandra Tarasava,BLR,female,1988-06-23,1.7,64.0,basketball,0,0,0 -185532375,Aliaksei Mzhachyk,BLR,male,1996-06-30,1.9,136.0,weightlifting,0,0,0 -481292461,Alican Kaynar,TUR,male,1988-10-30,1.91,98.0,sailing,0,0,0 -198660870,Alice Aprot Nawowuna,KEN,female,1994-01-02,1.52,54.0,athletics,0,0,0 -824189087,Alice Ingley,AUS,female,1993-01-13,1.72,80.0,archery,0,0,0 -200278263,Alice Mizzau,ITA,female,1993-03-18,1.8,65.0,aquatics,0,0,0 -273156241,Alice Naber-Lozeman,NED,female,1971-05-07,1.68,62.0,equestrian,0,0,0 -713167631,Alice Richardson,GBR,female,1987-05-14,1.72,69.0,rugby sevens,0,0,0 -965421454,Alice Schlesinger,GBR,female,1988-05-26,1.65,65.0,judo,0,0,0 -693106463,Alice Sinno,ITA,female,1992-09-08,1.75,69.0,sailing,0,0,0 -350838880,Alice Sotero,ITA,female,1991-05-28,1.66,53.0,modern pentathlon,0,0,0 -598475958,Alicia Blagg,GBR,female,1996-10-21,1.65,65.0,aquatics,0,0,0 -537043954,Alicia Brown,CAN,female,1990-01-21,1.65,57.0,athletics,0,0,0 -247476216,Alicia Cebrian Martinez de Lagos,ESP,female,1983-02-03,1.68,68.0,sailing,0,0,0 -556923374,Alicia Coutts,AUS,female,1987-09-14,1.76,69.0,aquatics,0,0,0 -906544891,Alicia Magaz,ESP,female,1994-05-24,1.61,60.0,hockey,0,0,0 -404345231,Alicia Quirk,AUS,female,1992-03-28,1.73,64.0,rugby sevens,1,0,0 -284199636,Alicja Tchorz,POL,female,1992-08-13,1.76,68.0,aquatics,0,0,0 -297498164,Alin Alexuc Ciurariu,ROU,male,1990-02-03,1.91,105.0,wrestling,0,0,0 -667093236,Alin Coste,ROU,male,1992-02-17,2.01,95.0,aquatics,0,0,0 -991471809,Alin George Moldoveanu,ROU,male,1983-05-03,1.73,73.0,shooting,0,0,0 -146849551,Alina Armas,NAM,female,1983-12-10,1.66,53.0,athletics,0,0,0 -9436047,Alina Fodorova,UKR,female,1989-07-31,1.75,65.0,athletics,0,0,0 -452472549,Alina Komashchuk,UKR,female,1993-04-24,1.69,75.0,fencing,0,1,0 -26321098,Alina Logvynenko,UKR,female,1990-07-18,1.8,67.0,athletics,0,0,0 -564738938,Alina Rotaru,ROU,female,1993-06-05,1.75,57.0,athletics,0,0,0 -130594149,Alina Stadnik Makhynia,UKR,female,1991-01-03,1.65,69.0,wrestling,0,0,0 -137031539,Alina Talay,BLR,female,1989-05-14,1.64,57.0,athletics,0,0,0 -315643745,Aline,BRA,female,1989-04-15,1.62,60.0,football,0,0,0 -902987523,Aline Focken,GER,female,1991-05-10,1.77,73.0,wrestling,0,0,0 -215286232,Aline da Silva Ferreira,BRA,female,1986-10-18,1.78,80.0,wrestling,0,0,0 -833092832,Aliona Dubitskaya,BLR,female,1990-01-25,1.8,76.0,athletics,0,0,0 -790705612,Alireza Khojasteh,IRI,male,1997-03-28,1.82,66.0,judo,0,0,0 -891915962,Alireza Mohammad Karimimachiani,IRI,male,1994-03-21,1.75,86.0,wrestling,0,0,0 -473463517,Alisa Kano,USA,female,1994-11-07,1.63,47.0,gymnastics,0,0,0 -572752822,Alisa Kiriliuk,RUS,female,1990-07-09,1.65,57.0,sailing,0,0,0 -269057885,Alise Post,USA,female,1991-01-17,1.58,56.0,cycling,0,1,0 -893065758,Alisha Glass,USA,female,1988-04-05,1.84,72.0,volleyball,0,0,1 -409870387,Alison Cerutti,BRA,male,1985-12-07,2.03,102.0,volleyball,1,0,0 -376187965,Alison Young,GBR,female,1987-05-29,1.82,70.0,sailing,0,0,0 -164566610,Alistair Bond,NZL,male,1989-08-16,1.86,71.0,rowing,0,0,0 -198533399,Alistair Brownlee,GBR,male,1988-04-23,1.84,70.0,triathlon,1,0,0 -901422043,Aliya Mustafina,RUS,female,1994-09-30,1.61,48.0,gymnastics,1,1,1 -340019479,Aliyah Abrams,GUY,female,1997-04-03,1.63,53.0,athletics,0,0,0 -248018989,Alize Cornet,FRA,female,1990-01-22,1.73,63.0,tennis,0,0,0 -777919762,Alla Cherkasova,UKR,female,1989-05-05,1.65,75.0,wrestling,0,0,0 -848336898,Alla Shishkina,RUS,female,1989-08-02,1.7,56.0,aquatics,1,0,0 -511388917,Allan Banegas,HON,male,1993-10-04,1.8,76.0,football,0,0,0 -638093339,Allan Fa'alava'au,AUS,male,1993-11-15,1.7,87.0,rugby sevens,0,0,0 -146313505,Allan Gutierrez,HON,male,1993-08-12,1.78,79.0,aquatics,0,0,0 -32531728,Allan Julie,SEY,male,1977-03-23,1.81,91.0,sailing,0,0,0 -40527372,Allan Norregaard,DEN,male,1981-03-19,1.73,67.0,sailing,0,0,0 -846754382,Allan do carmo,BRA,male,1989-08-03,1.74,73.0,aquatics,0,0,0 -165223366,Allans Vargas,HON,male,1993-09-25,1.84,69.0,football,0,0,0 -823501763,Allar Raja,EST,male,1983-06-22,1.9,95.0,rowing,0,0,1 -531225990,Allie Long,USA,female,1987-08-13,1.72,59.0,football,0,0,0 -373002185,Allison Beveridge,CAN,female,1993-06-01,1.69,62.0,cycling,0,0,1 -606328661,Allison M. Brock,USA,female,1979-12-07,1.68,58.0,equestrian,0,0,1 -68050568,Allison Pineau,FRA,female,1989-05-02,1.81,66.0,handball,0,1,0 -944908522,Allison Schmitt,USA,female,1990-06-07,1.86,74.0,aquatics,1,1,0 -881130710,Allistar Clarke,SKN,male,1990-10-03,1.87,71.0,athletics,0,0,0 -686662012,Allysha Chapman,CAN,female,1989-01-25,1.6,58.0,football,0,0,1 -629649792,Allyson Felix,USA,female,1985-11-18,1.68,56.0,athletics,2,1,0 -742474119,Allyson Ponson,ARU,female,1995-12-04,1.65,60.0,aquatics,0,0,0 -830602816,Almat Kebispayev,KAZ,male,1987-12-12,1.66,59.0,wrestling,0,0,0 -9410967,Almaz Ayana,ETH,female,1991-11-21,1.66,47.0,athletics,1,0,1 -949130459,Almir Velagic,GER,male,1981-08-22,1.83,149.0,weightlifting,0,0,0 -712627786,Almuth Schult,GER,female,1991-02-09,1.8,71.0,football,1,0,0 -300655502,Alona Koshevatskiy,ISR,female,1997-10-08,1.7,53.0,gymnastics,0,0,0 -673430,Alona Ribakova,LAT,female,1991-02-07,1.76,63.0,aquatics,0,0,0 -506696121,Alonso Edward,PAN,male,1989-12-08,1.81,76.0,athletics,0,0,0 -396920712,Alonso Valdez Prado,PER,male,1978-01-23,1.7,68.0,equestrian,0,0,0 -791528062,Alonzo Russell,BAH,male,1992-02-08,,,athletics,0,0,1 -860469603,Alphas Leken Kishoyian,KEN,male,1994-01-01,1.67,59.0,athletics,0,0,0 -601227072,Alphonce Felix Simbu,TAN,male,1992-02-14,,55.0,athletics,0,0,0 -46528538,Altobeli da Silva,BRA,male,1990-12-03,1.81,60.0,athletics,0,0,0 -344062200,Alvaro Doda de Miranda,BRA,male,1973-02-05,1.86,86.0,equestrian,0,0,0 -940428888,Alvaro Iglesias,ESP,male,1993-03-01,1.78,79.0,hockey,0,0,0 -407705493,Alvaro Martin,ESP,male,1994-06-18,1.79,63.0,athletics,0,0,0 -349070925,Alvaro de Arriba,ESP,male,1994-06-02,1.77,69.0,athletics,0,0,0 -198544112,Alvin Singh,FIJ,male,1988-06-09,1.85,88.0,football,0,0,0 -350021262,Alyce Burnett,AUS,female,1992-08-11,1.82,71.0,canoe,0,0,0 -362885756,Alyn Camara,GER,male,1989-03-31,1.96,84.0,athletics,0,0,0 -317210453,Alysbeth Felix,PUR,female,1993-03-07,1.71,59.0,athletics,0,0,0 -746918674,Alysha Newman,CAN,female,1994-06-29,1.72,67.0,athletics,0,0,0 -299325832,Alyson Dixon,GBR,female,1978-09-24,1.55,43.0,athletics,0,0,0 -192794585,Alyssa Bull,AUS,female,1995-12-01,1.73,65.0,canoe,0,0,0 -479973349,Alyssa Conley,RSA,female,1991-04-27,1.76,63.0,athletics,0,0,0 -61804195,Alyssa Manley,USA,female,1994-05-27,1.58,53.0,hockey,0,0,0 -360098950,Alyssa Naeher,USA,female,1988-04-20,1.75,61.0,football,0,0,0 -431586792,Alyxandria Treasure,CAN,female,1992-05-15,1.55,68.0,athletics,0,0,0 -461802696,Alzbeta Dufkova,CZE,female,1990-04-19,1.72,65.0,aquatics,0,0,0 -401938001,Amadou Camara,GUI,male,1994-09-10,1.63,69.0,aquatics,0,0,0 -312519582,Amadou Ndiaye,SEN,male,1992-12-06,1.8,80.0,athletics,0,0,0 -974097069,Amaia Erbina,ESP,female,1997-03-13,1.71,68.0,rugby sevens,0,0,0 -595234401,Amaka Ogoegbunam,NGR,female,1990-03-03,1.64,61.0,athletics,0,0,0 -605176733,Amalie Dideriksen,DEN,female,1996-05-24,1.75,62.0,cycling,0,0,0 -398682051,Amalie Iuel,NOR,female,1994-04-17,,,athletics,0,0,0 -543039949,Amalie Thomsen,DEN,female,1994-09-12,1.66,60.0,canoe,0,0,0 -424274474,Amaliya Sharoyan,ARM,female,1988-06-19,1.7,56.0,athletics,0,0,0 -684370128,Aman Wote,ETH,male,1984-04-18,1.83,68.0,athletics,0,0,0 -991318461,Amanda Araujo,BRA,female,1990-02-23,1.62,54.0,rugby sevens,0,0,0 -574915810,Amanda Carr,THA,female,1990-06-24,1.65,60.0,cycling,0,0,0 -461754486,Amanda Dlamini,RSA,female,1988-07-22,1.64,61.0,football,0,0,0 -960382539,Amanda Elmore,USA,female,1991-03-13,1.81,79.0,rowing,1,0,0 -403367229,Amanda Ilestedt,SWE,female,1993-01-17,1.78,65.0,football,0,0,0 -557518345,Amanda Kurtovic,NOR,female,1991-07-25,1.75,,handball,0,0,1 -343711251,Amanda Ng,SIN,female,1994-05-03,1.7,68.0,sailing,0,0,0 -936271742,Amanda Oliveira,BRA,female,1987-01-06,1.69,58.0,aquatics,0,0,0 -839220577,Amanda Polk,USA,female,1986-08-02,1.81,83.0,rowing,1,0,0 -362158810,Amanda Simeao,BRA,female,1994-06-02,1.66,62.0,fencing,0,0,0 -340529563,Amanda Spratt,AUS,female,1987-09-17,1.61,55.0,cycling,0,0,0 -118871166,Amanda Weir,USA,female,1986-03-11,1.88,77.0,aquatics,0,1,0 -537662343,Amandine Henry,FRA,female,1989-09-28,1.71,64.0,football,0,0,0 -17350450,Amandine Leynaud,FRA,female,1986-05-02,1.78,64.0,handball,0,1,0 -582601760,Amandine Lhote,FRA,female,1986-12-22,1.68,64.0,canoe,0,0,0 -994010453,Amanmurad Hommadov,TKM,male,1989-01-28,1.9,117.0,athletics,0,0,0 -40874614,Amanuel Mesel,ERI,male,1990-12-29,1.77,59.0,athletics,0,0,0 -555654573,Amar Music,CRO,male,1987-03-21,1.7,85.0,weightlifting,0,0,0 -347334644,Amarhajy Mahamedau,BLR,male,1990-04-12,1.84,88.0,wrestling,0,0,0 -156297478,Amas Daniel,NGR,male,1990-04-26,1.65,65.0,wrestling,0,0,0 -235304532,Amber Campbell,USA,female,1981-06-05,1.71,90.0,athletics,0,0,0 -518337993,Amber Hearn,NZL,female,1984-11-28,1.73,66.0,football,0,0,0 -843671557,Amber Hill,GBR,female,1997-08-21,1.58,52.0,shooting,0,0,0 -193520300,Ambroise Uwiragiye,RWA,male,1980-12-31,,,athletics,0,0,0 -458169585,Ameen Zakkar,QAT,male,1994-06-15,1.95,100.0,handball,0,0,0 -147471231,Ameer Webb,USA,male,1991-03-19,1.81,82.0,athletics,0,0,0 -39468987,Amel Bouderra,FRA,female,1989-03-26,1.63,,basketball,0,0,0 -140465357,Amel Majri,FRA,female,1993-01-25,1.64,54.0,football,0,0,0 -250216413,Amel Tuka,BIH,male,1991-01-09,1.87,77.0,athletics,0,0,0 -577293191,Amela Terzic,SRB,female,1993-01-02,1.69,54.0,athletics,0,0,0 -255630772,Amelia Belotti,ARG,female,1988-11-17,1.8,73.0,handball,0,0,0 -36505645,Amelia Cozad,USA,female,1991-05-06,1.61,51.0,aquatics,0,0,0 -656221142,Amelia Gibson,NZL,female,1991-07-05,1.72,71.0,hockey,0,0,0 -335567750,Amelia Rosa Fournel,ARG,female,1977-02-20,1.6,50.0,shooting,0,0,0 -310265171,Amelie Kretz,CAN,female,1993-05-19,1.68,53.0,triathlon,0,0,0 -68574249,Amer Hrustanovic,AUT,male,1988-06-11,1.8,91.0,wrestling,0,0,0 -17841864,Ami Kondo,JPN,female,1995-05-09,1.56,48.0,judo,0,0,1 -607523494,Amidou Mir,FRA,male,1995-01-01,1.73,78.0,cycling,0,0,0 -678578041,Amie Thompson,AUS,female,1996-01-31,1.67,58.0,aquatics,0,0,0 -811727068,Amina Bettiche,ALG,female,1987-12-14,1.65,55.0,athletics,0,0,0 -689435497,Amina Kajtaz,BIH,female,1996-12-31,1.73,62.0,aquatics,0,0,0 -409262616,Amina Rouba,ALG,female,1986-01-09,1.73,60.0,rowing,0,0,0 -895684177,Aminat Oluwafunmilayo Adeniyi,NGR,female,1993-04-21,1.65,58.0,wrestling,0,0,0 -509681869,Aminath Shajan,MDV,female,1993-10-29,,,aquatics,0,0,0 -378210672,Amine Bannour,TUN,male,1990-02-21,1.96,102.0,handball,0,0,0 -121680559,Amine Belferar,ALG,male,1991-02-16,1.75,65.0,athletics,0,0,0 -435929879,Amini Tuitavake Fonua,TGA,male,1989-12-14,1.86,80.0,aquatics,0,0,0 -389896499,Aminu Umar,NGR,male,1995-03-06,1.72,,football,0,0,1 -846949924,Amir Ghafour,IRI,male,1991-06-06,2.02,90.0,volleyball,0,0,0 -410626392,Amiran Papinashvili,GEO,male,1988-06-17,1.62,64.0,judo,0,0,0 -364715226,Amit Ivry,ISR,female,1989-09-02,1.7,65.0,aquatics,0,0,0 -529647636,Amjed Attwan,IRQ,male,1997-03-12,1.8,72.0,football,0,0,0 -945479307,Ammar Abdulhussein,IRQ,male,1993-02-13,1.8,76.0,football,0,0,0 -238039408,Ammara Pinto,MAW,female,1997-09-14,1.63,61.0,aquatics,0,0,0 -740490618,Amna Bakhit,SUD,female,1990-11-14,1.75,59.0,athletics,0,0,0 -657073926,Amnat Ruenroeng,THA,male,1979-12-18,1.64,,boxing,0,0,0 -761085260,Amor Ben Yahia,TUN,male,1985-07-01,1.76,63.0,athletics,0,0,0 -942970698,Amro Elgeziry,EGY,male,1986-11-29,1.85,75.0,modern pentathlon,0,0,0 -318291811,Amy Cragg,USA,female,1984-01-21,1.63,45.0,athletics,0,0,0 -872045656,Amy Cure,AUS,female,1992-12-31,1.72,63.0,cycling,0,0,0 -912480643,Amy Millar,CAN,female,1977-02-14,1.83,59.0,equestrian,0,0,0 -5024454,Amy Sene,SEN,female,1986-04-06,1.75,80.0,athletics,0,0,0 -460892006,Amy Tinkler,GBR,female,1999-10-27,1.52,47.0,gymnastics,0,0,1 -99920757,Amy Turner,AUS,female,1984-03-25,1.68,64.0,rugby sevens,1,0,0 -380050996,Amy Wilson Hardy,GBR,female,1991-09-13,1.67,69.0,rugby sevens,0,0,0 -491417402,An-Li Kachelhoffer,RSA,female,1987-08-16,1.6,50.0,cycling,0,0,0 -829962369,Ana Beatriz Bulcao,BRA,female,1993-12-04,1.67,58.0,fencing,0,0,0 -914515820,Ana Cabecinha,POR,female,1984-04-29,1.63,48.0,athletics,0,0,0 -714771450,Ana Claudia Silva,BRA,female,1988-11-06,1.58,56.0,athletics,0,0,0 -627491334,Ana Dabovic,SRB,female,1989-08-18,1.83,70.0,basketball,0,0,1 -697466343,Ana Derek,CRO,female,1998-09-04,1.64,58.0,gymnastics,0,0,0 -196047650,Ana Dulce Felix,POR,female,1982-10-23,1.66,53.0,athletics,0,0,0 -111502894,Ana Gallay,ARG,female,1986-01-16,1.73,66.0,volleyball,0,0,0 -209671126,Ana Iulia Dascal,ROU,female,2002-09-12,1.83,60.0,aquatics,0,0,0 -71309722,Ana Ivanovic,SRB,female,1987-11-06,1.77,63.0,tennis,0,0,0 -685105407,Ana Jose Tima,DOM,female,1989-10-10,1.68,56.0,athletics,0,0,0 -981171195,Ana Konjuh,CRO,female,1997-12-27,1.74,73.0,tennis,0,0,0 -575479562,Ana Luiza Barbachan,BRA,female,1989-08-15,1.71,68.0,sailing,0,0,0 -596603092,Ana Luiza Filiorianu,ROU,female,1999-07-10,1.64,42.0,gymnastics,0,0,0 -69147633,Ana Marcela Cunha,BRA,female,1992-03-23,1.65,66.0,aquatics,0,0,0 -865510243,Ana Maria Popescu,ROU,female,1984-11-26,1.75,64.0,fencing,1,0,0 -350672001,Ana Maria Rendon,COL,female,1986-03-10,1.6,63.0,archery,0,0,0 -453790004,Ana Paula Belo,BRA,female,1987-10-18,1.72,67.0,handball,0,0,0 -724211622,Ana Paula Vergutz,BRA,female,1989-04-20,1.76,71.0,canoe,0,0,0 -808863847,Ana Perez,ESP,female,1997-12-14,1.51,45.0,gymnastics,0,0,0 -272949970,Ana Ramirez,COL,female,1991-12-06,1.68,69.0,rugby sevens,0,0,0 -307991682,Ana Rente,POR,female,1988-04-27,1.58,53.0,gymnastics,0,0,0 -224508833,Ana Roqica,FIJ,female,1988-02-02,1.62,55.0,rugby sevens,0,0,0 -543833436,Ana Sanabria,COL,female,1990-05-02,1.56,51.0,cycling,0,0,0 -337538051,Ana Satila,BRA,female,1996-03-13,1.63,58.0,canoe,0,0,0 -953050325,Ana Simic,CRO,female,1990-05-05,1.78,57.0,athletics,0,0,0 -902638396,Ana Sofia Gomez,GUA,female,1995-11-24,1.56,46.0,gymnastics,0,0,0 -872791807,Ana Sofia Nobrega,ANG,female,1990-12-20,1.74,70.0,aquatics,0,0,0 -644940241,Ana Veronica Rodean,ROU,female,1984-06-23,1.7,55.0,athletics,0,0,0 -153868094,Ana Zaninovic,CRO,female,1987-06-26,1.72,55.0,taekwondo,0,0,0 -557108863,Ana-Roxana Lehaci,AUT,female,1990-08-11,1.72,64.0,canoe,0,0,0 -310379918,Anabel Medina Garrigues,ESP,female,1982-07-31,1.69,59.0,tennis,0,0,0 -241768985,Anabelle Smith,AUS,female,1993-02-03,1.68,55.0,aquatics,0,0,1 -867924792,Anamari Velensek,SLO,female,1991-05-15,1.78,78.0,judo,0,0,1 -575717728,Anamaria Ionita,ROU,female,1988-07-07,1.75,60.0,athletics,0,0,0 -711141982,Anas Beshr,EGY,male,1993-07-19,,,athletics,0,0,0 -922322829,Anaso Jobodwana,RSA,male,1992-07-30,1.87,71.0,athletics,0,0,0 -129887602,Anass Ait El Abdia,MAR,male,1993-03-21,1.8,66.0,cycling,0,0,0 -445010579,Anass Houssein,DJI,male,1995-01-10,1.75,66.0,judo,0,0,0 -320992856,Anastasia Abrosimova,RUS,female,1990-07-17,1.64,51.0,triathlon,0,0,0 -395443232,Anastasia Bliznyuk,RUS,female,1994-06-28,1.73,51.0,gymnastics,1,0,0 -630467842,Anastasia Bogdanovski,MKD,female,1993-07-30,1.74,57.0,aquatics,0,0,0 -301110204,Anastasia Gloushkov Leventhal,ISR,female,1985-05-24,1.65,53.0,aquatics,0,0,0 -823187249,Anastasia Pavlova,UKR,female,1995-02-09,1.72,53.0,archery,0,0,0 -42235167,Anastasia Pavlyuchenkova,RUS,female,1991-07-03,1.77,75.0,tennis,0,0,0 -971263231,Anastasia Rodionova,AUS,female,1982-05-12,,,tennis,0,0,0 -407491531,Anastasia Shlyakhovaya,RUS,female,1990-10-05,1.92,69.0,volleyball,0,0,0 -329483647,Anastasia Simanovich,RUS,female,1995-01-23,1.74,69.0,aquatics,0,0,1 -35258396,Anastasiia Baryshnikova,RUS,female,1990-12-19,1.73,67.0,taekwondo,0,0,0 -901391372,Anastasiia Beliakova,RUS,female,1993-05-01,1.73,,boxing,0,0,1 -102993308,Anastasiia Fesikova,RUS,female,1990-05-08,1.82,71.0,aquatics,0,0,0 -797491738,Anastasiia Hotfrid,GEO,female,1996-04-25,1.69,87.0,weightlifting,0,0,0 -787891097,Anastasiia Kozhenkova,UKR,female,1986-01-19,1.86,75.0,rowing,0,0,0 -572715750,Anastasiia Krapivina,RUS,female,1994-11-12,1.7,56.0,aquatics,0,0,0 -715543829,Anastasiia Lysenko,UKR,female,1995-12-02,1.76,101.0,weightlifting,0,0,0 -273869635,Anastasiia Maksimova,RUS,female,1991-06-27,1.7,50.0,gymnastics,1,0,0 -766355560,Anastasiia Nedobiga,UKR,female,1994-04-20,1.62,50.0,aquatics,0,0,0 -725232541,Anastasiia Tatareva,RUS,female,1997-07-19,1.65,44.0,gymnastics,1,0,0 -913288591,Anastasiia Todorova,UKR,female,1993-12-10,1.68,69.0,canoe,0,0,0 -874606295,Anastasiia Voinova,RUS,female,1993-02-05,1.62,62.0,cycling,0,1,0 -467136839,Anastasija Grigorjeva,LAT,female,1990-05-12,1.69,65.0,wrestling,0,0,0 -85844393,Anastasija Khmelnytska,GER,female,1997-12-31,1.71,56.0,gymnastics,0,0,0 -429085507,Anastasiya Mikhalenka,BLR,female,1995-12-08,1.62,67.0,weightlifting,0,0,0 -477742641,Anastasiya Prokopenko,BLR,female,1985-09-20,1.63,57.0,modern pentathlon,0,0,0 -204362655,Anastasiya Puzakova,BLR,female,1993-12-12,1.61,45.0,athletics,0,0,0 -830248205,Anastasiya Savchuk,UKR,female,1996-03-02,1.77,60.0,aquatics,0,0,0 -369555302,Anastasiya Serdyukova,UZB,female,1997-05-29,1.7,49.0,gymnastics,0,0,0 -610974773,Anastasiya Spas,UKR,female,1993-08-06,1.78,68.0,modern pentathlon,0,0,0 -27612186,Anastasiya Tulapina,KAZ,female,1990-03-05,1.67,54.0,athletics,0,0,0 -711528333,Anastasiya Tyurina,TJK,female,2001-09-27,1.65,50.0,aquatics,0,0,0 -422580925,Anastasiya Verameyenka,BLR,female,1987-07-10,1.92,82.0,basketball,0,0,0 -357399509,Anastasiya Voznyak,UKR,female,1998-12-09,1.69,49.0,gymnastics,0,0,0 -673911488,Anastassiya Pilipenko,KAZ,female,1986-09-13,1.74,55.0,athletics,0,0,0 -347049681,Anastassya Kudinova,KAZ,female,1988-02-27,1.76,60.0,athletics,0,0,0 -623313333,Anatolii Herey,UKR,male,1989-03-31,1.86,75.0,fencing,0,0,0 -234780868,Ancuta Bobocel,ROU,female,1987-10-03,1.67,49.0,athletics,0,0,0 -964678292,Andela Bulatovic,MNE,female,1987-01-15,1.75,67.0,handball,0,0,0 -775627135,Andelo Setka,CRO,male,1985-09-14,1.86,87.0,aquatics,0,1,0 -949150339,Ander Elosegi,ESP,male,1987-11-14,1.86,80.0,canoe,0,0,0 -955892799,Anders Dahl,DEN,male,1976-03-11,1.8,80.0,equestrian,0,0,0 -449230343,Anders Lie Nielsen,DEN,male,1991-04-11,1.86,74.0,aquatics,0,0,0 -289946138,Anders Pedersen,NOR,male,1992-05-04,,,sailing,0,0,0 -130198005,Anders Weiss,USA,male,1992-11-05,1.96,92.0,rowing,0,0,0 -248819461,Anderson Oliveira,BRA,male,1992-01-10,1.69,64.0,canoe,0,0,0 -292858869,Andile Dlamini,RSA,female,1992-09-02,1.69,55.0,football,0,0,0 -375239282,Andile Fikizolo,RSA,male,1994-05-12,1.75,69.0,football,0,0,0 -7192762,Andjelko Risticevic,SRB,male,1985-12-23,1.91,78.0,athletics,0,0,0 -995131091,Andranik Karapetyan,ARM,male,1995-12-15,1.8,77.0,weightlifting,0,0,0 -222880699,Andras Parti,HUN,male,1982-09-18,1.74,67.0,cycling,0,0,0 -257770574,Andras Redli,HUN,male,1983-10-21,1.93,82.0,fencing,0,0,1 -140531805,Andre,POR,male,1990-01-21,1.71,68.0,football,0,0,0 -511065132,Andre Breitbarth,GER,male,1990-04-06,1.91,125.0,judo,0,0,0 -662859051,Andre Fernando S. Martins Cardoso,POR,male,1984-09-03,1.68,57.0,cycling,0,0,0 -954547244,Andre Link,GER,male,1994-12-13,1.74,80.0,shooting,0,0,0 -580669644,Andre Matias,ANG,male,1989-06-22,1.8,76.0,rowing,0,0,0 -134393254,Andre Patrocinio,BRA,male,1990-02-20,1.73,74.0,hockey,0,0,0 -321939680,Andre Pereira,BRA,male,1993-12-07,1.85,84.0,aquatics,0,0,0 -890648404,Andre Sa,BRA,male,1977-05-06,1.8,75.0,tennis,0,0,0 -976716272,Andre Silva,BRA,male,1988-03-22,1.7,80.0,rugby sevens,0,0,0 -755645799,Andre Soares,BRA,male,1984-02-13,1.94,92.0,handball,0,0,0 -857846421,Andre de Grasse,CAN,male,1994-11-10,1.76,70.0,athletics,0,1,2 -174436666,Andrea Arsovic,SRB,female,1987-02-05,1.65,61.0,shooting,0,0,0 -974131096,Andrea Baldini,ITA,male,1985-12-19,1.75,70.0,fencing,0,0,0 -932784458,Andrea Brewster,IRL,female,1982-10-10,1.63,62.0,sailing,0,0,0 -949156252,Andrea Carolina Olaya Gutierrez,COL,female,1994-12-09,1.76,75.0,wrestling,0,0,0 -676115258,Andrea Cassara,ITA,male,1984-01-03,1.93,93.0,fencing,0,0,0 -161123035,Andrea Cedron,PER,female,1993-12-24,1.69,62.0,aquatics,0,0,0 -626748734,Andrea Chiarabini,ITA,male,1995-03-12,1.79,79.0,aquatics,0,0,0 -575091525,Andrea D'Arrigo,ITA,male,1995-04-28,1.94,85.0,aquatics,0,0,0 -400345706,Andrea Deelstra,NED,female,1985-03-06,1.64,46.0,athletics,0,0,0 -419373605,Andrea Fondelli,ITA,male,1994-02-27,1.9,96.0,aquatics,0,0,1 -478977690,Andrea Geubelle,USA,female,1991-06-26,1.63,58.0,athletics,0,0,0 -226707215,Andrea Hewitt,NZL,female,1982-04-04,1.6,50.0,triathlon,0,0,0 -429133485,Andrea Hlavackova,CZE,female,1986-08-10,1.74,63.0,tennis,0,0,0 -504132188,Andrea Ivancevic,CRO,female,1984-08-21,1.68,62.0,athletics,0,0,0 -158226977,Andrea Kilday,NZL,female,1982-08-05,1.61,48.0,taekwondo,0,0,0 -565831572,Andrea Klikovac,MNE,female,1991-05-05,1.75,73.0,handball,0,0,0 -65085149,Andrea Kneppers,NED,female,1993-02-24,1.75,67.0,aquatics,0,0,0 -187085633,Andrea Mayr,AUT,female,1979-10-15,1.74,53.0,athletics,0,0,0 -187152270,Andrea Micheletti,ITA,male,1991-06-22,1.87,70.0,rowing,0,0,0 -931620156,Andrea Miklos,ROU,female,1999-04-17,1.65,51.0,athletics,0,0,0 -448392462,Andrea Murez,ISR,female,1992-01-29,1.84,77.0,aquatics,0,0,0 -244862813,Andrea Perez Pena,ECU,female,1990-04-07,1.61,62.0,shooting,0,0,0 -926139240,Andrea Petkovic,GER,female,1987-09-09,1.8,70.0,tennis,0,0,0 -339790739,Andrea Salvisberg,SUI,male,1989-02-01,1.8,73.0,triathlon,0,0,0 -583417630,Andrea Santarelli,ITA,male,1993-06-03,1.84,76.0,fencing,0,1,0 -976796859,Andrea Seccafien,CAN,female,1990-08-27,1.52,46.0,athletics,0,0,0 -36880136,Andrea Tiberi,ITA,male,1985-11-15,1.83,71.0,cycling,0,0,0 -234036323,Andrea Toniato,ITA,male,1991-02-27,1.82,82.0,aquatics,0,0,0 -321087951,Andreanne Langlois,CAN,female,1993-04-01,1.58,59.0,canoe,0,0,0 -726050696,Andreas Bretschneider,GER,male,1989-08-04,1.67,60.0,gymnastics,0,0,0 -706697738,Andreas Bube,DEN,male,1987-07-13,1.78,65.0,athletics,0,0,0 -566684785,Andreas Chasikos,CYP,male,1984-06-07,1.75,70.0,shooting,0,0,0 -951348078,Andreas Kuffner,GER,male,1987-03-11,1.96,94.0,rowing,0,1,0 -153952805,Andreas Linde,SWE,male,1993-07-24,1.96,88.0,football,0,0,0 -411586865,Andreas Loew,GER,male,1982-01-19,1.77,81.0,shooting,0,0,0 -39605435,Andreas Maxso,DEN,male,1994-03-18,1.9,80.0,football,0,0,0 -435243866,Andreas Nilsson,SWE,male,1990-04-12,1.97,114.0,handball,0,0,0 -31739693,Andreas Pheobus Cariolou,CYP,male,1982-11-24,1.81,74.0,sailing,0,0,0 -713434315,Andreas Schilling,DEN,male,1991-05-25,1.85,70.0,triathlon,0,0,0 -773973841,Andreas Seppi,ITA,male,1984-02-21,1.9,75.0,tennis,0,0,0 -819478867,Andreas Toba,GER,male,1990-10-07,1.72,65.0,gymnastics,0,0,0 -908623609,Andreas Vazaios,GRE,male,1994-05-09,1.9,85.0,aquatics,0,0,0 -103646463,Andreas Wolff,GER,male,1991-03-03,1.98,110.0,handball,0,0,1 -294664221,Andreea Aanei,ROU,female,1993-11-18,1.7,120.0,weightlifting,0,0,0 -945918484,Andreea Arsine,ROU,female,1988-09-14,1.59,55.0,athletics,0,0,0 -755303582,Andreea Boghian,ROU,female,1991-11-29,1.86,78.0,rowing,0,0,1 -966630668,Andreea Chitu,ROU,female,1988-05-07,1.57,52.0,judo,0,0,0 -7713126,Andreea Mitu,ROU,female,1991-09-22,1.75,60.0,tennis,0,0,0 -882676679,Andrei Churyla,BLR,male,1993-05-19,1.89,74.0,athletics,0,0,0 -604822032,Andrei Gag,ROU,male,1991-04-27,1.91,120.0,athletics,0,0,0 -444763711,Andrei Jamsa,EST,male,1982-02-14,1.84,90.0,rowing,0,0,1 -494617052,Andrei Shabasov,RUS,male,1994-06-20,1.8,73.0,aquatics,0,0,0 -355389879,Andrei Vasile Muntean,ROU,male,1993-01-30,1.7,63.0,gymnastics,0,0,0 -153735693,Andrei Zamkovoi,RUS,male,1987-07-04,1.82,,boxing,0,0,0 -957911430,Andreia Bandeira,BRA,female,1987-05-03,1.69,,boxing,0,0,0 -264432420,Andreina Pinto,VEN,female,1991-09-10,1.76,68.0,aquatics,0,0,0 -305139050,Andrej Gacina,CRO,male,1986-05-21,1.85,85.0,table tennis,0,0,0 -10874144,Andrej Martin,SVK,male,1989-09-20,1.8,72.0,tennis,0,0,0 -681704491,Andrej Olijnik,LTU,male,1987-10-16,1.85,87.0,canoe,0,0,0 -448316510,Andres Arroyo,PUR,male,1995-06-07,1.77,64.0,athletics,0,0,0 -723895342,Andres Chocho,ECU,male,1983-11-04,1.7,66.0,athletics,0,0,0 -771905591,Andres Ducasse,CHI,male,1992-06-24,1.71,66.0,sailing,0,0,0 -669378368,Andres Eduardo Mata Perez,ESP,male,1992-11-11,1.74,76.0,weightlifting,0,0,0 -349842092,Andres Mauricio Caicedo Piedrahita,COL,male,1997-08-15,1.74,76.0,weightlifting,0,0,0 -686783587,Andres Mir,ESP,male,1987-01-25,1.8,75.0,hockey,0,0,0 -922616500,Andres Nocioni,ARG,male,1979-11-30,2.01,102.0,basketball,0,0,0 -700618156,Andres Pila,COL,male,1991-05-11,1.74,75.0,archery,0,0,0 -164503062,Andres Renteria,COL,male,1993-03-06,1.81,81.0,football,0,0,0 -446162748,Andres Roa,COL,male,1993-05-25,1.75,68.0,football,0,0,0 -157804987,Andres Roberto Montano Arroyo,ECU,male,1990-04-06,1.66,59.0,wrestling,0,0,0 -841462894,Andres Ruiz,COL,male,1988-07-16,1.64,64.0,athletics,0,0,0 -551708998,Andres Silva,URU,male,1986-03-27,1.78,78.0,athletics,0,0,0 -979721356,Andressa,BRA,female,1995-05-01,1.6,60.0,football,0,0,0 -715702096,Andressa Alves,BRA,female,1992-11-10,1.68,56.0,football,0,0,0 -721349876,Andressa de Morais,BRA,female,1990-12-21,1.78,97.0,athletics,0,0,0 -140014329,Andrew Amonde,KEN,male,1983-12-25,1.9,104.0,rugby sevens,0,0,0 -308408059,Andrew Bogut,AUS,male,1984-11-28,2.07,122.0,basketball,0,0,0 -169560751,Andrew Butchart,GBR,male,1991-10-14,1.75,64.0,athletics,0,0,0 -777045922,Andrew Campbell Jr,USA,male,1992-02-02,1.78,70.0,rowing,0,0,0 -871500671,Andrew Charter,AUS,male,1987-03-30,1.82,87.0,hockey,0,0,0 -164276381,Andrew Chetcuti,MLT,male,1992-11-19,1.77,72.0,aquatics,0,0,0 -207851406,Andrew Durutalo,USA,male,1987-10-25,1.88,107.0,rugby sevens,0,0,0 -2538424,Andrew Evans,USA,male,1991-01-25,1.99,113.0,athletics,0,0,0 -149520623,Andrew Fisher,BRN,male,1991-12-15,,,athletics,0,0,0 -351340396,Andrew Graham Peebles,ZIM,male,1989-01-09,1.91,87.0,rowing,0,0,0 -99309663,Andrew Lewis,TTO,male,1989-11-30,1.77,79.0,sailing,0,0,0 -165292505,Andrew Pozzi,GBR,male,1992-05-15,1.9,84.0,athletics,0,0,0 -926287704,Andrew Riley,JAM,male,1988-09-06,1.85,82.0,athletics,0,0,0 -530085605,Andrew T Hodge,GBR,male,1979-03-03,1.92,97.0,rowing,1,0,0 -191993260,Andrew Thomas Bisek,USA,male,1986-08-18,1.78,81.0,wrestling,0,0,0 -384767370,Andrew Thomas Mlugu,TAN,male,1995-11-12,1.58,73.0,judo,0,0,0 -665587816,Andrew Vernon,GBR,male,1986-01-07,1.82,71.0,athletics,0,0,0 -584794145,Andrew Willis,GBR,male,1990-12-03,1.89,88.0,aquatics,0,0,0 -559300154,Andrew Yorke,CAN,male,1988-12-20,1.9,75.0,triathlon,0,0,0 -930021019,Andrey Amador Bikkazakova,CRC,male,1986-08-29,1.83,73.0,cycling,0,0,0 -642024679,Andrey Ashchev,RUS,male,1983-05-10,2.02,105.0,volleyball,0,0,0 -213371438,Andrey Fonseca,CRC,male,1993-04-08,1.68,56.0,cycling,0,0,0 -915732512,Andrey Grechin,RUS,male,1987-10-21,1.99,99.0,aquatics,0,0,0 -967358166,Andrey Kraitor,RUS,male,1992-11-05,1.79,90.0,canoe,0,0,0 -927974669,Andrey Kuznetsov,RUS,male,1991-02-22,1.85,72.0,tennis,0,0,0 -132173574,Andrey Likhovitskiy,BLR,male,1986-06-23,1.72,66.0,gymnastics,0,0,0 -54828663,Andrey Mitin,RUS,male,1970-04-05,1.74,80.0,equestrian,0,0,0 -142867058,Andrey Petrov,UZB,male,1986-10-13,1.69,62.0,athletics,0,0,0 -434890755,Andrey Yerguchyov,KAZ,male,1995-04-23,1.88,100.0,canoe,0,0,0 -102165738,Andrey Yudin,RUS,male,1996-06-06,1.75,70.0,gymnastics,0,0,0 -295536761,Andrey Zeits,KAZ,male,1986-12-14,,,cycling,0,0,0 -317735498,Andri Eleftheriou,CYP,female,1984-06-19,1.62,50.0,shooting,0,0,0 -619323217,Andrii Govorov,UKR,male,1992-04-10,1.9,89.0,aquatics,0,0,0 -730965977,Andrii Khripta,UKR,male,1986-11-29,,,cycling,0,0,0 -709643676,Andrii Sienichkin,UKR,male,1991-05-01,1.75,62.0,gymnastics,0,0,0 -789218383,Andrija Prlainovic,SRB,male,1987-04-28,1.87,93.0,aquatics,1,0,0 -397768337,Andrija Sljukic,SRB,male,1995-09-08,1.96,92.0,rowing,0,0,0 -506743542,Andrique Allisop,SEY,male,1993-06-02,1.75,,boxing,0,0,0 -180553214,Andrius Gudzius,LTU,male,1991-02-14,2.0,130.0,athletics,0,0,0 -109066206,Andrius Sidlauskas,LTU,male,1997-04-06,1.87,77.0,aquatics,0,0,0 -910064394,Andriy Fedechko,UKR,male,1990-12-04,1.78,72.0,modern pentathlon,0,0,0 -856444352,Andriy Grivko,UKR,male,1983-08-07,1.8,69.0,cycling,0,0,0 -777498041,Andriy Kvyatkovskyy,UKR,male,1990-02-02,1.75,65.0,wrestling,0,0,0 -129967030,Andriy Protsenko,UKR,male,1988-05-20,1.94,80.0,athletics,0,0,0 -572732712,Andriy Yagodka,UKR,male,1988-07-06,1.95,85.0,fencing,0,0,0 -277394115,Andro Buslje,CRO,male,1986-01-04,2.0,115.0,aquatics,0,1,0 -526943972,Andy Murray,GBR,male,1987-05-15,1.88,85.0,tennis,1,0,0 -551671372,Andy Ogide,NGR,male,1987-10-01,2.04,107.0,basketball,0,0,0 -918724681,Andy Pereira,CUB,male,1989-08-31,1.66,73.0,table tennis,0,0,0 -573932079,Ane Marcelle dos Santos,BRA,female,1994-01-12,1.53,53.0,archery,0,0,0 -259399361,Ane Santesteban Gonzalez,ESP,female,1990-12-12,1.6,48.0,cycling,0,0,0 -855774717,Anel Oosthuizen,RSA,female,1995-04-22,1.67,55.0,athletics,0,0,0 -282925578,Anette Viborg,DEN,female,1990-09-17,1.72,68.0,sailing,0,0,0 -868296400,Anezka Drahotova,CZE,female,1995-07-22,1.83,58.0,athletics,0,0,0 -26642064,Anfisa Pochkalova,UKR,female,1990-03-01,1.78,70.0,fencing,0,0,0 -404311165,Angel Correa,ARG,male,1995-03-09,1.74,80.0,football,0,0,0 -618761201,Angel Fournier Rodriguez,CUB,male,1987-12-31,1.98,108.0,rowing,0,0,0 -435819850,Angel Kodinov,BUL,male,1997-09-04,1.88,88.0,canoe,0,0,0 -303559145,Angel Lopez,ESP,male,1992-01-16,1.72,83.0,rugby sevens,0,0,0 -663091466,Angel McCoughtry,USA,female,1986-09-10,1.85,70.0,basketball,1,0,0 -283823153,Angel Pulgar,VEN,male,1989-02-07,1.74,79.0,cycling,0,0,0 -820102676,Angela Castro,BOL,female,1993-02-21,1.6,54.0,athletics,0,0,0 -916809801,Angela Clavijo,COL,female,1993-09-01,1.63,62.0,football,0,0,0 -287231198,Angela Hannah,GBR,female,1986-03-24,1.74,72.0,canoe,0,0,0 -948174500,Angela Malestein,NED,female,1993-01-31,1.73,66.0,handball,0,0,0 -859256652,Angela Petty,NZL,female,1991-08-16,1.68,58.0,athletics,0,0,0 -772558236,Angela Tenorio,ECU,female,1996-01-27,1.63,56.0,athletics,0,0,0 -789985846,Angela Whyte,CAN,female,1980-05-22,1.7,62.0,athletics,0,0,0 -14266301,Angela del Pan,ESP,female,1985-04-19,1.73,68.0,rugby sevens,0,0,0 -424933321,Angelica Bengtsson,SWE,female,1993-07-08,1.63,52.0,athletics,0,0,0 -518674471,Angelica Delgado,USA,female,1990-12-14,1.61,52.0,judo,0,0,0 -327887923,Angelica Moser,SUI,female,1997-10-09,1.69,65.0,athletics,0,0,0 -610826770,Angelica Roos,SWE,female,1989-04-15,1.58,58.0,weightlifting,0,0,0 -812402263,Angelica Wallen,SWE,female,1986-04-11,1.72,70.0,handball,0,0,0 -499427026,Angelika Cichocka,POL,female,1988-03-15,1.7,56.0,athletics,0,0,0 -647265868,Angelika Sita Ouedraogo,BUR,female,1993-12-04,1.72,63.0,aquatics,0,0,0 -24258423,Angelina Kysla,UKR,female,1991-02-15,1.58,49.0,gymnastics,0,0,0 -806788576,Angelina Melnikova,RUS,female,2000-07-18,1.51,44.0,gymnastics,0,1,0 -175332035,Angelique Kerber,GER,female,1988-01-18,1.73,68.0,tennis,0,1,0 -493430225,Angelos Vlachopoulos,GRE,male,1991-09-28,1.79,75.0,aquatics,0,0,0 -457358368,Angie Orjuela,COL,female,1989-05-09,1.58,46.0,athletics,0,0,0 -697735361,Angie Sabrina Gonzalez,VEN,female,1981-01-03,1.6,57.0,cycling,0,0,0 -618528044,Angus Groom,GBR,male,1992-06-16,1.95,94.0,rowing,0,0,0 -482234536,Anicka Newell,CAN,female,1993-08-05,1.75,64.0,athletics,0,0,0 -143427231,Anicka van Emden,NED,female,1986-12-10,1.69,63.0,judo,0,0,1 -441101021,Anika Lorenz,GER,female,1990-12-09,1.72,70.0,sailing,0,0,0 -54089758,Anilda Thomas,IND,female,1993-05-06,,,athletics,0,0,0 -393115111,Anirban Lahiri,IND,male,1987-06-29,1.72,80.0,golf,0,0,0 -52111806,Anish Khem,FIJ,male,1993-08-27,1.75,67.0,football,0,0,0 -795806548,Anisha Vekemans,BEL,female,1991-08-17,1.6,52.0,cycling,0,0,0 -998460252,Anissa Khelfaoui,ALG,female,1991-08-29,1.68,62.0,fencing,0,0,0 -145066330,Anita Alvarez,USA,female,1996-12-02,1.71,51.0,aquatics,0,0,0 -790724196,Anita Hinriksdottir,ISL,female,1996-01-13,1.72,49.0,athletics,0,0,0 -709319591,Anita Marton,HUN,female,1989-01-15,1.72,95.0,athletics,0,0,1 -79802394,Anita McLAREN,NZL,female,1987-10-02,1.63,56.0,hockey,0,0,0 -175586177,Anita Wlodarczyk,POL,female,1985-08-08,1.78,95.0,athletics,1,0,0 -865947696,Aniuar Geduev,RUS,male,1987-01-26,1.73,74.0,wrestling,0,1,0 -358118028,Aniya Necol Louissaint,HAI,female,1998-09-09,1.73,67.0,taekwondo,0,0,0 -639948235,Anja Crevar,SRB,female,2000-05-24,1.64,49.0,aquatics,0,0,0 -623213060,Anja Klinar,SLO,female,1988-04-03,1.7,62.0,aquatics,0,0,0 -699323942,Anja Mittag,GER,female,1985-05-16,1.68,58.0,football,1,0,0 -616847626,Anja Scherl,GER,female,1986-04-12,1.6,48.0,athletics,0,0,0 -109335235,Anjelina Nadai Lohalith,ROT,female,1993-01-01,1.63,50.0,athletics,0,0,0 -571260050,Anju Takamizawa,JPN,female,1996-03-06,1.65,50.0,athletics,0,0,0 -642447313,Ankhtsetseg Munkhjantsan,MGL,female,1997-12-25,1.63,69.0,weightlifting,0,0,0 -307535936,Ankit Sharma,IND,male,1992-07-20,1.77,75.0,athletics,0,0,0 -266461639,Ann-Sophie Duyck,BEL,female,1987-07-23,1.72,60.0,cycling,0,0,0 -327178595,Anna Boada Peiro,ESP,female,1992-12-30,1.65,60.0,rowing,0,0,0 -85532849,Anna Cruz,ESP,female,1986-10-27,1.76,60.0,basketball,0,1,0 -921750536,Anna Danesi,ITA,female,1996-04-20,1.95,75.0,volleyball,0,0,0 -110782111,Anna Dowgiert,POL,female,1990-07-15,1.7,58.0,aquatics,0,0,0 -636501122,Anna Emilie Moller,DEN,female,1997-07-28,1.66,52.0,athletics,0,0,0 -800527374,Anna Espar Llaquet,ESP,female,1993-01-08,1.8,66.0,aquatics,0,0,0 -592806648,Anna Flanagan,AUS,female,1992-01-08,1.8,66.0,hockey,0,0,0 -315491322,Anna Green,NZL,female,1990-08-20,1.67,57.0,football,0,0,0 -739353909,Anna Greta Olasz,HUN,female,1993-09-19,1.68,55.0,aquatics,0,0,0 -721775367,Anna Grineva,RUS,female,1988-01-31,1.85,87.0,aquatics,0,0,1 -471756502,Anna Hahner,GER,female,1989-11-20,1.65,48.0,athletics,0,0,0 -128450991,Anna Illes,HUN,female,1994-02-21,1.8,73.0,aquatics,0,0,0 -165448816,Anna Incerti,ITA,female,1980-01-19,1.68,44.0,athletics,0,0,0 -672606405,Anna Jagaciak,POL,female,1990-02-10,1.77,59.0,athletics,0,0,0 -570157500,Anna Jenny Fransson,SWE,female,1987-07-18,1.69,73.0,wrestling,0,0,1 -218793925,Anna Karnaukh,RUS,female,1993-08-31,1.73,61.0,aquatics,0,0,1 -144902061,Anna Karolina Schmiedlova,SVK,female,1994-09-13,1.76,63.0,tennis,0,0,0 -959541631,Anna Kasprzak,DEN,female,1989-12-08,1.73,66.0,equestrian,0,0,0 -112286651,Anna Kielbasinska,POL,female,1990-06-26,1.7,59.0,athletics,0,0,0 -573321803,Anna Knauer,GER,female,1995-02-20,1.7,60.0,cycling,0,0,0 -711366566,Anna Korakaki,GRE,female,1996-04-08,1.75,65.0,shooting,1,0,1 -839118437,Anna Kornuta,UKR,female,1988-11-10,1.68,57.0,athletics,0,0,0 -550413879,Anna Laurell Nash,SWE,female,1980-02-12,1.84,,boxing,0,0,0 -759876501,Anna Lunyova,UKR,female,1991-10-01,1.76,60.0,athletics,0,0,0 -688177663,Anna Maliszewska,POL,female,1993-07-04,1.7,52.0,modern pentathlon,0,0,0 -818693717,Anna Malova,RUS,female,1990-04-16,1.75,59.0,volleyball,0,0,0 -953320535,Anna Malvina Svennung,SWE,female,1984-10-24,1.78,74.0,rowing,0,0,0 -621267862,Anna Maria Mazzetti,ITA,female,1988-08-25,1.61,50.0,triathlon,0,0,0 -334806416,Anna Maria Sepp,EST,female,1996-02-02,1.72,64.0,sailing,0,0,0 -988042479,Anna Marton,HUN,female,1995-03-31,1.8,70.0,fencing,0,0,0 -211067010,Anna Meares,AUS,female,1983-09-21,1.65,70.0,cycling,0,0,1 -47625127,Anna Nordqvist,SWE,female,1987-06-10,1.83,75.0,golf,0,0,0 -613249321,Anna Ntountounaki,GRE,female,1995-09-09,1.77,66.0,aquatics,0,0,0 -68194105,Anna Plichta,POL,female,1992-02-10,1.76,60.0,cycling,0,0,0 -602398132,Anna Santamans,FRA,female,1993-04-25,1.77,61.0,aquatics,0,0,0 -799918043,Anna Sedoykina,RUS,female,1984-08-01,1.84,74.0,handball,1,0,0 -480514013,Anna Sen,RUS,female,1990-12-03,1.86,81.0,handball,1,0,0 -320661487,Anna Sztankovics,HUN,female,1996-01-10,1.77,63.0,aquatics,0,0,0 -475900147,Anna Timofeeva,RUS,female,1987-07-18,1.78,86.0,aquatics,0,0,1 -553165489,Anna Titimets,UKR,female,1989-03-05,1.73,63.0,athletics,0,0,0 -760246597,Anna Ustyukhina,RUS,female,1989-03-18,1.77,70.0,aquatics,0,0,1 -628102440,Anna Voloshyna,UKR,female,1991-09-26,1.68,53.0,aquatics,0,0,0 -363091224,Anna Vyakhireva,RUS,female,1995-03-13,1.68,63.0,handball,1,0,0 -984260519,Anna Wierzbowska,POL,female,1990-12-08,1.84,80.0,rowing,0,0,0 -232686773,Anna van der Breggen,NED,female,1990-04-18,1.67,56.0,cycling,1,0,1 -173475309,Anna-Lena Groenefeld,GER,female,1985-06-04,1.8,73.0,tennis,0,0,0 -878372430,Anna-Maria Alexandri,AUT,female,1997-09-15,1.7,48.0,aquatics,0,0,0 -25318407,Annabel Laure Ali,CMR,female,1985-03-04,1.76,75.0,wrestling,0,0,0 -849907578,Annalie Longo,NZL,female,1991-07-01,1.56,54.0,football,0,0,0 -36119786,Annalise Murphy,IRL,female,1990-02-01,1.84,72.0,sailing,0,1,0 -892128901,Anne Andersen,DEN,female,1992-11-10,1.83,86.0,rowing,0,0,1 -618276677,Anne Buijs,NED,female,1991-12-02,1.9,73.0,volleyball,0,0,0 -8185560,Anne Cairns,SAM,female,1981-01-11,,,canoe,0,0,0 -979365404,Anne Haug,GER,female,1983-01-20,1.64,51.0,triathlon,0,0,0 -862170739,Anne Holm Baumeister,DEN,female,1987-12-29,1.68,54.0,athletics,0,0,0 -470667048,Anne Lolk Thomsen,DEN,female,1983-05-15,1.75,60.0,rowing,0,0,0 -554467437,Anne Schroder,GER,female,1994-09-11,1.7,58.0,hockey,0,0,1 -676612958,Anne Terpstra,NED,female,1991-01-05,1.66,57.0,cycling,0,0,0 -663634916,Anne Zagre,BEL,female,1990-03-13,1.76,68.0,athletics,0,0,0 -723096716,Anne-Mari Hyrylainen,FIN,female,1978-08-15,1.68,50.0,athletics,0,0,0 -800780979,Anne-Marie Rindom,DEN,female,1991-06-14,1.7,68.0,sailing,0,0,1 -869195887,Anneisha McLaughlin-Whilby,JAM,female,1986-01-06,1.7,66.0,athletics,0,1,0 -455588224,Annekatrin Thiele,GER,female,1984-10-18,1.73,68.0,rowing,1,0,0 -428641250,Anneliese Rubie,AUS,female,1992-04-22,1.71,58.0,athletics,0,0,0 -392790304,Anneloes van Veen,NED,female,1990-08-07,1.77,67.0,sailing,0,0,0 -389902510,Annemiek Bekkering,NED,female,1991-08-05,1.6,54.0,sailing,0,0,0 -706092659,Annemiek van Vleuten,NED,female,1982-10-08,1.68,59.0,cycling,0,0,0 -279678648,Annette Duetz,NED,female,1993-06-29,1.8,74.0,sailing,0,0,0 -297653287,Annette Edmondson,AUS,female,1991-12-12,1.71,65.0,cycling,0,0,0 -790807668,Anni Teija Orvokki Vuohijoki,FIN,female,1988-05-24,1.62,62.0,weightlifting,0,0,0 -925256825,Annie Haeger,USA,female,1990-02-05,1.68,58.0,sailing,0,0,0 -960982811,Annika Beck,GER,female,1994-02-16,1.67,59.0,tennis,0,0,0 -986477773,Annika Bochmann,GER,female,1991-07-16,1.67,58.0,sailing,0,0,0 -724113769,Annika Bruhn,GER,female,1992-10-05,1.83,69.0,aquatics,0,0,0 -614874748,Annika Langvad,DEN,female,1984-03-22,1.74,63.0,cycling,0,0,0 -196922673,Annika Roloff,GER,female,1991-03-10,1.66,58.0,athletics,0,0,0 -595641112,Annika Schleu,GER,female,1990-04-03,1.75,63.0,modern pentathlon,0,0,0 -799645889,Annika Sprink,GER,female,1995-10-20,1.73,61.0,hockey,0,0,1 -156381580,Annike Krahn,GER,female,1985-07-01,1.73,61.0,football,1,0,0 -521298946,Annsert Whyte,JAM,male,1987-10-04,1.88,86.0,athletics,0,0,0 -403056126,Ano Kuwai,JPN,female,1989-10-20,1.72,70.0,rugby sevens,0,0,0 -618542850,Anouk Verge-Depre,SUI,female,1992-02-11,1.85,70.0,volleyball,0,0,0 -57200072,Anouk Vetter,NED,female,1993-02-04,1.77,62.0,athletics,0,0,0 -976455975,Anqi Xu,CHN,female,1992-01-23,1.82,76.0,fencing,0,1,0 -576815287,Antanas Kavaliauskas,LTU,male,1984-09-19,2.08,114.0,basketball,0,0,0 -224127843,Anthonique Strachan,BAH,female,1993-08-22,1.68,57.0,athletics,0,0,0 -293695659,Anthonny Sitraka Ralefy,MAD,male,1995-07-10,1.82,79.0,aquatics,0,0,0 -547860838,Anthony Barbar,LIB,male,1992-11-18,,,aquatics,0,0,0 -76732813,Anthony Dean,AUS,male,1991-04-22,1.75,87.0,cycling,0,0,0 -261223419,Anthony Ervin,USA,male,1981-05-26,1.91,80.0,aquatics,2,0,0 -179926166,Anthony Fahden,USA,male,1986-02-27,1.81,71.0,rowing,0,0,0 -335737888,Anthony Mylann Obame,GAB,male,1988-09-10,1.9,97.0,taekwondo,0,0,0 -63461595,Anthony Perez,VEN,male,1993-09-29,2.05,93.0,basketball,0,0,0 -818993615,Anthony Romaniw,CAN,male,1991-09-15,1.8,80.0,athletics,0,0,0 -368458137,Anthony Terras,FRA,male,1985-06-21,1.7,69.0,shooting,0,0,0 -575174171,Anthony Zambrano,COL,male,1998-01-17,1.84,72.0,athletics,0,0,0 -947380759,Antigoni Drisbioti,GRE,female,1984-03-21,1.61,50.0,athletics,0,0,0 -851486993,Antje von Seydlitz-Kurzbach,CAN,female,1990-09-16,1.79,76.0,rowing,0,0,0 -627845913,Antoaneta Boneva,BUL,female,1986-01-17,1.68,70.0,shooting,0,0,0 -969463454,Antoine Adams,SKN,male,1988-08-31,1.8,79.0,athletics,0,0,0 -110778822,Antoine Bouchard,CAN,male,1994-08-24,1.8,66.0,judo,0,0,0 -352830774,Antoine Diot,FRA,male,1989-01-17,1.93,86.0,basketball,0,0,0 -778856814,Antoine Duchesne,CAN,male,1991-09-12,1.89,73.0,cycling,0,0,0 -638759707,Antoine Gakeme,BDI,male,1991-12-24,1.69,60.0,athletics,0,0,0 -265482075,Antoine Gillet,BEL,male,1988-03-22,1.85,78.0,athletics,0,0,0 -418069781,Antoine Valois-Fortier,CAN,male,1990-03-13,1.9,81.0,judo,0,0,0 -124031196,Antoinette Gasongo,BDI,female,1994-04-24,1.7,51.0,judo,0,0,0 -67909068,Antoinette Nana Djimou Ida,FRA,female,1985-08-02,1.74,69.0,athletics,0,0,0 -939122275,Anton Astakhov,RUS,male,1987-04-30,1.76,85.0,shooting,0,0,0 -866446784,Anton Avdeev,RUS,male,1986-09-08,1.74,85.0,fencing,0,0,0 -300749546,Anton Braun,GER,male,1990-04-28,2.02,104.0,rowing,0,0,0 -989516794,Anton Chupkov,RUS,male,1997-02-22,1.88,71.0,aquatics,0,0,1 -55365103,Anton Dahlberg,SWE,male,1985-05-10,1.82,71.0,sailing,0,0,0 -727981250,Anton Fokin,UZB,male,1982-11-13,1.68,57.0,gymnastics,0,0,0 -393253446,Anton Ipsen,DEN,male,1994-09-04,1.86,74.0,aquatics,0,0,0 -620488948,Anton Kosmac,SLO,male,1976-12-14,1.83,66.0,athletics,0,0,0 -469779027,Anton Kucmin,SVK,male,1984-06-07,1.8,64.0,athletics,0,0,0 -108256011,Anton McKee,ISL,male,1993-12-18,1.83,84.0,aquatics,0,0,0 -835942400,Anton Prilepov,BLR,male,1984-02-05,1.86,96.0,archery,0,0,0 -463095413,Anton Rizov,BUL,male,1987-12-29,1.7,75.0,shooting,0,0,0 -299113520,Anton Sintsov,RUS,male,1985-02-03,1.7,62.0,cycling,0,0,0 -75967084,Anton Sudesh Peiris Kurukulasooriyage,SRI,male,1985-02-03,1.65,62.0,weightlifting,0,0,0 -877558321,Anton Zarutskiy,RUS,male,1986-04-27,1.95,94.0,rowing,0,0,0 -723171334,Antonela Mena,ARG,female,1988-02-28,1.8,84.0,handball,0,0,0 -972925193,Antonella Palmisano,ITA,female,1991-08-06,1.65,45.0,athletics,0,0,0 -96294708,Antonella del Core,ITA,female,1980-11-05,1.8,75.0,volleyball,0,0,0 -281632549,Antoni Kindler,CAN,male,1988-05-16,1.88,85.0,hockey,0,0,0 -881359443,Antonia Moreira,ANG,female,1982-04-26,1.72,70.0,judo,0,0,0 -773591831,Antonin Rouzier,FRA,male,1986-08-18,2.0,102.0,volleyball,0,0,0 -945091038,Antonino Barilla,ITA,male,1987-11-28,1.87,96.0,shooting,0,0,0 -952687673,Antonio Abadia,ESP,male,1990-07-02,1.7,65.0,athletics,0,0,0 -946006998,Antonio Alkana,RSA,male,1990-04-12,1.83,76.0,athletics,0,0,0 -438649023,Antonio Arroyo Perez,ESP,male,1994-05-09,1.7,68.0,aquatics,0,0,0 -198987455,Antonio Fernandez,ESP,male,1991-06-12,1.84,76.0,archery,0,0,0 -778264975,Antonio J. Leal,VEN,male,1987-06-25,1.65,60.0,fencing,0,0,0 -53317288,Antonio PETROVIC,MNE,male,1982-09-24,1.93,98.0,aquatics,0,0,0 -151724741,Antonio Petkovic,CRO,male,1986-01-11,1.9,90.0,aquatics,0,1,0 -824387423,Antonio Vargas,USA,male,1996-08-15,1.68,,boxing,0,0,0 -268280424,Antonis Martasidis,CYP,male,1992-06-14,1.78,85.0,weightlifting,0,0,0 -565509368,Antony Fowler,GBR,male,1991-03-10,1.79,,boxing,0,0,0 -801230201,Antony Lozano,HON,male,1993-04-25,1.82,81.0,football,0,0,0 -500105232,Antri Christoforou,CYP,female,1992-04-02,1.66,53.0,cycling,0,0,0 -470808417,Antti Ruuskanen,FIN,male,1984-02-21,1.89,86.0,athletics,0,0,0 -329333328,Antwon Hicks,NGR,male,1983-03-12,1.88,80.0,athletics,0,0,0 -196778355,Anuradha Indrajith Cooray,SRI,male,1978-03-24,1.74,58.0,athletics,0,0,0 -807714880,Anuradha Thokchom,IND,female,1989-02-02,1.62,54.0,hockey,0,0,0 -479087750,Anvar Yunusov,TJK,male,1987-02-01,1.69,,boxing,0,0,0 -500334706,Anyika Onuora,GBR,female,1984-10-28,1.78,76.0,athletics,0,0,1 -503893240,Anze Tavcar,SLO,male,1994-12-02,1.87,79.0,aquatics,0,0,0 -585429109,Anzor Boltukaev,RUS,male,1986-04-05,1.8,97.0,wrestling,0,0,0 -903085335,Apisai Domolailai,FIJ,male,1989-04-16,1.92,98.0,rugby sevens,1,0,0 -399850299,Apolonia Vaivai,FIJ,female,1991-02-05,1.37,69.0,weightlifting,0,0,0 -746896443,Apostolos Christou,GRE,male,1996-11-01,1.98,88.0,aquatics,0,0,0 -23572147,Apostolos Parellis,CYP,male,1985-07-24,1.86,110.0,athletics,0,0,0 -9514544,April Ross,USA,female,1982-06-20,1.86,74.0,volleyball,0,0,1 -728939478,Apurvi Chandela,IND,female,1993-01-04,1.57,54.0,shooting,0,0,0 -706967900,Arajik Marutjan,GER,male,1992-08-15,1.8,,boxing,0,0,0 -450196345,Arakel Mirzoyan,ARM,male,1989-10-21,1.7,84.0,weightlifting,0,0,0 -481716571,Aram Avagyan,ARM,male,1991-01-18,1.73,,boxing,0,0,0 -69028117,Aran Zalewski,AUS,male,1991-03-21,1.86,80.0,hockey,0,0,0 -391027633,Arantxa Parra-Santonja,ESP,female,1982-11-09,1.76,61.0,tennis,0,0,0 -982181588,Arantza Gumucio,CHI,female,1989-10-04,1.65,61.0,sailing,0,0,0 -319236400,Aras Kaya,TUR,male,1994-04-04,1.81,60.0,athletics,0,0,0 -756028808,Arashi Morisaka,JPN,male,1996-07-02,1.76,,boxing,0,0,0 -174361869,Arcangeline Fouodji Sonkbou,CMR,female,1987-08-26,1.58,68.0,weightlifting,0,0,0 -765868860,Ardo Arusaar,EST,male,1988-06-24,1.81,102.0,wrestling,0,0,0 -554149372,Are Hansen,NOR,male,1982-01-16,,,shooting,0,0,0 -269857952,Are Strandli,NOR,male,1988-08-18,,,rowing,0,0,1 -488754594,Areneo David,MAW,male,1995-06-06,1.64,58.0,archery,0,0,0 -67773543,Ari Mannio,FIN,male,1987-07-23,1.85,100.0,athletics,0,0,0 -381423797,Ari-Pekka Liukkonen,FIN,male,1989-02-09,2.08,92.0,aquatics,0,0,0 -952365137,Aria Fischer,USA,female,1999-03-02,1.83,78.0,aquatics,1,0,0 -76956627,Arialis J. Gandulla,CUB,female,1995-06-22,1.7,75.0,athletics,0,0,0 -68245783,Ariana Kira Hilborna,LAT,female,1980-09-19,1.67,52.0,athletics,0,0,0 -69546573,Ariana Orrego,PER,female,1998-09-25,1.57,49.0,gymnastics,0,0,0 -124325348,Ariana Washington,USA,female,1996-08-27,1.76,61.0,athletics,0,0,0 -978867868,Ariane Fortin,CAN,female,1984-11-20,1.75,,boxing,0,0,0 -177132328,Arianna Castiglioni,ITA,female,1997-08-15,1.67,55.0,aquatics,0,0,0 -571388778,Arianna Errigo,ITA,female,1988-06-06,1.8,68.0,fencing,0,0,0 -712016882,Arianna Garibotti,ITA,female,1989-12-09,1.69,64.0,aquatics,0,1,0 -807270955,Arianna Perilli,SMR,female,1978-05-01,1.61,65.0,shooting,0,0,0 -705250725,Arianna Schivo,ITA,female,1986-09-16,1.58,47.0,equestrian,0,0,0 -656130098,Arianna Vanderpool-Wallace,BAH,female,1990-03-04,1.68,61.0,aquatics,0,0,0 -197129425,Arina Openysheva,RUS,female,1999-03-24,1.68,59.0,aquatics,0,0,0 -520698565,Arina Rodionova,AUS,female,1989-12-15,,,tennis,0,0,0 -906864947,Arina Tsitsilina,BLR,female,1998-10-09,1.73,50.0,gymnastics,0,0,0 -713644170,Arisa Sato,JPN,female,1989-07-18,1.64,52.0,volleyball,0,0,0 -159588097,Ariya Jutanugarn,THA,female,1995-11-23,1.74,73.0,golf,0,0,0 -30042829,Ariya Phounsavath,LAO,male,1991-03-02,1.82,58.0,cycling,0,0,0 -388943,Arkadiusz Michalski,POL,male,1990-01-07,1.8,105.0,weightlifting,0,0,0 -720999336,Arlen Lopez,CUB,male,1993-02-21,1.78,,boxing,1,0,0 -292905261,Arlenis Sierra Canadilla,CUB,female,1992-12-07,1.62,57.0,cycling,0,0,0 -991852219,Arleta Podolak,POL,female,1993-10-20,1.6,57.0,judo,0,0,0 -207170334,Arley Rodriguez,COL,male,1993-02-13,1.76,67.0,football,0,0,0 -467565690,Arli Chontey,KAZ,male,1992-07-01,1.5,56.0,weightlifting,0,0,0 -708613279,Arman Hall,USA,male,1994-02-12,1.83,74.0,athletics,1,0,0 -653569053,Arman-Marshall Silla,BLR,male,1994-07-13,2.03,105.0,taekwondo,0,0,0 -583809753,Armandas Kelmelis,LTU,male,1998-03-22,1.93,86.0,rowing,0,0,0 -166236142,Arnaud Hybois,FRA,male,1982-01-26,1.8,74.0,canoe,0,0,0 -974000570,Arnis Rumbenieks,LAT,male,1988-04-04,1.75,63.0,athletics,0,0,0 -1436864,Arokia Rajiv,IND,male,1991-05-22,1.77,64.0,athletics,0,0,0 -459345576,Aron Baynes,AUS,male,1986-12-09,2.07,115.0,basketball,0,0,0 -858784469,Aron Gadorfalvi,HUN,male,1977-12-05,1.84,73.0,sailing,0,0,0 -725424114,Aron Kifle,ERI,male,1998-02-20,,,athletics,0,0,0 -754963695,Aron Szilagyi,HUN,male,1990-01-14,1.8,78.0,fencing,1,0,0 -279584048,Arsen Eraliev,KGZ,male,1990-05-15,1.65,59.0,wrestling,0,0,0 -505546970,Arsen Julfalakyan,ARM,male,1987-05-08,1.66,76.0,wrestling,0,0,0 -223079860,Arseth Heather,MRI,female,1991-11-30,1.71,61.0,aquatics,0,0,0 -361362188,Arslanbek Achilov,TKM,male,1993-07-01,1.77,,boxing,0,0,0 -75218682,Artem Bloshenko,UKR,male,1985-02-01,1.87,100.0,judo,0,0,0 -552816016,Artem Chebotarev,RUS,male,1988-10-26,1.84,,boxing,0,0,0 -996041766,Artem Ermakov,RUS,male,1982-03-16,1.88,80.0,volleyball,0,0,0 -172069479,Artem Harutyunyan,GER,male,1990-08-13,1.72,,boxing,0,0,1 -914185536,Artem Kosov,RUS,male,1986-08-04,1.93,96.0,rowing,0,0,0 -102564991,Artem Morozov,UKR,male,1980-02-29,1.95,100.0,rowing,0,0,0 -307289001,Artem Pochtarov,UKR,male,1993-07-24,1.83,77.0,badminton,0,0,0 -67482757,Artem Volvich,RUS,male,1990-01-22,2.08,96.0,volleyball,0,0,0 -770170821,Artemi Gavezou,ESP,female,1994-06-19,1.69,55.0,gymnastics,0,1,0 -268835792,Arthur Abele,GER,male,1986-07-30,1.84,85.0,athletics,0,0,0 -33769282,Arthur Bergo,BRA,male,1994-03-07,1.83,83.0,rugby sevens,0,0,0 -216948866,Arthur Biyarslanov,CAN,male,1995-04-22,1.74,,boxing,0,0,0 -306653489,Arthur Lanigan-O'Keeffe,IRL,male,1991-09-13,1.82,76.0,modern pentathlon,0,0,0 -938345533,Arthur Mariano,BRA,male,1993-09-18,1.69,65.0,gymnastics,0,0,1 -11470529,Arthur Zanetti,BRA,male,1990-04-15,1.56,64.0,gymnastics,0,1,0 -353946547,Arthur van Doren,BEL,male,1994-10-01,1.78,74.0,hockey,0,1,0 -749009956,Artsem Bandarenka,BLR,male,1991-06-19,1.87,78.0,athletics,0,0,0 -270627032,Artur Akhmatkhuzin,RUS,male,1988-05-21,1.87,79.0,fencing,1,0,0 -206524686,Artur Aleksanyan,ARM,male,1991-10-21,1.9,98.0,wrestling,1,0,0 -80806141,Artur Brzozowski,POL,male,1985-03-29,1.74,67.0,athletics,0,0,0 -746970689,Artur Davtyan,ARM,male,1992-08-08,1.6,55.0,gymnastics,0,0,0 -311001055,Artur Hovhannisyan,ARM,male,1996-03-23,1.6,,boxing,0,0,0 -46547339,Artur Kozlowski,POL,male,1985-01-19,1.69,54.0,athletics,0,0,0 -802555089,Artur Mastianica,LTU,male,1992-07-30,1.87,73.0,athletics,0,0,0 -936737122,Artur Mikolajczewski,POL,male,1990-06-27,1.8,73.0,rowing,0,0,0 -74899025,Arturo Chavez,PER,male,1990-01-12,1.9,76.0,athletics,0,0,0 -729366221,Arturo Gonzalez,MEX,male,1994-09-05,1.73,70.0,football,0,0,0 -512554925,Arturo Ramirez,VEN,male,1991-09-14,1.7,65.0,athletics,0,0,0 -318342628,Arturo Rivarola Trappe,PAR,male,1989-11-02,1.83,80.0,rowing,0,0,0 -32858315,Arturs Nikiforenko,LAT,male,1992-05-03,1.85,110.0,judo,0,0,0 -512734462,Arturs Plesnieks,LAT,male,1992-01-21,1.8,104.0,weightlifting,0,0,0 -334665837,Artuur Peters,BEL,male,1996-10-26,1.93,95.0,canoe,0,0,0 -638975983,Artyom Zakharov,KAZ,male,1991-10-27,1.77,70.0,cycling,0,0,0 -231595890,Arun Panchia,NZL,male,1989-04-22,1.8,73.0,hockey,0,0,0 -531231218,Arvin Moazami Godarzi,IRI,male,1990-03-26,1.85,73.0,cycling,0,0,0 -574180353,Arya Nasimi Shad,IRI,male,1999-11-07,1.78,68.0,aquatics,0,0,0 -717411449,Asadulla Lachinau,BLR,male,1986-04-17,1.58,60.0,wrestling,0,0,0 -508304618,Asafa Powell,JAM,male,1982-11-23,1.91,93.0,athletics,1,0,0 -108415368,Asako O,JPN,female,1987-12-16,1.89,85.0,basketball,0,0,0 -454303485,Asami Yoshida,JPN,female,1987-10-09,1.65,62.0,basketball,0,0,0 -900715474,Asaramanitra Ratiarison,MAD,female,1988-01-07,1.55,48.0,judo,0,0,0 -335612059,Asbel Kiprop,KEN,male,1989-06-30,1.9,63.0,athletics,0,0,0 -481165955,Asdis Hjalmsdottir,ISL,female,1985-10-28,1.78,70.0,athletics,0,0,0 -718103063,Asena Rokomarama,FIJ,female,1996-05-02,1.6,58.0,rugby sevens,0,0,0 -436712955,Asenathi Jim,RSA,male,1992-01-26,1.76,65.0,sailing,0,0,0 -423360573,Asger Stromgaard Sorensen,DEN,male,1996-06-05,1.92,89.0,football,0,0,0 -687479557,Ash Southern,AUS,female,1992-10-22,1.88,82.0,aquatics,0,0,0 -648671470,Asha Philip,GBR,female,1990-10-25,1.64,67.0,athletics,0,0,1 -464180251,Ashlee Ankudinoff,AUS,female,1990-08-20,1.74,67.0,cycling,0,0,0 -397806865,Ashleigh Gentle,AUS,female,1991-02-25,1.71,52.0,triathlon,0,0,0 -143278150,Ashleigh Johnson,USA,female,1994-09-12,1.86,81.0,aquatics,1,0,0 -193259308,Ashleigh Moolman-Pasio,RSA,female,1985-12-09,1.63,52.0,cycling,0,0,0 -70434826,Ashleigh Nelson,GBR,female,1991-02-20,1.75,69.0,athletics,0,0,0 -667381951,Ashleigh Simon,RSA,female,1989-05-11,1.58,,golf,0,0,0 -341434045,Ashley Jackson,GBR,male,1987-08-27,1.7,73.0,hockey,0,0,0 -742039431,Ashley Kelly,IVB,female,1991-03-25,1.73,75.0,athletics,0,0,0 -790221508,Ashley Lawrence,CAN,female,1995-06-11,1.64,60.0,football,0,0,1 -896644128,Ashley McKenzie,GBR,male,1989-07-17,1.68,60.0,judo,0,0,0 -810246164,Ashley Nee,USA,female,1989-06-15,1.63,54.0,canoe,0,0,0 -889563611,Ashley Spencer,USA,female,1993-06-08,1.78,58.0,athletics,0,0,1 -405218283,Ashley Steacy,CAN,female,1987-06-28,1.58,64.0,rugby sevens,0,0,1 -151634524,Ashley Stoddart,AUS,female,1993-06-10,1.72,70.0,sailing,0,0,0 -241360203,Ashlyn Harris,USA,female,1985-10-19,1.73,64.0,football,0,0,0 -40755680,Ashraf Abouelhassan,EGY,male,1975-05-17,1.86,86.0,volleyball,0,0,0 -617579036,Ashraf Amgad Elseify,QAT,male,1995-02-20,1.85,105.0,athletics,0,0,0 -902296774,Ashton Baumann,CAN,male,1993-01-05,1.91,74.0,aquatics,0,0,0 -629254109,Ashton Eaton,USA,male,1988-01-21,1.86,81.0,athletics,1,0,0 -388101012,Ashun Wu,CHN,male,1985-06-22,1.81,81.0,golf,0,0,0 -415694006,Ashwini Chidananda Akkunji,IND,female,1987-10-07,1.75,58.0,athletics,0,0,0 -126172872,Ashwini Ponnappa,IND,female,1989-09-18,1.65,59.0,badminton,0,0,0 -95516264,Aska Cambridge,JPN,male,1993-05-31,1.79,74.0,athletics,0,1,0 -41712943,Askale Tiksa,ETH,female,1994-07-21,1.65,52.0,athletics,0,0,0 -568130066,Aslan Kakhidze,KAZ,male,1988-10-28,1.85,39.0,wrestling,0,0,0 -645540187,Asley Gonzalez,CUB,male,1989-09-05,1.79,90.0,judo,0,0,0 -95150337,Asmir Kolasinac,SRB,male,1984-10-15,1.87,140.0,athletics,0,0,0 -532768073,Asnage Castelly,HAI,male,1978-03-29,1.82,74.0,wrestling,0,0,0 -560710110,Assiya Ipek,TUR,female,1993-12-05,1.68,70.0,weightlifting,0,0,0 -750446286,Assmaa Niang,MAR,female,1983-01-04,1.68,70.0,judo,0,0,0 -713040991,Astier Nicolas,FRA,male,1989-01-19,1.8,70.0,equestrian,1,1,0 -124475429,Astou Ndour,ESP,female,1994-08-22,1.95,68.0,basketball,0,1,0 -131094271,Astou Traore,SEN,female,1981-04-30,1.85,75.0,basketball,0,0,0 -230678922,Astrid Guyart,FRA,female,1983-03-17,1.74,59.0,fencing,0,0,0 -899652187,Astrit Ajdarevic,SWE,male,1990-04-17,1.88,80.0,football,0,0,0 -137582033,Asuka Teramoto,JPN,female,1995-11-19,1.45,37.0,gymnastics,0,0,0 -6712714,Atallah Alanazi,KSA,male,1988-02-22,1.74,74.0,shooting,0,0,0 -60539950,Atanu Das,IND,male,1992-04-05,1.75,79.0,archery,0,0,0 -59630450,Atef Saad,TUN,male,1988-03-20,1.76,57.0,athletics,0,0,0 -646350766,Ates Cinar,TUR,male,1986-05-16,1.72,72.0,sailing,0,0,0 -915895832,Atheyna Bylon,PAN,female,1989-04-06,1.75,,boxing,0,0,0 -665581158,Athos Schwantes,BRA,male,1985-02-13,1.9,85.0,fencing,0,0,0 -877684883,Atsushi Arai,JPN,male,1994-02-03,1.69,62.0,aquatics,0,0,0 -50738602,Atsuto Iida,JPN,male,1993-12-24,1.81,82.0,aquatics,0,0,0 -550829050,Attapon Uea-Aree,THA,male,1989-11-18,1.75,78.0,shooting,0,0,0 -731428841,Attila Decker,HUN,male,1987-08-25,1.97,94.0,aquatics,0,0,0 -913131099,Attila Kugler,HUN,male,1986-09-16,1.86,89.0,canoe,0,0,0 -390188051,Aubrey Modiba,RSA,male,1995-07-22,1.72,64.0,football,0,0,0 -283853104,Aubrey Smith,JAM,male,1988-06-30,1.8,75.0,athletics,0,0,0 -974052589,Aude Compan,FRA,female,1993-03-03,1.73,67.0,sailing,0,0,0 -44895631,Audrey Amiel,FRA,female,1987-03-03,1.64,65.0,rugby sevens,0,0,0 -754931470,Audrey Cordon,FRA,female,1989-09-22,1.7,60.0,cycling,0,0,0 -330818489,Audrey Lacroix,CAN,female,1983-11-17,1.63,54.0,aquatics,0,0,0 -596852328,Audrey Merle,FRA,female,1995-05-19,1.53,45.0,triathlon,0,0,0 -213434791,Audrey Tcheumeo,FRA,female,1990-04-20,1.77,78.0,judo,0,1,0 -869065556,Audrey Yong,SIN,female,1994-10-02,1.56,50.0,sailing,0,0,0 -913854740,Augustin Maillefer,SUI,male,1993-04-29,1.94,98.0,rowing,0,0,0 -904782814,Augustine Lugonzo,KEN,male,1992-07-29,1.83,86.0,rugby sevens,0,0,0 -145801140,Augustine Pulu,NZL,male,1990-01-04,1.8,89.0,rugby sevens,0,0,0 -507418985,Augusto Dutra de Oliveira,BRA,male,1990-07-16,1.8,70.0,athletics,0,0,0 -818866481,Augusto Lima,BRA,male,1991-09-17,2.06,118.0,basketball,0,0,0 -551777924,Augusto Midana,GBS,male,1984-05-20,1.67,74.0,wrestling,0,0,0 -571398392,Augusto Soares,TLS,male,1986-08-22,1.65,,athletics,0,0,0 -875934579,Augusto de Paula,BRA,male,1988-06-20,1.82,79.0,hockey,0,0,0 -189592697,Aurea Cruz,PUR,female,1982-01-10,1.8,63.0,volleyball,0,0,0 -863650191,Aurelia Bradeanu,ROU,female,1979-05-05,1.8,75.0,handball,0,0,0 -757280840,Aurelie Alcindor,MRI,female,1994-03-20,1.69,47.0,athletics,0,0,0 -364491097,Aurelie Muller,FRA,female,1990-06-07,1.69,57.0,aquatics,0,0,0 -166390906,Auriane Mallo,FRA,female,1993-10-11,1.8,63.0,fencing,0,0,0 -11624617,Aurimas Adomavicius,LTU,male,1993-09-23,2.04,100.0,rowing,0,0,0 -525803886,Aurimas Didzbalis,LTU,male,1991-06-13,1.72,92.0,weightlifting,0,0,1 -741095776,Aurimas Lankas,LTU,male,1985-09-07,1.78,89.0,canoe,0,0,1 -157040817,Auriole Dongmo,CMR,female,1990-08-03,1.73,95.0,athletics,0,0,0 -957513695,Austin Hack,USA,male,1992-05-17,2.04,99.0,rowing,0,0,0 -134543473,Automne Pavia,FRA,female,1989-01-03,1.71,57.0,judo,0,0,0 -300863169,Avtandili Tchrikishvili,GEO,male,1991-03-18,1.82,80.0,judo,0,0,0 -905229347,Avtar Singh,IND,male,1992-04-03,1.8,90.0,judo,0,0,0 -932761050,Awa Ly Ndiaye,SEN,female,2000-01-15,1.69,53.0,aquatics,0,0,0 -254855388,Axel Augis,FRA,male,1990-12-06,1.72,71.0,gymnastics,0,0,0 -663484224,Axel Harstedt,SWE,male,1987-02-28,1.97,135.0,athletics,0,0,0 -454328324,Axel Muller,ARG,male,1993-11-25,1.83,84.0,rugby sevens,0,0,0 -874568110,Axel Werner,ARG,male,1996-02-28,1.81,82.0,football,0,0,0 -182938039,Axelle Dauwens,BEL,female,1990-12-01,1.71,62.0,athletics,0,0,0 -950658256,Aya Takeuchi,JPN,female,1986-08-05,,,rugby sevens,0,0,0 -491035500,Aya Traore,SEN,female,1983-07-27,1.83,75.0,basketball,0,0,0 -427700587,Ayaka Nishimura,JPN,female,1989-05-10,1.65,58.0,hockey,0,0,0 -555668646,Ayaka Suzuki,JPN,female,1989-09-30,1.68,65.0,rugby sevens,0,0,0 -40493277,Ayaka Takahashi,JPN,female,1990-04-19,1.65,60.0,badminton,1,0,0 -335371986,Ayami Oishi,JPN,female,1991-04-09,1.69,57.0,rowing,0,0,0 -647256767,Ayane Kurihara,JPN,female,1989-09-27,1.72,59.0,badminton,0,0,0 -136502040,Ayanleh Souleiman,DJI,male,1992-12-03,1.9,70.0,athletics,0,0,0 -691452618,Ayesha Shahriyar Mohammed Albalooshi,UAE,female,1992-01-23,1.6,57.0,weightlifting,0,0,0 -899389595,Ayman Fayez,EGY,male,1991-03-03,1.81,72.0,fencing,0,0,0 -102121811,Aymen Hammed,TUN,male,1983-07-26,1.96,100.0,handball,0,0,0 -691236,Aymen Toumi,TUN,male,1990-07-11,1.83,77.0,handball,0,0,0 -232025157,Ayomide Folorunso,ITA,female,1996-10-17,1.7,55.0,athletics,0,0,0 -585835557,Ayonika Paul,IND,female,1992-09-23,1.74,74.0,shooting,0,0,0 -375178687,Ayoub Abdellaoui,ALG,male,1993-02-16,1.75,68.0,football,0,0,0 -296010311,Ayouba Traore,MLI,male,1982-08-09,1.8,,judo,0,0,0 -702811118,Ayse Cora,TUR,female,1993-03-03,1.75,69.0,basketball,0,0,0 -256843630,Ayuko Suzuki,JPN,female,1991-10-08,1.54,39.0,athletics,0,0,0 -57499245,Ayyasamy Dharun,IND,male,1996-12-31,1.77,64.0,athletics,0,0,0 -603312305,Azad Albarzi,SYR,male,1988-01-04,1.95,85.0,aquatics,0,0,0 -121737407,Azahara Munoz,ESP,female,1987-11-19,1.67,56.0,golf,0,0,0 -284565638,Azenaide Daniela Carlos,ANG,female,1990-06-14,1.8,69.0,handball,0,0,0 -682854309,Aziz Ouhadi,MAR,male,1984-07-24,1.68,72.0,athletics,0,0,0 -295847383,Azizulhasni Awang,MAS,male,1988-01-05,1.66,69.0,cycling,0,0,1 -572495754,Azmy Mehelba,EGY,male,1991-03-26,1.72,77.0,shooting,0,0,0 -959511873,Azucena Diaz,ESP,female,1982-12-19,1.63,55.0,athletics,0,0,0 -941256298,Azza Besbes,TUN,female,1990-11-28,1.75,62.0,fencing,0,0,0 -117554316,Baard Magnus Nesteng,NOR,male,1979-05-14,1.83,80.0,archery,0,0,0 -652126944,Babett Peter,GER,female,1988-05-12,1.71,64.0,football,1,0,0 -316544316,Baboloki Thebe,BOT,male,1997-03-18,,,athletics,0,0,0 -879393745,Bachana Khorava,GEO,male,1993-03-15,1.71,67.0,athletics,0,0,0 -167699785,Bachir Mahamat,CHA,male,1996-12-01,1.86,59.0,athletics,0,0,0 -345025003,Badawy Mohamed Moneim,EGY,male,1986-01-11,1.95,91.0,volleyball,0,0,0 -21166109,Baean Jouma,SYR,female,1994-04-13,1.82,76.0,aquatics,0,0,0 -574430988,Baghdad Bounedjah,ALG,male,1991-11-24,1.89,75.0,football,0,0,0 -57983858,Bahar Caglar,TUR,female,1988-09-28,1.9,76.0,basketball,0,0,0 -417882039,Bakhodir Jalolov,UZB,male,1994-07-08,1.98,,boxing,0,0,0 -803535676,Bakhtiyar Kozhatayev,KAZ,male,1992-03-28,1.77,70.0,cycling,0,0,0 -302183046,Balasz Sziranyi Somogyi,ESP,male,1983-01-10,1.96,106.0,aquatics,0,0,0 -798097157,Balazs Baji,HUN,male,1989-06-09,1.92,85.0,athletics,0,0,0 -682683760,Balazs Erdelyi,HUN,male,1990-02-16,1.96,94.0,aquatics,0,0,0 -958780251,Balazs Harai,HUN,male,1987-04-05,2.02,110.0,aquatics,0,0,0 -458728105,Balazs Kiss,HUN,male,1983-01-27,1.78,105.0,wrestling,0,0,0 -959413498,Balint Kopasz,HUN,male,1997-01-01,1.81,72.0,canoe,0,0,0 -881638132,Balla Dieye,SEN,male,1980-11-13,1.88,68.0,taekwondo,0,0,0 -684206677,Bambanani Mbane,RSA,female,1990-03-12,1.62,59.0,football,0,0,0 -368675991,Baofang Zhao,CHN,female,1993-08-12,1.7,64.0,cycling,0,0,0 -918929048,Barakat Mubarak Al-Harthi,OMA,male,1988-06-15,1.73,67.0,athletics,0,0,0 -227683804,Barbara,BRA,female,1988-07-04,1.71,75.0,football,0,0,0 -599919416,Barbara Arenhart,BRA,female,1986-10-04,1.82,73.0,handball,0,0,0 -628540930,Barbara Bujka,HUN,female,1986-09-05,1.72,82.0,aquatics,0,0,0 -798796089,Barbara Cornudella Ravetllat,ESP,female,1992-09-06,1.6,50.0,sailing,0,0,0 -756739224,Barbara Engleder,GER,female,1982-09-16,1.62,72.0,shooting,1,0,0 -941704507,Barbara Kovacs,HUN,female,1993-07-26,1.67,52.0,athletics,0,0,0 -160245017,Barbara Matic,CRO,female,1994-12-03,1.71,70.0,judo,0,0,0 -143281351,Barbara Nwaba,USA,female,1989-01-18,1.76,66.0,athletics,0,0,0 -476278288,Barbara Pla,ESP,female,1983-07-17,1.62,61.0,rugby sevens,0,0,0 -573831491,Barbara Riveros,CHI,female,1987-08-03,1.57,46.0,triathlon,0,0,0 -19020354,Barbara Seixas de Freitas,BRA,female,1987-08-03,1.77,66.0,volleyball,0,1,0 -591676186,Barbara Szabo,HUN,female,1990-02-17,1.73,58.0,athletics,0,0,0 -121734732,Barbora Balazova,SVK,female,1992-03-18,1.69,73.0,table tennis,0,0,0 -610741199,Barbora Hermannova,CZE,female,1990-11-07,1.8,79.0,volleyball,0,0,0 -958022952,Barbora Kodedova,CZE,female,1990-02-27,1.65,54.0,modern pentathlon,0,0,0 -646823652,Barbora Mokosova,SVK,female,1997-03-10,1.68,62.0,gymnastics,0,0,0 -233978154,Barbora Seemanova,CZE,female,2000-04-01,1.75,56.0,aquatics,0,0,0 -804524103,Barbora Spotakova,CZE,female,1981-06-30,1.82,80.0,athletics,0,0,1 -203332956,Barbora Strycova,CZE,female,1986-03-28,1.65,62.0,tennis,0,0,1 -759587471,Barbora Zavadova,CZE,female,1993-01-23,1.77,68.0,aquatics,0,0,0 -467627104,Barna Bor,HUN,male,1986-12-12,1.91,130.0,judo,0,0,0 -509608912,Barnabe Delarze,SUI,male,1994-06-30,1.93,100.0,rowing,0,0,0 -444827470,Barrett Laursen,DEN,male,1994-11-17,1.84,75.0,football,0,0,0 -394220896,Barry Middleton,GBR,male,1984-01-12,1.76,70.0,hockey,0,0,0 -112086322,Bart Deurloo,NED,male,1991-02-23,1.7,60.0,gymnastics,0,0,0 -999333930,Bartlomiej Wojciech Bonk,POL,male,1984-10-11,1.81,104.0,weightlifting,0,0,0 -402969291,Bartosz Bednorz,POL,male,1994-07-25,2.01,84.0,volleyball,0,0,0 -250887268,Bartosz Jurecki,POL,male,1979-01-31,1.92,107.0,handball,0,0,0 -594052376,Bartosz Kurek,POL,male,1988-08-29,2.05,87.0,volleyball,0,0,0 -8007833,Bartosz Losiak,POL,male,1992-05-14,1.9,88.0,volleyball,0,0,0 -678195803,Bas Verwijlen,NED,male,1983-10-01,1.9,85.0,fencing,0,0,0 -902813031,Bashar Resan,IRQ,male,1996-12-22,1.75,69.0,football,0,0,0 -495486399,Bashir Abdi,BEL,male,1989-02-10,1.76,56.0,athletics,0,0,0 -785539303,Bashir Asgari Babajanzadeh Darzi,IRI,male,1989-08-20,1.88,130.0,wrestling,0,0,0 -418800406,Bassel Alrayes,QAT,male,1979-03-04,1.8,95.0,handball,0,0,0 -366413893,Bassem Hassan Mohammed,QAT,male,1987-05-31,1.77,78.0,equestrian,0,0,0 -670680423,Basten Caerts,BEL,male,1997-10-27,1.85,85.0,aquatics,0,0,0 -735742491,Bastian Steger,GER,male,1981-03-19,1.7,65.0,table tennis,0,0,1 -231798489,Bastien Auzeil,FRA,male,1989-10-22,1.95,89.0,athletics,0,0,0 -795525665,Battsetseg Soronzonbold,MGL,female,1990-05-03,1.7,67.0,wrestling,0,0,0 -409808283,Batuhan Gozgec,TUR,male,1990-12-20,1.72,,boxing,0,0,0 -38294792,Bauke Mollema,NED,male,1986-11-26,1.84,68.0,cycling,0,0,0 -931107334,Baul An,KOR,male,1994-03-25,1.69,66.0,judo,0,1,0 -726557644,Bautista Ezcurra,ARG,male,1995-04-21,1.8,82.0,rugby sevens,0,0,0 -887354934,Bautista Saubidet Birkner,ARG,male,1995-11-28,1.79,72.0,sailing,0,0,0 -350429572,Bayron Guama de la Cruz,ECU,male,1985-06-14,1.65,61.0,cycling,0,0,0 -298210804,Bayron Piedra,ECU,male,1982-08-19,1.75,68.0,athletics,0,0,0 -534612447,Beata Mikolajczyk,POL,female,1985-10-15,1.7,68.0,canoe,0,0,1 -663483429,Beata Naigambo,NAM,female,1980-03-11,1.67,48.0,athletics,0,0,0 -98378488,Beate Schrott,AUT,female,1988-04-15,1.77,71.0,athletics,0,0,0 -909285894,Beatrice Bartelloni,ITA,female,1993-02-05,1.65,50.0,cycling,0,0,0 -864630818,Beatrice Callegari,ITA,female,1991-12-20,1.75,60.0,aquatics,0,0,0 -496142981,Beatrice Chepkoech,KEN,female,1991-07-06,1.7,54.0,athletics,0,0,0 -30019986,Beatrice Edwige,FRA,female,1988-10-03,1.82,76.0,handball,0,1,0 -687126240,Beatrice Gyaman,GHA,female,1987-02-17,,53.0,athletics,0,0,0 -33337656,Beatrice Kamuchanga Alice,COD,female,1997-11-20,,,athletics,0,0,0 -870483785,Beatriz,BRA,female,1993-12-17,1.76,69.0,football,0,0,0 -247018721,Beatriz Elizabeth Piron Candelario,DOM,female,1995-02-27,1.5,48.0,weightlifting,0,0,0 -483770449,Beatriz Feres,BRA,female,1988-02-22,1.63,56.0,aquatics,0,0,0 -128263534,Beatriz Ferrer-Salat,ESP,female,1966-03-11,1.76,58.0,equestrian,0,0,0 -870056502,Beatriz Muhlbauer,BRA,female,1986-02-26,1.72,67.0,rugby sevens,0,0,0 -871879403,Beatriz Ortiz Munoz,ESP,female,1995-06-21,1.76,64.0,aquatics,0,0,0 -102300427,Beatriz Pascual,ESP,female,1982-05-09,1.63,53.0,athletics,0,0,0 -237614757,Beatriz Perez,ESP,female,1991-05-04,1.67,60.0,hockey,0,0,0 -844171101,Becky Sauerbrunn,USA,female,1985-06-06,1.7,61.0,football,0,0,0 -229820052,Bedan Karoki Muchiri,KEN,male,1990-08-21,1.7,54.0,athletics,0,0,0 -72183566,Bediha Gun,TUR,female,1994-10-26,1.67,53.0,wrestling,0,0,0 -389909627,Bedopassa Buassat Djonde,GBS,male,1992-09-20,1.72,97.0,wrestling,0,0,0 -618056949,Begona Garcia,ESP,female,1995-07-19,1.64,54.0,hockey,0,0,0 -471766284,Begona Gumucio,CHI,female,1992-01-14,1.69,66.0,sailing,0,0,0 -57339699,Behdad Salimikordasiabi,IRI,male,1989-12-08,1.92,170.0,weightlifting,0,0,0 -302808648,Beka Gviniashvili,GEO,male,1995-10-26,1.75,95.0,judo,0,0,0 -683307277,Bekhbayar Erdenebat,MGL,male,1992-08-13,1.61,62.0,wrestling,0,0,0 -133958272,Bekir Karayel,TUR,male,1982-05-10,1.7,62.0,athletics,0,0,0 -922060137,Bekir Ozlu,TUR,male,1988-08-30,1.7,60.0,judo,0,0,0 -252644554,Bektemir Melikuziev,UZB,male,1996-04-08,1.77,,boxing,0,1,0 -312706182,Bekzod Abdurakhmonov,UZB,male,1990-03-15,1.72,74.0,wrestling,0,0,0 -234823649,Bela Simon,HUN,male,1988-08-04,1.86,85.0,rowing,0,0,0 -25375270,Belen Casetta,ARG,female,1994-09-26,1.63,52.0,athletics,0,0,0 -126816152,Belen Succi,ARG,female,1985-10-16,1.77,72.0,hockey,0,0,0 -782850902,Belinda Hocking,AUS,female,1990-09-14,1.67,57.0,aquatics,0,0,0 -152287521,Belinda Trussell,CAN,female,1971-08-27,1.6,50.0,equestrian,0,0,0 -925936432,Ben Blankenship,USA,male,1988-12-15,1.78,63.0,athletics,0,0,0 -8906492,Ben Kanute,USA,male,1992-12-14,1.83,70.0,triathlon,0,0,0 -614521259,Ben Maher,GBR,male,1983-01-30,1.64,50.0,equestrian,0,0,0 -249786185,Ben Pinkelman,USA,male,1994-06-13,1.94,99.0,rugby sevens,0,0,0 -905247284,Ben Proud,GBR,male,1994-09-21,1.89,92.0,aquatics,0,0,0 -193268614,Ben Saxton,GBR,male,1990-06-14,1.75,73.0,sailing,0,0,0 -499087119,Ben Saxton,CAN,male,1988-11-21,2.0,92.0,volleyball,0,0,0 -764834942,Ben Schwietert,NED,male,1997-02-16,1.92,83.0,aquatics,0,0,0 -922248947,Ben St Lawrence,AUS,male,1981-11-07,1.79,65.0,athletics,0,0,0 -43362179,Ben Uzoh,NGR,male,1988-03-18,1.91,92.0,basketball,0,0,0 -113110709,Ben Vogg,SUI,male,1992-09-07,1.8,73.0,equestrian,0,0,0 -727557746,Ben Youssef Meite,CIV,male,1986-11-11,1.8,75.0,athletics,0,0,0 -719296944,Bence Demeter,HUN,male,1990-03-20,1.8,72.0,modern pentathlon,0,0,0 -795193546,Bence Pulai,HUN,male,1991-10-27,1.93,89.0,aquatics,0,0,0 -282673343,Bence Venyercsan,HUN,male,1996-01-08,1.73,56.0,athletics,0,0,0 -197443251,Bendeguz Petervari-Molnar,HUN,male,1993-03-14,1.9,95.0,rowing,0,0,0 -890462649,Benedek Olah,FIN,male,1991-03-29,1.91,87.0,table tennis,0,0,0 -139635489,Benik Abrahamyan,GEO,male,1985-07-31,1.86,115.0,athletics,0,0,0 -181551295,Benjamin Auffret,FRA,male,1995-03-15,1.66,54.0,aquatics,0,0,0 -544916259,Benjamin Ceiner,HUN,male,1992-04-24,1.97,94.0,canoe,0,0,0 -899077018,Benjamin Compaore,FRA,male,1987-08-05,1.89,83.0,athletics,0,0,0 -301130953,Benjamin Didier Hennequin,FRA,male,1984-08-24,1.74,84.0,weightlifting,0,0,0 -857144847,Benjamin Enzema,GEQ,male,1989-03-25,1.7,63.0,athletics,0,0,0 -824789786,Benjamin Errol Provisor,USA,male,1990-06-26,1.73,88.0,wrestling,0,0,0 -811228409,Benjamin Fletcher,GBR,male,1992-03-13,1.85,100.0,judo,0,0,0 -192357893,Benjamin Gischard,SUI,male,1995-11-17,1.62,62.0,gymnastics,0,0,0 -202636824,Benjamin Gratz,HUN,male,1996-02-16,1.85,76.0,aquatics,0,0,0 -65135786,Benjamin Grez Ahrens,CHI,male,1992-12-03,1.81,76.0,sailing,0,0,0 -429748988,Benjamin Hallock,USA,male,1997-11-22,1.99,108.0,aquatics,0,0,0 -138745925,Benjamin Hockin Brusquetti,PAR,male,1986-09-27,1.95,90.0,aquatics,0,0,0 -678983494,Benjamin Kiplagat,UGA,male,1989-03-04,,,athletics,0,0,0 -594397202,Benjamin Lang,FRA,male,1987-02-04,1.86,86.0,rowing,0,0,0 -735684995,Benjamin Martin,CAN,male,1987-04-18,1.82,75.0,hockey,0,0,0 -917375467,Benjamin Savsek,SLO,male,1987-03-24,1.77,74.0,canoe,0,0,0 -279378478,Benjamin Schulte,GUM,male,1995-12-22,1.93,83.0,aquatics,0,0,0 -466190490,Benjamin Steffen,SUI,male,1982-03-08,1.89,78.0,fencing,0,0,0 -54447153,Benjamin Thorne,CAN,male,1993-03-19,1.8,56.0,athletics,0,0,0 -64578005,Benjamin Toniutti,FRA,male,1989-10-30,1.83,73.0,volleyball,0,0,0 -674024954,Benjamin Vadnai,HUN,male,1995-12-30,1.83,82.0,sailing,0,0,0 -121967304,Benjamin Waterhouse,ASA,male,1985-06-11,1.72,71.0,judo,0,0,0 -12228881,Benn Harradine,AUS,male,1982-10-14,1.98,115.0,athletics,0,0,0 -381218659,Benny Muziyo Muziyo,ZAM,male,1992-11-08,,,boxing,0,0,0 -580939747,Benoit Paire,FRA,male,1989-05-08,1.94,80.0,tennis,0,0,0 -544056432,Benson Gicharu Njangiru,KEN,male,1985-05-03,1.68,,boxing,0,0,0 -141851746,Benson Kiplagat Seurei,BRN,male,1984-03-27,1.75,60.0,athletics,0,0,0 -574621304,Berik Abdrakhmanov,KAZ,male,1986-06-20,1.65,,boxing,0,0,0 -662027088,Bernadette Graf,AUT,female,1992-06-25,1.75,70.0,judo,0,0,0 -951599589,Bernadette Pujals,MEX,female,1968-06-08,1.58,55.0,equestrian,0,0,0 -155963598,Bernadette Szocs,ROU,female,1995-03-05,1.59,48.0,table tennis,0,0,0 -347604292,Bernard Lagat,USA,male,1974-12-12,1.73,58.0,athletics,0,0,0 -78415215,Bernardin Ledoux Kingue Matam,FRA,male,1990-05-20,1.63,69.0,weightlifting,0,0,0 -839460124,Bernardo Baloyes,COL,male,1994-01-06,1.68,66.0,athletics,0,0,0 -833677171,Bernardo Guerrero Diaz,CHI,male,1986-06-10,1.9,75.0,rowing,0,0,0 -564965643,Bernardo Oliveira,BRA,male,1993-06-08,1.73,70.0,archery,0,0,0 -351550766,Bernardo Oneto Gomes,BRA,male,1993-11-12,1.88,90.0,aquatics,0,0,0 -44038366,Bernardo Rocha,BRA,male,1989-07-03,1.81,95.0,aquatics,0,0,0 -619849300,Bernd Wiesberger,AUT,male,1985-10-08,1.89,89.0,golf,0,0,0 -997731920,Bernhard Sieber,AUT,male,1990-08-06,1.8,72.0,rowing,0,0,0 -684096917,Berta Betanzos Moro,ESP,female,1988-01-15,1.79,71.0,sailing,0,0,0 -134339876,Berta Bonastre,ESP,female,1992-06-03,1.57,50.0,hockey,0,0,0 -98117610,Berta Garcia,ESP,female,1982-04-12,1.73,73.0,rugby sevens,0,0,0 -390927427,Berthrade Simone Flore Bikatal,CMR,female,1992-07-23,1.83,76.0,volleyball,0,0,0 -347904621,Bertrand Roine,QAT,male,1981-02-17,1.98,99.0,handball,0,0,0 -634276383,Beryl Gastaldello,FRA,female,1995-02-16,1.76,66.0,aquatics,0,0,0 -613088812,Beslan Mudranov,RUS,male,1986-07-07,1.66,60.0,judo,1,0,0 -822286344,Besu Sado,ETH,female,1996-06-12,1.72,56.0,athletics,0,0,0 -943971426,Beth Potter,GBR,female,1991-12-27,1.7,51.0,athletics,0,0,0 -552255079,Bethanie Mattek-Sands,USA,female,1985-03-23,1.68,65.0,tennis,1,0,0 -63368470,Betlhem Desalegn,UAE,female,1991-11-11,1.6,55.0,athletics,0,0,0 -325783858,Betsy Hassett,NZL,female,1990-08-04,1.58,56.0,football,0,0,0 -392303714,Betsy Saina,KEN,female,1988-06-30,1.52,47.0,athletics,0,0,0 -79046621,Betty Heidler,GER,female,1983-10-14,1.75,85.0,athletics,0,0,0 -690348095,Betzabeth Angelica Arguello Villegas,VEN,female,1991-01-28,1.64,53.0,wrestling,0,0,0 -523896033,Beverly Ramos,PUR,female,1987-08-24,1.68,52.0,athletics,0,0,0 -644567979,Bianca Farella,CAN,female,1992-04-10,1.73,73.0,rugby sevens,0,0,1 -826009653,Bianca Hammett,AUS,female,1990-09-12,1.72,55.0,aquatics,0,0,0 -427195390,Bianca Razor,ROU,female,1994-08-08,1.7,59.0,athletics,0,0,0 -579106443,Bianca Stuart,BAH,female,1988-05-17,,,athletics,0,0,0 -245553516,Bianca Walkden,GBR,female,1991-09-29,1.82,74.0,taekwondo,0,0,1 -179256116,Bianca Williams,GBR,female,1993-12-18,1.69,56.0,athletics,0,0,0 -164136445,Bianka Busa,SRB,female,1994-07-25,1.87,74.0,volleyball,0,1,0 -107444997,Biao Chai,CHN,male,1990-10-10,1.86,80.0,badminton,0,0,0 -589789761,Bibiro Ali Taher,CHA,female,1988-04-24,1.6,44.0,athletics,0,0,0 -440136772,Biko Adema,KEN,male,1987-09-01,1.77,85.0,rugby sevens,0,0,0 -583607934,Bilal Tabti,ALG,male,1993-06-07,1.75,62.0,athletics,0,0,0 -543026073,Bilel Mhamdi,TUN,male,1992-01-27,1.76,,boxing,0,0,0 -815957774,Biljana Pavicevic,MNE,female,1988-05-12,1.69,63.0,handball,0,0,0 -484484990,Billy Bakker,NED,male,1988-11-23,1.88,82.0,hockey,0,0,0 -585423464,Billy Besson,FRA,male,1981-03-08,1.69,69.0,sailing,0,0,0 -594152540,Billy Odhiambo,KEN,male,1993-11-07,1.84,95.0,rugby sevens,0,0,0 -787821587,Billy Scott Irakoze,BDI,male,1996-10-30,1.96,74.0,aquatics,0,0,0 -426790116,Bilyal Makhov,RUS,male,1987-09-20,2.0,125.0,wrestling,0,0,0 -613452490,Bin Dong,CHN,male,1988-11-22,1.8,74.0,athletics,0,0,1 -525898651,Bin Feng,CHN,female,1994-04-03,1.84,95.0,athletics,0,0,0 -877888215,Bin Lv,CHN,male,1994-10-18,1.65,,boxing,0,0,0 -420088639,Bin Yang,CHN,male,1989-07-10,1.8,80.0,wrestling,0,0,0 -774637226,Binbin Zhang,CHN,female,1989-02-23,1.64,55.0,shooting,0,1,0 -597092136,Bingtian Su,CHN,male,1989-08-29,1.73,70.0,athletics,0,0,0 -661948747,Bintou Dieme,SEN,female,1984-02-01,1.67,58.0,basketball,0,0,0 -442225974,Binyuan Hu,CHN,male,1977-11-07,1.8,75.0,shooting,0,0,0 -944420337,Birgit Koschischek,AUT,female,1987-05-22,1.69,61.0,aquatics,0,0,0 -271713930,Birgit Michels,GER,female,1984-09-28,1.78,70.0,badminton,0,0,0 -131536621,Birhanu Balew,BRN,male,1996-02-27,,,athletics,0,0,0 -774582149,Birsel Vardarli Demirmen,TUR,female,1984-07-12,1.75,60.0,basketball,0,0,0 -112486635,Birzhan Zhakypov,KAZ,male,1984-07-07,1.63,,boxing,0,0,0 -522639871,Bjorn Hornikel,GER,male,1992-05-06,1.92,91.0,aquatics,0,0,0 -470613425,Bjorn van den Ende,NED,male,1986-01-10,1.9,70.0,rowing,0,0,0 -221212634,Blabjerg Mathiasen,DEN,male,1995-01-11,1.85,78.0,football,0,0,0 -735422828,Blai Mallarach Guell,ESP,male,1987-08-21,1.87,87.0,aquatics,0,0,0 -395509377,Blair Cameron Bann,CAN,male,1988-02-26,1.84,84.0,volleyball,0,0,0 -304290646,Blair Evans,AUS,female,1991-04-03,1.75,65.0,aquatics,0,0,0 -157315163,Blair Hilton,NZL,male,1989-08-28,1.81,78.0,hockey,0,0,0 -930133339,Blair Tarrant,NZL,male,1990-05-11,1.85,83.0,hockey,0,0,0 -842741490,Blair Tuke,NZL,male,1989-07-25,1.81,78.0,sailing,1,0,0 -435079767,Blake Blackburn,AUS,male,1992-08-03,1.81,82.0,shooting,0,0,0 -993776091,Blake Gaudry,AUS,male,1991-11-29,1.79,72.0,gymnastics,0,0,0 -950950151,Blake Govers,AUS,male,1996-07-06,1.87,85.0,hockey,0,0,0 -570608473,Blake Pieroni,USA,male,1995-11-15,1.88,86.0,aquatics,1,0,0 -214905854,Blandine Dancette,FRA,female,1988-02-14,1.69,60.0,handball,0,0,0 -690400835,Blanka Vlasic,CRO,female,1983-11-08,1.93,71.0,athletics,0,0,1 -855954658,Blaz Blagotinsek,SLO,male,1994-01-17,2.02,108.0,handball,0,0,0 -21924133,Blaz Janc,SLO,male,1996-11-20,1.86,88.0,handball,0,0,0 -473644468,Blessing Oborududu,NGR,female,1989-03-12,1.63,63.0,wrestling,0,0,0 -95183266,Blessing Okagbare,NGR,female,1988-10-09,1.81,71.0,athletics,0,0,0 -306720792,Bo Qiu,CHN,male,1993-01-31,1.65,62.0,aquatics,0,0,0 -891887866,Boaz Meylink,NED,male,1984-03-22,1.95,93.0,rowing,0,0,1 -502149585,Bob de Voogd,NED,male,1988-09-16,1.83,80.0,hockey,0,0,0 -491078381,Bobae Ki,KOR,female,1988-02-20,1.67,57.0,archery,1,0,1 -650906817,Bobana Velickovic,SRB,female,1990-01-25,1.7,59.0,shooting,0,0,0 -112585422,Bobby Lea,USA,male,1983-10-17,1.88,77.0,cycling,0,0,0 -130126334,Bobur Shokirjonov,UZB,male,1990-12-05,1.95,105.0,athletics,0,0,0 -495066893,Bode Abiodun,NGR,male,1980-09-10,1.7,68.0,table tennis,0,0,0 -903668874,Bodi Turner,AUS,male,1994-09-18,1.8,81.0,cycling,0,0,0 -915515161,Bodin Isara,THA,male,1990-12-12,1.75,78.0,badminton,0,0,0 -215144146,Bodo Essissima Madeleine Samantha,CMR,female,1992-04-29,1.82,75.0,volleyball,0,0,0 -813532025,Boe Warawara,VAN,male,1995-01-26,,,boxing,0,0,0 -4071248,Bogdan Bogdanovic,SRB,male,1992-08-18,1.97,99.0,basketball,0,1,0 -68724456,Bogdan Nikishin,UKR,male,1980-05-29,1.88,85.0,fencing,0,0,0 -23204063,Boglarka Kapas,HUN,female,1993-04-22,1.68,54.0,aquatics,0,0,1 -49967537,Bogna Jozwiak,POL,female,1983-04-25,1.82,80.0,fencing,0,0,0 -585709776,Bohdan Bondarenko,UKR,male,1989-08-30,1.98,77.0,athletics,0,0,1 -309321530,Boitumelo Masilo,BOT,male,1995-08-05,,,athletics,0,0,0 -56310662,Bojan Bogdanovic,CRO,male,1989-04-18,2.0,103.0,basketball,0,0,0 -371663534,Bojan Tokic,SLO,male,1981-01-13,1.78,76.0,table tennis,0,0,0 -597510808,Bojana Popovic,MNE,female,1979-11-20,1.88,86.0,handball,0,0,0 -189764365,Bojana Zivkovic,SRB,female,1988-03-29,1.86,72.0,volleyball,0,1,0 -599338352,Bokai Huang,CHN,male,1996-09-26,1.9,78.0,athletics,0,0,0 -633981325,Bokyeong Jeong,KOR,female,1991-04-17,1.53,51.0,judo,0,1,0 -37603597,Bombayla Devi Laishram,IND,female,1985-02-22,1.64,60.0,archery,0,0,0 -969273805,Bomi Kim,KOR,female,1985-10-07,1.57,54.0,hockey,0,0,0 -841861773,Bonchan Ku,KOR,male,1993-01-31,1.81,84.0,archery,2,0,0 -593350909,Bongil Gu,KOR,male,1989-04-27,1.82,70.0,fencing,0,0,0 -947726,Boniface Mucheru Tumuti,KEN,male,1992-05-02,1.75,72.0,athletics,0,1,0 -25616888,Boonsak Ponsana,THA,male,1982-02-22,1.79,75.0,badminton,0,0,0 -678784901,Boonthung Srisung,THA,male,1981-05-30,1.71,59.0,athletics,0,0,0 -993211347,Bora Gulari,USA,male,1975-10-22,1.81,74.0,sailing,0,0,0 -310122376,Boris Balaz,SVK,male,1997-11-20,1.82,72.0,archery,0,0,0 -404999388,Boris Berian,USA,male,1992-12-19,1.83,69.0,athletics,0,0,0 -241752566,Boris Diaw,FRA,male,1982-04-16,2.03,115.0,basketball,0,0,0 -861405399,Boris Kirillov,AZE,male,1992-08-04,1.81,75.0,aquatics,0,0,0 -703703338,Boris Yotov,AZE,male,1996-02-25,1.93,93.0,rowing,0,0,0 -206019414,Borislav Stefanov Novachkov,BUL,male,1989-11-29,1.7,71.0,wrestling,0,0,0 -888969887,Borja Carrascosa,ESP,male,1982-02-05,1.82,71.0,equestrian,0,0,0 -142258251,Borja Fernandez,QAT,male,1981-12-25,2.06,110.0,handball,0,0,0 -200431710,Borja Vivas,ESP,male,1984-05-26,2.04,139.0,athletics,0,0,0 -139684134,Borna Coric,CRO,male,1996-11-14,1.85,78.0,tennis,0,0,0 -720390128,Borys Shvets,UKR,male,1991-08-20,1.75,65.0,sailing,0,0,0 -895703195,Bosco Perez-Pla,ESP,male,1987-09-26,1.8,78.0,hockey,0,0,0 -103791379,Bostjan Macek,SLO,male,1972-06-17,1.74,118.0,shooting,0,0,0 -876174352,Bouchra Fatima Zohra Hirech,ALG,female,2000-08-22,1.7,80.0,weightlifting,0,0,0 -142434686,Boudewijn Roell,NED,male,1989-05-12,1.95,100.0,rowing,0,0,1 -797423057,Bourhan Abro,DJI,male,1995-05-30,1.8,70.0,aquatics,0,0,0 -819043889,Boyd Martin,USA,male,1979-08-20,1.88,79.0,equestrian,0,0,0 -449397409,Bozo Starcevic,CRO,male,1988-12-11,1.72,78.0,wrestling,0,0,0 -573991365,Bradlee Ashby,NZL,male,1995-11-23,2.0,92.0,aquatics,0,0,0 -804733660,Bradley Adkins,USA,male,1993-12-30,1.89,79.0,athletics,0,0,0 -220375842,Bradley Edward Tandy,RSA,male,1991-05-02,1.58,86.0,aquatics,0,0,0 -901307783,Bradley Shaw,NZL,male,1983-02-13,1.81,92.0,hockey,0,0,0 -943842758,Bradley Vincent,MRI,male,1991-11-30,1.95,89.0,aquatics,0,0,0 -499035410,Bradley Wiggins,GBR,male,1980-04-28,1.9,82.0,cycling,1,0,0 -139075859,Brady Ellison,USA,male,1988-10-27,1.81,86.0,archery,0,1,1 -140387635,Brahim Kaazouzi,MAR,male,1990-06-15,1.79,62.0,athletics,0,0,0 -85379844,Braian Toledo,ARG,male,1993-09-08,1.87,100.0,athletics,0,0,0 -327920403,Bralon Taplin,GRN,male,1992-05-08,,,athletics,0,0,0 -919979883,Branca Feres,BRA,female,1988-02-22,1.64,56.0,aquatics,0,0,0 -738096310,Brandon Jones,BIZ,male,1987-07-19,1.99,86.0,athletics,0,0,0 -995088210,Brandon McBride,CAN,male,1994-06-15,1.93,74.0,athletics,0,0,0 -148847003,Brandon Schuster,SAM,male,1998-04-23,1.88,75.0,aquatics,0,0,0 -152917319,Brandon Starc,AUS,male,1993-11-24,1.88,71.0,athletics,0,0,0 -873969989,Brandon Stone,RSA,male,1993-04-20,1.8,65.0,golf,0,0,0 -990046579,Brandon Valentine-Parris,VIN,male,1995-04-17,1.73,55.0,athletics,0,0,0 -128091825,Brandonn Almeida,BRA,male,1997-03-16,1.86,73.0,aquatics,0,0,0 -349293339,Branislav Mitrovic,SRB,male,1985-01-30,2.01,100.0,aquatics,1,0,0 -890726080,Brankica Mihajlovic,SRB,female,1991-04-13,1.9,83.0,volleyball,0,1,0 -826864352,Brave Lifa,MAW,male,1995-09-05,1.75,70.0,aquatics,0,0,0 -741657080,Brayan Garcia,HON,male,1993-05-26,1.75,69.0,football,0,0,0 -734779763,Brayan Ramirez,HON,male,1994-06-16,1.77,66.0,football,0,0,0 -193606583,Breanna Stewart,USA,female,1994-08-27,1.93,79.0,basketball,1,0,0 -630721644,Bredni Roque Mendoza,MEX,male,1987-11-11,1.6,69.0,weightlifting,0,0,0 -845586759,Breege Connolly,IRL,female,1978-02-01,1.63,54.0,athletics,0,0,0 -383824981,Brenda Bowskill,CAN,female,1992-04-21,1.7,68.0,sailing,0,0,0 -911119710,Brenda Flores,MEX,female,1991-09-04,1.63,50.0,athletics,0,0,0 -477118293,Brenda Martinez,USA,female,1987-09-08,1.71,56.0,athletics,0,0,0 -340440422,Brenda Rojas,ARG,female,1995-10-15,1.66,64.0,canoe,0,0,0 -293582359,Brenda Yamyleth Bailey Gomez,HON,female,1994-09-11,1.59,55.0,wrestling,0,0,0 -695503192,Brendan Boyce,IRL,male,1986-10-08,1.83,76.0,athletics,0,0,0 -262561474,Brendan Emmet Irvine,IRL,male,1996-05-17,1.67,,boxing,0,0,0 -170273040,Brendan Hodge,CAN,male,1984-12-31,1.8,72.0,rowing,0,0,0 -911726861,Brenden Bissett,CAN,male,1993-01-28,1.78,73.0,hockey,0,0,0 -42739871,Brendon Reading,AUS,male,1989-01-26,1.83,62.0,athletics,0,0,0 -321655820,Brendon Rodney,CAN,male,1992-04-09,1.95,80.0,athletics,0,0,1 -406829138,Brenessa Thompson,GUY,female,1996-07-22,1.63,62.0,athletics,0,0,0 -125216390,Brent Bookwalter,USA,male,1984-02-16,1.81,68.0,cycling,0,0,0 -998710520,Bret Bonanni,USA,male,1994-01-20,1.94,92.0,aquatics,0,0,0 -430001444,Brett Robinson,AUS,male,1991-05-08,1.76,60.0,athletics,0,0,0 -883824307,Brian Afanador,PUR,male,1997-03-06,1.75,68.0,table tennis,0,0,0 -234656577,Brian Babilonia,PUR,male,1994-09-16,1.8,66.0,cycling,0,0,0 -98906595,Brian Baker,USA,male,1985-04-30,1.91,77.0,tennis,0,0,0 -652345352,Brian Pintado,ECU,male,1995-07-29,1.68,57.0,athletics,0,0,0 -870923074,Brian Rosso,ARG,male,1987-08-16,1.84,84.0,rowing,0,0,0 -353066862,Briana Provancha,USA,female,1989-04-25,1.71,66.0,sailing,0,0,0 -861484261,Brianna Rollins,USA,female,1991-08-18,1.66,58.0,athletics,1,0,0 -123998646,Brianna Throssell,AUS,female,1996-02-10,1.75,61.0,aquatics,0,0,0 -542571086,Brianne Theisen Eaton,CAN,female,1988-12-18,1.75,64.0,athletics,0,0,1 -509052821,Brice Etes,MON,male,1984-04-11,1.85,73.0,athletics,0,0,0 -478313255,Brice Leverdez,FRA,male,1986-04-09,1.8,75.0,badminton,0,0,0 -237845996,Bridgitte Ellen Hartley,RSA,female,1983-07-14,1.74,65.0,canoe,0,0,0 -856612683,Brigita Virbalyte-Dimsiene,LTU,female,1985-02-01,1.63,48.0,athletics,0,0,0 -211902340,Brigitte Merlano,COL,female,1982-04-29,1.74,65.0,athletics,0,0,0 -44753775,Brigitte Ntiamoah,FRA,female,1994-03-05,1.72,66.0,athletics,0,0,0 -308919050,Brijesh Lawrence,SKN,male,1989-12-27,1.8,93.0,athletics,0,0,0 -345441615,Briken Calja,ALB,male,1990-02-19,1.7,69.0,weightlifting,0,0,0 -128651141,Brimin Kiprop Kipruto,KEN,male,1985-07-31,1.75,55.0,athletics,0,0,0 -349753082,Brinn Bevan,GBR,male,1997-06-16,1.65,62.0,gymnastics,0,0,0 -893795627,Britt Eerland,NED,female,1994-02-22,1.68,62.0,table tennis,0,0,0 -395081312,Britta Buthe,GER,female,1988-05-25,1.86,78.0,volleyball,0,0,0 -524776452,Brittany Benn,CAN,female,1989-04-23,1.65,68.0,rugby sevens,0,0,1 -837634473,Brittany Borman,USA,female,1989-07-01,1.83,77.0,athletics,0,0,0 -136154437,Brittany Crew,CAN,female,1994-03-06,1.78,112.0,athletics,0,0,0 -974255098,Brittany Elmslie,AUS,female,1994-06-19,1.8,69.0,aquatics,1,1,0 -975241751,Brittany Maclean,CAN,female,1994-03-03,1.68,59.0,aquatics,0,0,1 -667985741,Brittany O'Brien,AUS,female,1998-05-27,1.68,55.0,aquatics,0,0,0 -601613381,Brittany Rogers,CAN,female,1993-06-08,1.65,60.0,gymnastics,0,0,0 -387060457,Brittney Griner,USA,female,1990-10-18,2.03,94.0,basketball,1,0,0 -772781845,Brittney Reese,USA,female,1986-09-09,1.73,61.0,athletics,0,1,0 -476644551,Brock Motum,AUS,male,1990-10-16,2.08,107.0,basketball,0,0,0 -942791136,Bronte Barratt,AUS,female,1989-02-08,1.71,58.0,aquatics,0,1,0 -936338083,Bronte Campbell,AUS,female,1994-05-14,1.79,58.0,aquatics,1,0,0 -339065127,Bronwen Knox,AUS,female,1986-04-16,1.82,88.0,aquatics,0,0,0 -625465432,Brook Robertson,NZL,male,1994-02-19,1.94,95.0,rowing,0,0,0 -229593375,Brooke Crain,USA,female,1993-04-29,1.63,55.0,cycling,0,0,0 -198522539,Brooke Henderson,CAN,female,1997-09-10,1.63,50.0,golf,0,0,0 -723701996,Brooke Neal,NZL,female,1992-07-04,1.87,71.0,hockey,0,0,0 -176282194,Brooke Peris,AUS,female,1993-01-16,1.72,57.0,hockey,0,0,0 -290572119,Brooke Stratton,AUS,female,1993-07-12,,,athletics,0,0,0 -586492695,Brooke Sweat,USA,female,1986-03-27,1.73,69.0,volleyball,0,0,0 -873916818,Bruna,BRA,female,1985-10-16,1.78,65.0,football,0,0,0 -877478692,Bruna Farias,BRA,female,1992-05-19,1.56,51.0,athletics,0,0,0 -171915543,Bruna Takahashi,BRA,female,2000-07-19,1.7,54.0,table tennis,0,0,0 -641508892,Bruno Bethlem,BRA,male,1975-10-22,1.81,72.0,sailing,0,0,0 -462138517,Bruno Fernandes,POR,male,1994-09-08,1.82,80.0,football,0,0,0 -934893676,Bruno Fratus,BRA,male,1989-06-30,1.87,80.0,aquatics,0,0,0 -715319246,Bruno Hortelano,ESP,male,1991-09-18,1.8,70.0,athletics,0,0,0 -44169801,Bruno Lima,ARG,male,1996-02-04,1.98,87.0,volleyball,0,0,0 -436846514,Bruno Mendonca,BRA,male,1984-01-07,1.83,93.0,hockey,0,0,0 -696736168,Bruno Mossa Rezende,BRA,male,1986-07-02,1.9,76.0,volleyball,1,0,0 -836716137,Bruno Ortiz Canavate Ozeki,ESP,male,1993-02-15,1.94,88.0,aquatics,0,0,0 -68618209,Bruno Oscar Schmidt,BRA,male,1986-10-06,1.85,85.0,volleyball,1,0,0 -671022008,Bruno Paes,BRA,male,1993-06-24,1.77,76.0,hockey,0,0,0 -248706880,Bruno Passaro,ARG,male,1989-02-10,1.75,72.0,equestrian,0,0,0 -365680500,Bruno Soares,BRA,male,1982-02-27,1.8,75.0,tennis,0,0,0 -313463896,Bruno Varela,POR,male,1994-11-04,1.9,80.0,football,0,0,0 -118251415,Bruno de Barros,BRA,male,1987-01-07,1.82,85.0,athletics,0,0,0 -526513149,Bryan Acosta,HON,male,1993-11-24,1.75,74.0,football,0,0,0 -442636270,Bryan Gabriel Sola Zambrano,ECU,male,1992-04-03,1.78,75.0,rowing,0,0,0 -682193857,Bryan Keane,IRL,male,1980-08-20,1.75,65.0,triathlon,0,0,0 -692740020,Bryden Nicholas,COK,male,1989-03-10,1.7,70.0,canoe,0,0,0 -159693755,Bryony Page,GBR,female,1990-12-10,1.72,62.0,gymnastics,0,1,0 -358642208,Bryony Shaw,GBR,female,1983-04-28,1.66,60.0,sailing,0,0,0 -257258199,Bubba Watson,USA,male,1978-11-05,1.91,81.0,golf,0,0,0 -833335595,Buly da Conceicao Triste,STP,male,1991-07-04,1.64,58.0,canoe,0,0,0 -326008552,Bunturabie Jalloh,SLE,female,1998-05-10,,79.0,aquatics,0,0,0 -581220953,Burkheart Ellis Jr,BAR,male,1992-09-18,1.91,85.0,athletics,0,0,0 -613127539,Buse Tosun,TUR,female,1995-12-05,1.73,69.0,wrestling,0,0,0 -436450943,Bush Mwale,KEN,male,1993-11-14,1.85,92.0,rugby sevens,0,0,0 -510153828,Busra Katipoglu,TUR,female,1992-01-17,1.65,63.0,judo,0,0,0 -812259982,Byambajav Tseveenravdan,MGL,male,1990-07-07,1.69,58.0,athletics,0,0,0 -942162925,Byeonghun An,KOR,male,1991-09-17,1.87,87.0,golf,0,0,0 -888329699,Byeongkwang Choe,KOR,male,1991-04-07,1.85,73.0,athletics,0,0,0 -920028907,Byron Robinson,USA,male,1995-05-10,1.76,74.0,athletics,0,0,0 -252432878,C.T. Pan,TPE,male,1991-11-12,1.68,65.0,golf,0,0,0 -598623977,Caba Siladji,SRB,male,1990-08-23,1.85,77.0,aquatics,0,0,0 -247346852,Caeleb Dressel,USA,male,1996-08-16,1.91,86.0,aquatics,2,0,0 -914044399,Cagla Buyukakcay,TUR,female,1989-09-28,1.72,58.0,tennis,0,0,0 -406549874,Caia van Maasakker,NED,female,1989-04-05,1.8,69.0,hockey,0,1,0 -18622820,Caileigh Filmer,CAN,female,1996-12-18,1.69,76.0,rowing,0,0,0 -196015602,Caio Bonfim,BRA,male,1991-03-19,1.74,60.0,athletics,0,0,0 -959903896,Caitlin Cooper,AUS,female,1988-02-12,1.71,66.0,football,0,0,0 -181162100,Caitlin Foord,AUS,female,1994-11-11,1.65,58.0,football,0,0,0 -233739460,Caitlin Ryan,NZL,female,1992-02-09,1.78,95.0,canoe,0,0,0 -637321391,Caitlin Sargent-Jones,AUS,female,1992-06-14,1.71,60.0,athletics,0,0,0 -147956765,Caitlin van Sickle,USA,female,1990-01-26,1.66,58.0,hockey,0,0,0 -100675394,Cale Simmons,USA,male,1991-02-05,,,athletics,0,0,0 -715671042,Caleb Mwangangi Ndiku,KEN,male,1992-10-09,1.67,65.0,athletics,0,0,0 -521652208,Caleb Paine,USA,male,1990-11-15,1.91,97.0,sailing,0,0,1 -47877217,Caleb Shepherd,NZL,male,1993-06-29,1.68,55.0,rowing,0,0,0 -7468287,Callum Hawkins,GBR,male,1992-06-22,1.79,62.0,athletics,0,0,0 -205286149,Callum Scotson,AUS,male,1996-08-10,1.84,77.0,cycling,0,1,0 -598986059,Callum Skinner,GBR,male,1992-08-20,1.87,93.0,cycling,1,1,0 -227626203,Calvyn Justus,RSA,male,1995-12-14,1.95,70.0,aquatics,0,0,0 -638140243,Cam Kurle,GBR,male,1997-07-19,1.86,66.0,aquatics,0,0,0 -665917259,Camelia Hotea,ROU,female,1984-10-27,1.75,64.0,handball,0,0,0 -379834078,Cameron Bairstow,AUS,male,1990-12-07,2.06,118.0,basketball,0,0,0 -673317793,Cameron Clark,AUS,male,1993-03-20,1.85,84.0,rugby sevens,0,0,0 -115794046,Cameron Girdlestone,AUS,male,1988-04-29,1.9,92.0,rowing,0,1,0 -464109574,Cameron McEvoy,AUS,male,1994-05-13,1.85,75.0,aquatics,0,0,2 -744574832,Cameron Pimentel,BER,male,1991-04-13,1.83,80.0,sailing,0,0,0 -87311280,Cameron Smedley,CAN,male,1990-10-09,1.78,75.0,canoe,0,0,0 -59463747,Cameron van der Burgh,RSA,male,1988-05-25,1.85,85.0,aquatics,0,1,0 -62078309,Camila,BRA,female,1994-10-10,1.59,60.0,football,0,0,0 -761225267,Camila Pedrosa,BRA,female,1975-03-12,1.72,68.0,aquatics,0,0,0 -403141016,Camila Valle Granados,PER,female,1995-07-07,1.6,60.0,rowing,0,0,0 -814870853,Camilla Cattaneo,ITA,female,1990-02-12,1.73,54.0,aquatics,0,0,0 -711276110,Camilla Cedercreutz,FIN,female,1993-01-31,1.67,66.0,sailing,0,0,0 -338343277,Camilla Hattersley,GBR,female,1995-02-24,1.74,59.0,aquatics,0,0,0 -384002345,Camilla Herrem,NOR,female,1986-10-08,1.67,,handball,0,0,1 -439378306,Camilla Kruger,ZIM,female,1986-09-19,1.61,51.0,equestrian,0,0,0 -118087738,Camilla Patriarca,ITA,female,1994-11-04,1.7,50.0,gymnastics,0,0,0 -766408669,Camilla Speirs,IRL,female,1989-08-23,1.54,57.0,equestrian,0,0,0 -187290528,Camille Abily,FRA,female,1984-12-05,1.68,60.0,football,0,0,0 -373069016,Camille Ayglon Saurina,FRA,female,1985-05-21,1.8,66.0,handball,0,1,0 -788191192,Camille Cheng,HKG,female,1993-05-09,1.78,65.0,aquatics,0,0,0 -919018525,Camille Grassineau,FRA,female,1990-09-10,1.65,58.0,rugby sevens,0,0,0 -614590923,Camille Lacourt,FRA,male,1985-04-22,2.0,85.0,aquatics,0,0,0 -510528142,Camille Lecointre,FRA,female,1985-02-25,1.59,56.0,sailing,0,0,1 -241013969,Camilyne Oyuayo,KEN,female,1982-04-16,1.67,72.0,rugby sevens,0,0,0 -94450987,Cammile Adams,USA,female,1991-09-11,1.73,65.0,aquatics,0,0,0 -495694072,Cancan Ren,CHN,female,1988-01-26,1.68,,boxing,0,0,1 -595425335,Candie Kung,TPE,female,1981-08-08,1.7,70.0,golf,0,0,0 -873362411,Cansel Deniz,KAZ,female,1991-08-26,1.75,67.0,taekwondo,0,0,0 -705883507,Caridad Jerez,ESP,female,1991-01-23,1.66,58.0,athletics,0,0,0 -978280243,Carin Stromberg,SWE,female,1993-07-10,1.84,83.0,handball,0,0,0 -725855648,Carina Ana Garcia Kradolfer,BOL,female,1984-01-03,1.6,58.0,shooting,0,0,0 -448095596,Carina Baer,GER,female,1990-01-23,1.85,75.0,rowing,1,0,0 -840008522,Carina Horn,RSA,female,1989-03-09,1.68,62.0,athletics,0,0,0 -337197215,Carl Dohmann,GER,male,1990-05-18,1.83,62.0,athletics,0,0,0 -570448421,Carl Fredrik Stefan Schoen,SWE,male,1987-07-11,1.87,102.0,wrestling,0,0,0 -48374531,Carl Hester,GBR,male,1967-06-29,1.82,86.0,equestrian,0,1,0 -123412011,Carla Nelte,GER,female,1990-09-21,1.71,60.0,badminton,0,0,0 -415538486,Carla Rebecchi,ARG,female,1984-09-07,1.63,55.0,hockey,0,0,0 -728630028,Carla Salome Rocha,POR,female,1990-04-25,1.57,46.0,athletics,0,0,0 -873188308,Carla Suarez Navarro,ESP,female,1988-09-03,1.62,61.0,tennis,0,0,0 -226631924,Carles Castillejo,ESP,male,1978-08-18,1.65,61.0,athletics,0,0,0 -714231073,Carli Lloyd,USA,female,1989-08-06,1.8,75.0,volleyball,0,0,1 -228340991,Carli Lloyd,USA,female,1982-07-16,1.72,61.0,football,0,0,0 -323715305,Carlien Dirkse van den Heuvel,NED,female,1987-04-16,1.7,56.0,hockey,0,1,0 -215241067,Carlin Isles,USA,male,1989-11-21,1.73,74.0,rugby sevens,0,0,0 -549956425,Carline Bouw,NED,female,1984-12-14,1.84,72.0,rowing,0,1,0 -807476315,Carline Muir,CAN,female,1987-10-01,1.7,65.0,athletics,0,0,0 -233428453,Carling Zeeman,CAN,female,1991-05-27,1.87,85.0,rowing,0,0,0 -924818266,Carlo Tacchini,ITA,male,1995-01-25,1.76,73.0,canoe,0,0,0 -132849150,Carlos Alberto Ramirez Yepes,COL,male,1994-03-12,1.78,65.0,cycling,0,0,1 -639279380,Carlos Andres Mina,ECU,male,1992-04-30,1.87,,boxing,0,0,0 -654897957,Carlos Andres Munoz Jaramillo,COL,male,1992-08-03,1.8,75.0,wrestling,0,0,0 -929589986,Carlos Arturo Izquierdo Mendez,COL,male,1997-10-02,1.82,74.0,wrestling,0,0,0 -875748101,Carlos Cisneros,MEX,male,1993-08-30,1.75,72.0,football,0,0,0 -992315197,Carlos Claverie,VEN,male,1996-09-19,1.95,85.0,aquatics,0,0,0 -664891338,Carlos Coloma Nicolas,ESP,male,1981-09-28,1.71,65.0,cycling,0,0,1 -562138964,Carlos Delfino,ARG,male,1982-08-29,2.0,95.0,basketball,0,0,0 -955217464,Carlos Eduardo Quipo Pilataxi,ECU,male,1990-05-17,1.56,,boxing,0,0,0 -520489478,Carlos Fierro,MEX,male,1994-07-23,1.75,69.0,football,0,0,0 -487944267,Carlos Guerra,MEX,male,1981-08-03,1.96,95.0,volleyball,0,0,0 -770819591,Carlos Lemos,COL,male,1988-06-03,1.83,76.0,athletics,0,0,0 -666394255,Carlos Lobos Munoz,CHI,male,1980-12-21,1.75,73.0,equestrian,0,0,0 -120477759,Carlos Mario Oquendo Zabala,COL,male,1987-11-16,1.86,85.0,cycling,0,0,0 -744295667,Carlos Parro,BRA,male,1979-06-05,1.79,76.0,equestrian,0,0,0 -983424219,Carlos Peralta Gallego,ESP,male,1994-01-30,1.65,60.0,aquatics,0,0,0 -572124752,Carlos Ruben Navarro Valdez,MEX,male,1996-05-08,1.77,58.0,taekwondo,0,0,0 -279620317,Carlos Salcedo,MEX,male,1993-09-29,1.84,82.0,football,0,0,0 -561704538,Carlos Tobalina,ESP,male,1985-08-02,1.88,120.0,athletics,0,0,0 -318016892,Carlos Zenon Balderas Jr.,USA,male,1996-08-24,1.76,,boxing,0,0,0 -848682435,Carlota Ciganda,ESP,female,1990-06-01,1.74,68.0,golf,0,0,0 -707042640,Carlota Petchame,ESP,female,1990-06-25,1.6,53.0,hockey,0,0,0 -637147877,Carlotta Ferlito,ITA,female,1995-02-15,1.6,50.0,gymnastics,0,0,0 -383488560,Carlotta Zofkova,ITA,female,1993-02-22,1.83,74.0,aquatics,0,0,0 -11238404,Carmelo Anthony,USA,male,1984-05-29,2.03,109.0,basketball,1,0,0 -477040413,Carmen Farmer,USA,female,1980-12-04,1.86,81.0,rugby sevens,0,0,0 -440033203,Carmen Martin,ESP,female,1988-05-29,1.72,72.0,handball,0,0,0 -638974451,Carmen Marton,AUS,female,1986-06-30,1.72,67.0,taekwondo,0,0,0 -529017724,Carmen Patricia Martinez,PAR,female,1982-12-26,1.6,55.0,athletics,0,0,0 -692509182,Carmiesha Cox,BAH,female,1995-05-16,,,athletics,0,0,0 -386843805,Carmine Tommasone,ITA,male,1984-03-30,1.67,,boxing,0,0,0 -179760844,Carola Salvatella,ESP,female,1994-07-08,1.71,58.0,hockey,0,0,0 -331103388,Carolena Carstens,PAN,female,1996-01-18,1.68,57.0,taekwondo,0,0,0 -622673655,Carolin Golubytskyi,GER,female,1985-12-19,1.67,57.0,fencing,0,0,0 -751199209,Carolin Schafer,GER,female,1991-12-05,1.78,65.0,athletics,0,0,0 -999509819,Carolina Aguirre,COL,female,1996-01-29,1.6,59.0,archery,0,0,0 -809071082,Carolina Arbelaez,COL,female,1995-03-08,1.61,55.0,football,0,0,0 -467587531,Carolina Arias,COL,female,1990-09-02,1.59,52.0,football,0,0,0 -552675291,Carolina Castillo Hidalgo,COL,female,1990-11-04,1.57,48.0,wrestling,0,0,0 -905750537,Carolina Marin,ESP,female,1993-06-15,1.72,65.0,badminton,1,0,0 -362193279,Carolina Rodriguez,ESP,female,1986-05-24,1.64,48.0,gymnastics,0,0,0 -275280990,Carolina Rodriguez Gutierrez,MEX,female,1993-09-30,1.63,80.0,cycling,0,0,0 -488698668,Carolina Routier,ESP,female,1990-04-23,1.72,60.0,triathlon,0,0,0 -763290090,Carolina Werner,GER,female,1994-03-02,1.68,58.0,sailing,0,0,0 -237276170,Caroline Buchanan,AUS,female,1990-10-24,1.65,68.0,cycling,0,0,0 -618435469,Caroline Garcia,FRA,female,1993-10-16,1.77,61.0,tennis,0,0,0 -8177230,Caroline Kumahara,BRA,female,1995-07-27,1.66,58.0,table tennis,0,0,0 -965494911,Caroline Ladagnous,FRA,female,1988-09-22,1.72,65.0,rugby sevens,0,0,0 -454726580,Caroline Marton,AUS,female,1984-04-14,1.68,56.0,taekwondo,0,0,0 -378248537,Caroline Masson,GER,female,1989-05-14,1.73,72.0,golf,0,0,0 -479238224,Caroline Seger,SWE,female,1985-03-19,1.73,62.0,football,0,1,0 -53417800,Caroline Wozniacki,DEN,female,1990-07-11,1.79,65.0,tennis,0,0,0 -396948755,Carolle Zahi,FRA,female,1994-06-12,1.7,66.0,athletics,0,0,0 -633400313,Carrie Smith,AUS,female,1995-01-28,1.65,55.0,sailing,0,0,0 -37516553,Carsten Mogensen,DEN,male,1983-07-24,1.88,75.0,badminton,0,0,0 -944589858,Carvin Nkanata,KEN,male,1991-05-06,,,athletics,0,0,0 -666382963,Casey Dumont,AUS,female,1992-01-25,1.8,82.0,football,0,0,0 -899383164,Casey Eichfeld,USA,male,1989-11-15,1.78,77.0,canoe,0,0,0 -472505034,Casey Patterson,USA,male,1980-04-20,1.99,88.0,volleyball,0,0,0 -503788455,Casey Sablowski,AUS,female,1989-03-19,1.7,62.0,hockey,0,0,0 -450608241,Casper Mortensen,DEN,male,1989-12-14,1.9,88.0,handball,1,0,0 -452251425,Casper Nielsen,DEN,male,1994-04-29,1.8,70.0,football,0,0,0 -583629384,Casper Pedersen,DEN,male,1996-03-15,1.86,74.0,cycling,0,0,0 -579676651,Casper von Folsach,DEN,male,1993-03-30,1.91,82.0,cycling,0,0,1 -289009980,Cassandre Beaugrand,FRA,female,1997-05-23,1.77,54.0,triathlon,0,0,0 -105404448,Cassio Cesar Rippel,BRA,male,1978-05-02,1.93,95.0,shooting,0,0,0 -858551569,Cassio Rivetti,UKR,male,1980-02-20,1.75,65.0,equestrian,0,0,0 -190873405,Caster Semenya,RSA,female,1991-01-07,1.78,70.0,athletics,1,0,0 -654622452,Catalina Elena Escobar Gomez,COL,female,1990-09-21,1.56,50.0,gymnastics,0,0,0 -768142063,Catalina Perez,COL,female,1994-11-08,1.74,60.0,football,0,0,0 -943865464,Catalina Ponor,ROU,female,1987-08-20,1.56,47.0,gymnastics,0,0,0 -885026394,Catalina Usme,COL,female,1989-12-25,1.68,67.0,football,0,0,0 -468505846,Cate Campbell,AUS,female,1992-05-20,1.86,74.0,aquatics,1,1,0 -576401005,Caterine Ibarguen,COL,female,1984-02-12,1.85,70.0,athletics,1,0,0 -985557149,Catharine Pendrel,CAN,female,1980-09-30,1.66,50.0,cycling,0,0,1 -638981587,Catherine Abilla,KEN,female,1989-04-01,1.62,56.0,rugby sevens,0,0,0 -974386617,Catherine Beauchemin-Pinard,CAN,female,1994-06-26,1.61,57.0,judo,0,0,0 -315097608,Catherine Bertone,ITA,female,1972-05-06,1.6,45.0,athletics,0,0,0 -578087972,Catherine Bott,NZL,female,1995-04-22,1.65,62.0,football,0,0,0 -62565482,Catherine Meili,USA,female,1991-04-16,1.71,61.0,aquatics,1,0,1 -567465355,Catherine Skinner,AUS,female,1990-02-11,1.72,80.0,shooting,1,0,0 -761809056,Cathrine Dufour,DEN,female,1992-01-02,1.7,68.0,equestrian,0,0,0 -980333381,Catia Azevedo,POR,female,1994-03-09,1.7,50.0,athletics,0,0,0 -187632862,Catriel Andres Soto,ARG,male,1987-04-29,1.75,68.0,cycling,0,0,0 -255503629,Catriona Matthew,GBR,female,1969-08-25,1.63,60.0,golf,0,0,0 -694774919,Cayla George,AUS,female,1989-05-01,1.92,90.0,basketball,0,0,0 -71279109,Caylee Watson,ISV,female,1994-10-10,,,aquatics,0,0,0 -999104659,Cazuo Matsumoto,BRA,male,1985-08-02,1.8,88.0,table tennis,0,0,0 -869798237,Cecil Afrika,RSA,male,1988-03-03,1.75,75.0,rugby sevens,0,0,1 -558867540,Cecile Pieper,GER,female,1994-08-31,1.66,56.0,hockey,0,0,1 -369221148,Cecilia Berder,FRA,female,1989-12-13,1.74,65.0,fencing,0,0,0 -728202401,Cecilia Bouele,CGO,female,1993-01-07,1.66,60.0,athletics,0,0,0 -721352055,Cecilia Carranza Saroli,ARG,female,1986-12-29,1.64,63.0,sailing,1,0,0 -333612989,Cecilia Perez,MEX,female,1991-11-01,1.65,53.0,triathlon,0,0,0 -958457739,Cecilia Wollmann,BER,female,1998-01-23,1.64,60.0,sailing,0,0,0 -947360474,Cedric Charlier,BEL,male,1987-11-27,1.81,81.0,hockey,0,1,0 -82592097,Cedric Dubler,AUS,male,1995-01-13,,,athletics,0,0,0 -408751160,Cedric Sorhaindo,FRA,male,1984-06-07,1.92,110.0,handball,0,1,0 -437255555,Ceiber David Avila,COL,male,1989-05-26,1.63,,boxing,0,0,0 -245970037,Cejhae Greene,ANT,male,1995-10-06,1.74,72.0,athletics,0,0,0 -205420967,Celeste Plak,NED,female,1995-10-26,1.9,90.0,volleyball,0,0,0 -388140867,Celestin Nihorimbere,BDI,male,1993-01-11,1.67,55.0,athletics,0,0,0 -537701944,Celestine Masinde,KEN,female,1987-01-12,1.62,60.0,rugby sevens,0,0,0 -636512936,Celiangeli Morales,PUR,female,1985-11-02,1.65,63.0,athletics,0,0,0 -978104747,Celine Distel-Bonnet,FRA,female,1987-05-25,1.7,59.0,athletics,0,0,0 -582673345,Celine Goberville,FRA,female,1986-09-19,1.56,53.0,shooting,0,0,0 -290030374,Celio Dias,POR,male,1993-02-08,1.88,90.0,judo,0,0,0 -206341910,Celma Bonfim da Graca,STP,female,1977-12-23,1.65,58.0,athletics,0,0,0 -291597305,Celso Oliveira,BRA,male,1988-10-28,1.88,88.0,canoe,0,0,0 -991308199,Celtus Williams Abiola Dossou Yovo,BEN,male,1986-04-01,1.75,89.0,judo,0,0,0 -69938071,Cem Yilmaz,TUR,male,1982-06-03,1.8,70.0,rowing,0,0,0 -300985690,Cenk Ildem,TUR,male,1986-01-05,1.8,98.0,wrestling,0,0,1 -435408046,Cesar Augusto Almeida,BRA,male,1989-01-06,1.88,98.0,handball,0,0,0 -518609220,Cesar Castro,BRA,male,1982-09-02,1.75,73.0,aquatics,0,0,0 -454137543,Cesar Ernesto de Cesare,ECU,male,1980-07-12,1.9,92.0,canoe,0,0,0 -15259050,Cesar Marcano,VEN,male,1987-10-22,1.77,76.0,cycling,0,0,0 -427211622,Cesar Montes,MEX,male,1997-02-24,1.91,70.0,football,0,0,0 -794568410,Cesar Sempere,ESP,male,1984-05-26,1.82,86.0,rugby sevens,0,0,0 -844330987,Cesar Y. Ruiz,CUB,male,1995-01-18,1.84,77.0,athletics,0,0,0 -51787706,Chad Guy Bertrand le Clos,RSA,male,1992-04-12,1.9,83.0,aquatics,0,2,0 -797308674,Chad Ho,RSA,male,1990-06-21,1.72,75.0,aquatics,0,0,0 -406780652,Chaebin Im,KOR,male,1991-10-29,1.69,75.0,cycling,0,0,0 -300475041,Chafik Bouaoud,ALG,male,1999-02-12,1.77,74.0,shooting,0,0,0 -388718541,Chagnaadorj Usukhbayar,MGL,male,1997-05-06,1.55,56.0,weightlifting,0,0,0 -465407360,Chahinez Nasri,TUN,female,1996-06-03,1.68,51.0,athletics,0,0,0 -355057478,Chaim Schalk,CAN,male,1986-04-23,1.95,88.0,volleyball,0,0,0 -143002811,Chain Singh,IND,male,1989-04-05,1.72,68.0,shooting,0,0,0 -845845123,Chakir Ansari,MAR,male,1991-06-22,1.68,57.0,wrestling,0,0,0 -738060846,Chala Beyo,ETH,male,1996-01-18,1.76,58.0,athletics,0,0,0 -175009938,Chamara Repiyallage,SRI,male,1992-05-10,1.6,73.0,judo,0,0,0 -278008849,Chamberlain Oguchi,NGR,male,1986-04-28,1.98,90.0,basketball,0,0,0 -723338672,Chandanda Thimmaiah,IND,male,1991-01-18,1.71,61.0,hockey,0,0,0 -974332557,Chandong Lee,KOR,male,1993-01-10,1.83,83.0,football,0,0,0 -523414424,Changgeun Lee,KOR,male,1993-08-30,,,football,0,0,0 -328876180,Changhoon Kwon,KOR,male,1994-06-30,1.74,66.0,football,0,0,0 -981134521,Changjin Moon,KOR,male,1993-07-12,1.7,63.0,football,0,0,0 -988404471,Changju Kim,KOR,male,1985-09-20,1.78,69.0,sailing,0,0,0 -134774857,Changmin Lee,KOR,male,1994-01-20,1.78,74.0,football,0,0,0 -200603322,Changning Zhang,CHN,female,1995-11-06,1.93,79.0,volleyball,1,0,0 -965944116,Changrim An,KOR,male,1994-03-02,1.7,73.0,judo,0,0,0 -428792415,Changrui Xue,CHN,male,1991-05-31,1.88,75.0,athletics,0,0,0 -278225437,Changzhou Huang,CHN,male,1994-08-20,1.86,73.0,athletics,0,0,0 -227590205,Chanice Chase,CAN,female,1993-08-06,1.73,61.0,athletics,0,0,0 -66906563,Chantal Achterberg,NED,female,1985-04-16,1.72,72.0,rowing,0,1,0 -25695000,Chantal Hoffmann,LUX,female,1987-10-30,1.67,62.0,cycling,0,0,0 -543504980,Chantal Van Landeghem,CAN,female,1994-03-05,1.8,77.0,aquatics,0,0,1 -797864870,Chantelle Esau,RSA,female,1990-12-14,1.63,52.0,football,0,0,0 -327647564,Chanu Saikhom Mirabai,IND,female,1994-08-08,1.45,48.0,weightlifting,0,0,0 -912660957,Chao He,CHN,male,1992-02-11,1.68,67.0,aquatics,0,0,0 -89640785,Chao Xu,CHN,male,1994-11-05,1.85,82.0,cycling,0,0,0 -248093384,Chao Yue,CHN,female,1991-01-05,1.65,50.0,athletics,0,0,0 -176787590,Chao Zhou,CHN,female,1987-01-12,1.7,70.0,judo,0,0,0 -516647126,Chaopan Lin,CHN,male,1995-08-27,1.63,60.0,gymnastics,0,0,1 -569192989,Charity Williams,CAN,female,1996-10-20,1.62,68.0,rugby sevens,0,0,1 -957327519,Charlene Woitha,GER,female,1993-08-21,1.78,77.0,athletics,0,0,0 -792508512,Charles Albert Shone Conwell,USA,male,1997-11-02,1.76,,boxing,0,0,0 -624227669,Charles Chibana,BRA,male,1989-09-11,1.63,66.0,judo,0,0,0 -32540693,Charles Cole,USA,male,1986-06-21,1.94,90.0,rowing,0,0,0 -607494288,Charles Correa,BRA,male,1992-10-09,1.59,58.0,canoe,0,0,0 -91062898,Charles Fernandez,GUA,male,1995-12-28,1.81,74.0,modern pentathlon,0,0,0 -557871221,Charles Grethen,LUX,male,1992-06-02,1.8,68.0,athletics,0,0,0 -15332817,Charles Jock,USA,male,1989-11-23,1.91,74.0,athletics,0,0,0 -262046958,Charles Kahudi,FRA,male,1986-07-19,1.97,104.0,basketball,0,0,0 -935795662,Charles Philibert-Thiboutot,CAN,male,1990-12-31,1.82,63.0,athletics,0,0,0 -763413652,Charles Yosei Muneria,KEN,male,1996-02-10,1.67,55.0,athletics,0,0,0 -762626712,Charley Hull,GBR,female,1996-03-20,1.7,70.0,golf,0,0,0 -373271527,Charlie Buckingham,USA,male,1989-01-16,1.88,81.0,sailing,0,0,0 -617780689,Charlie Grice,GBR,male,1993-11-07,1.82,70.0,athletics,0,0,0 -932285565,Charline Mathias,LUX,female,1992-05-23,1.73,60.0,athletics,0,0,0 -889819769,Charline Picon,FRA,female,1984-12-23,1.69,57.0,sailing,1,0,0 -477185472,Charline van Snick,BEL,female,1990-09-02,1.57,48.0,judo,0,0,0 -647849033,Charlotta Fougberg,SWE,female,1985-06-19,1.65,51.0,athletics,0,0,0 -445362383,Charlotte Becker,GER,female,1983-05-19,1.73,64.0,cycling,0,0,0 -721017208,Charlotte Bonin,ITA,female,1987-02-10,1.73,60.0,triathlon,0,0,0 -403574731,Charlotte Bonnet,FRA,female,1995-02-14,1.75,64.0,aquatics,0,0,0 -865933496,Charlotte Caslick,AUS,female,1995-03-09,1.72,65.0,rugby sevens,1,0,0 -172842725,Charlotte Dobson,GBR,female,1986-06-05,1.68,62.0,sailing,0,0,0 -118964304,Charlotte Dujardin,GBR,female,1985-07-12,1.7,57.0,equestrian,1,1,0 -833774216,Charlotte Harrison,NZL,female,1989-07-31,1.67,55.0,hockey,0,0,0 -841791715,Charlotte Lembach,FRA,female,1988-04-01,1.64,57.0,fencing,0,0,0 -634284739,Charlotte Mordasini,SWE,female,1988-12-31,1.71,60.0,equestrian,0,0,0 -996536701,Charlotte Stapenhorst,GER,female,1995-06-15,1.68,68.0,hockey,0,0,1 -837449921,Charlotte Sutherland,AUS,female,1991-06-26,1.8,76.0,rowing,0,0,0 -892102101,Charlotte Taylor,GBR,female,1985-08-14,1.65,57.0,rowing,0,0,0 -660698958,Charlotte Wingfield,MLT,female,1994-11-30,1.73,69.0,athletics,0,0,0 -534915497,Charly Coronel Suarez,PHI,male,1988-08-14,1.68,,boxing,0,0,0 -280839325,Chase Kalisz,USA,male,1994-03-07,1.94,86.0,aquatics,0,1,0 -172605040,Chatchai Butdee,THA,male,1985-03-26,1.66,,boxing,0,0,0 -851008982,Chatuphum Chinnawong,THA,male,1993-07-19,1.67,77.0,weightlifting,0,0,0 -714871025,Chaunte Lowe,USA,female,1984-01-12,1.76,59.0,athletics,0,0,0 -680189509,Chavaughn Walsh,ANT,male,1987-12-29,1.8,72.0,athletics,0,0,0 -284105289,Cheick Sallah Junior Cisse,CIV,male,1993-09-19,1.9,,taekwondo,1,0,0 -105081370,Chelsea Jaensch,AUS,female,1985-01-06,1.63,56.0,athletics,0,0,0 -638380672,Chelsea Lea Gubecka,AUS,female,1998-09-08,1.62,62.0,aquatics,0,0,0 -774700015,Chen Gao,CHN,female,1992-08-11,1.7,58.0,football,0,0,0 -355354758,Chen Shen,CHN,female,1990-07-28,1.71,62.0,fencing,0,0,0 -332886320,Chen Xu,CHN,male,1984-11-29,1.88,82.0,badminton,0,0,0 -886188106,Chen-Ling Lien,TPE,female,1988-01-31,1.68,57.0,judo,0,0,0 -247619979,Chenggang Yu,CHN,male,1984-02-23,1.81,72.0,rowing,0,0,0 -220688472,Chenglong Zhang,CHN,male,1989-05-12,1.73,65.0,gymnastics,0,0,1 -831305339,Chengxin Yin,CHN,female,1995-02-05,1.7,55.0,aquatics,0,1,0 -397297354,Chenlu Qin,CHN,male,1992-10-24,1.75,70.0,cycling,0,0,0 -98995708,Cherif Younousse Samba,QAT,male,1995-05-22,1.94,77.0,volleyball,0,0,0 -543666131,Cheslin Kolbe,RSA,male,1993-10-28,1.79,89.0,rugby sevens,0,0,1 -717003685,Chi-Chung Tan,TPE,male,1990-02-24,1.55,56.0,weightlifting,0,0,0 -116299624,Chia Hsin Tsai,TPE,male,1982-07-25,1.82,82.0,badminton,0,0,0 -36584251,Chia Ying Wu,TPE,female,1992-10-25,1.55,47.0,shooting,0,0,0 -469947705,Chia-Chia Chuang,TPE,female,1989-05-14,1.79,65.0,taekwondo,0,0,0 -480858629,Chiaki Tomita,JPN,female,1993-10-18,1.65,55.0,rowing,0,0,0 -148995739,Chiara Bazzoni,ITA,female,1984-07-05,1.72,58.0,athletics,0,0,0 -454523212,Chiara Cainero,ITA,female,1978-03-24,1.71,81.0,shooting,0,1,0 -875043196,Chiara Masini Luccetti,ITA,female,1993-03-26,1.71,63.0,aquatics,0,0,0 -781396990,Chiara Tabani,ITA,female,1994-08-27,1.76,72.0,aquatics,0,1,0 -430952782,Chico Ramos,POR,male,1995-04-10,1.85,80.0,football,0,0,0 -464650816,Chido Dzingirai,ZIM,female,1991-10-25,1.72,69.0,football,0,0,0 -294878524,Chieh Chen,TPE,male,1992-05-08,1.82,73.0,athletics,0,0,0 -930498029,Chien-An Chen,TPE,male,1991-06-16,1.7,72.0,table tennis,0,0,0 -769049333,Chien-Ho Hsieh,TPE,female,1987-11-25,1.64,48.0,athletics,0,0,0 -296026753,Chien-Hung Pan,TPE,male,1988-08-07,1.69,69.0,weightlifting,0,0,0 -846139203,Chien-Ying Le,TPE,female,1990-04-17,1.65,69.0,archery,0,0,1 -404278296,Chierika Ukogu,NGR,female,1992-10-02,1.8,67.0,rowing,0,0,0 -558527294,Chih Kai Lee,TPE,male,1996-04-03,1.7,65.0,gymnastics,0,0,0 -141719705,Chih-Yuan Chuang,TPE,male,1981-04-02,1.68,60.0,table tennis,0,0,0 -251378331,Chiharu Nakamura,JPN,female,1988-04-25,1.63,63.0,rugby sevens,0,0,0 -32395502,Chihiro Igarashi,JPN,female,1995-05-24,1.7,63.0,aquatics,0,0,0 -802100153,Chijindu Ujah,GBR,male,1994-03-05,1.82,81.0,athletics,0,0,0 -2657574,Chika Aoki,JPN,female,1990-02-21,1.58,54.0,fencing,0,0,0 -489705846,Chilsung Park,KOR,male,1982-07-08,1.73,67.0,athletics,0,0,0 -520588728,Chin-Ping Ho,TPE,male,1983-10-23,1.73,60.0,athletics,0,0,0 -475891314,Chinglensana Kangujam,IND,male,1991-12-02,1.69,72.0,hockey,0,0,0 -697417425,Chinwe Okoro,NGR,female,1989-06-20,1.78,102.0,athletics,0,0,0 -885083820,Chinzorig Baatarsukh,MGL,male,1991-09-21,1.74,,boxing,0,0,0 -775422570,Chirine Njeim,LIB,female,1984-10-04,,,athletics,0,0,0 -781340794,Chisato Fukushima,JPN,female,1988-06-27,1.65,52.0,athletics,0,0,0 -798175500,Chisato Yokoo,JPN,female,1992-05-22,1.64,60.0,rugby sevens,0,0,0 -912156686,Chiu Mang Tang,HKG,male,1990-07-24,1.8,72.0,rowing,0,0,0 -347216262,Chloe Bulleux,FRA,female,1991-11-18,1.72,65.0,handball,0,1,0 -247836901,Chloe Dalton,AUS,female,1993-07-11,1.8,72.0,rugby sevens,1,0,0 -908812309,Chloe Dygert,USA,female,1997-01-01,1.76,66.0,cycling,0,1,0 -66098527,Chloe Esposito,AUS,female,1991-09-19,1.68,55.0,modern pentathlon,1,0,0 -792650052,Chloe Leurquin,BEL,female,1990-08-09,1.69,57.0,golf,0,0,0 -315954176,Chloe Logarzo,AUS,female,1994-12-22,1.65,55.0,football,0,0,0 -819853212,Chloe Magee,IRL,female,1988-11-29,1.65,61.0,badminton,0,0,0 -622470385,Chloe Marie Helene Sauvourel,CAF,female,1999-06-18,1.66,68.0,aquatics,0,0,0 -838794586,Chloe Rayner,AUS,female,1996-09-18,1.55,48.0,judo,0,0,0 -308484063,Chloe Tipple,NZL,female,1991-06-05,1.78,80.0,shooting,0,0,0 -150543887,Chloe Tutton,GBR,female,1996-07-17,1.68,62.0,aquatics,0,0,0 -87537613,Chloe Woodruff,USA,female,1987-07-21,1.58,51.0,cycling,0,0,0 -993806329,Choaib Belhaj Salah,TUN,male,1987-06-04,1.94,93.0,volleyball,0,0,0 -310773041,Chol Pak,PRK,male,1990-11-08,1.65,53.0,athletics,0,0,0 -609981962,Chong Wei Lee,MAS,male,1982-10-21,1.7,68.0,badminton,0,1,0 -82546289,Chouaib Bouloudinats,ALG,male,1987-01-08,1.82,,boxing,0,0,0 -93555495,Chris Adcock,GBR,male,1989-04-27,1.83,80.0,badminton,0,0,0 -518947875,Chris Anker Sorensen,DEN,male,1984-09-05,1.84,65.0,cycling,0,0,0 -889760541,Chris Baker,GBR,male,1991-02-02,1.94,80.0,athletics,0,0,0 -123795777,Chris Bartley,GBR,male,1984-02-02,1.78,72.0,rowing,0,0,0 -381231123,Chris Benard,USA,male,1990-04-09,1.91,83.0,athletics,0,0,0 -629312899,Chris Bennett,GBR,male,1989-12-17,1.88,115.0,athletics,0,0,0 -148030859,Chris Brown,BAH,male,1978-10-15,1.78,75.0,athletics,0,0,1 -719858003,Chris Ciriello,AUS,male,1985-10-01,1.82,83.0,hockey,0,0,0 -784067568,Chris Erickson,AUS,male,1981-12-01,1.75,60.0,athletics,0,0,0 -572725066,Chris Goulding,AUS,male,1988-10-24,1.92,93.0,basketball,0,0,0 -277029308,Chris Grube,GBR,male,1985-01-22,1.94,72.0,sailing,0,0,0 -110660329,Chris Langridge,GBR,male,1985-05-02,1.8,,badminton,0,0,1 -628001010,Chris Mears,GBR,male,1993-02-07,1.73,73.0,aquatics,1,0,0 -182303154,Chris O'Hare,GBR,male,1990-11-23,1.78,63.0,athletics,0,0,0 -150373801,Chris Walker-Hebborn,GBR,male,1990-07-01,1.84,84.0,aquatics,0,1,0 -261887027,Chris Winter,CAN,male,1986-07-22,1.88,75.0,athletics,0,0,0 -491144467,Chris Wyles,USA,male,1983-09-13,1.83,95.0,rugby sevens,0,0,0 -187059251,Chrisann Gordon,JAM,female,1994-09-18,1.68,65.0,athletics,0,1,0 -123893495,Chrishuna Williams,USA,female,1993-03-31,1.61,54.0,athletics,0,0,0 -261434157,Christa Harmotto Dietzen,USA,female,1986-10-12,1.88,79.0,volleyball,0,0,1 -961185789,Christabel Nettey,CAN,female,1991-06-02,1.62,57.0,athletics,0,0,0 -112339974,Christania Williams,JAM,female,1994-10-17,1.65,63.0,athletics,0,1,0 -114831351,Christelle Daunay,FRA,female,1974-12-05,1.62,43.0,athletics,0,0,0 -572557268,Christelle Tchoudjang Nana,CMR,female,1989-07-07,1.84,80.0,volleyball,0,0,0 -900988313,Christen Press,USA,female,1988-12-29,1.7,60.0,football,0,0,0 -783674289,Christiaan Varenhorst,NED,male,1990-05-06,2.11,104.0,volleyball,0,0,0 -983312756,Christian Ahlmann,GER,male,1974-12-17,1.89,80.0,equestrian,0,0,1 -101327235,Christian Amoah,GHA,male,1999-07-25,1.84,84.0,weightlifting,0,0,0 -390828908,Christian Baumann,SUI,male,1995-02-25,1.63,60.0,gymnastics,0,0,0 -871551515,Christian Coleman,USA,male,1996-03-06,1.76,72.0,athletics,0,0,0 -798416693,Christian Diener,GER,male,1993-06-03,1.82,82.0,aquatics,0,0,0 -869931216,Christian Dissinger,GER,male,1991-11-15,2.03,105.0,handball,0,0,1 -429253838,Christian Guenter,GER,male,1993-02-28,1.84,82.0,football,0,0,0 -621658753,Christian Kreienbuhl,SUI,male,1981-06-06,1.86,68.0,athletics,0,0,0 -461859304,Christian Mbilli,FRA,male,1995-04-26,1.75,,boxing,0,0,0 -530803618,Christian Nassif Djidagui,CAF,male,1994-01-01,,,aquatics,0,0,0 -49854896,Christian Peter Lubeck,DEN,male,1991-04-23,1.86,81.0,sailing,0,0,0 -839986218,Christian Presciutti,ITA,male,1982-11-27,1.84,87.0,aquatics,0,0,1 -150989629,Christian Reichert,GER,male,1985-02-07,1.88,83.0,aquatics,0,0,0 -513022644,Christian Reitz,GER,male,1987-04-29,1.84,90.0,shooting,1,0,0 -446986047,Christian Taylor,USA,male,1990-06-18,1.88,81.0,athletics,1,0,0 -607156497,Christian Vom Lehn,GER,male,1992-04-14,1.9,78.0,aquatics,0,0,0 -45955981,Christian Zillekens,GER,male,1995-12-29,1.79,68.0,modern pentathlon,0,0,0 -511915390,Christian Zimmermann,PLE,male,1961-12-12,,,equestrian,0,0,0 -309587083,Christianne Legentil,MRI,female,1992-05-27,1.53,52.0,judo,0,0,0 -23725637,Christin Hussong,GER,female,1994-03-17,1.86,84.0,athletics,0,0,0 -748125644,Christina Epps,USA,female,1991-06-20,1.76,65.0,athletics,0,0,0 -646217322,Christina Hering,GER,female,1994-10-09,1.85,63.0,athletics,0,0,0 -532991563,Christina Obergfoll,GER,female,1981-08-22,1.75,79.0,athletics,0,0,0 -802053321,Christina Schwanitz,GER,female,1985-12-24,1.8,115.0,athletics,0,0,0 -544811537,Christine Amertil,BAH,female,1979-08-18,1.68,54.0,athletics,0,0,0 -304380235,Christine Bjerendal,SWE,female,1987-02-03,1.61,59.0,archery,0,0,0 -524363491,Christine Botlogetswe,BOT,female,1995-10-01,,,athletics,0,0,0 -45076354,Christine Day,JAM,female,1986-08-23,1.68,51.0,athletics,0,1,0 -774219408,Christine Kalmer,RSA,female,1986-02-10,1.72,55.0,athletics,0,0,0 -348981852,Christine Majerus,LUX,female,1987-02-25,1.74,57.0,cycling,0,0,0 -600192107,Christine Ohuruogu,GBR,female,1984-05-17,1.72,70.0,athletics,0,0,1 -141698796,Christine Roper,CAN,female,1990-05-15,1.88,90.0,rowing,0,0,0 -250723812,Christine Sinclair,CAN,female,1983-06-12,1.76,68.0,football,0,0,1 -514854270,Christine Wenzel,GER,female,1981-07-10,1.71,63.0,shooting,0,0,0 -996180184,Christine Wolf,AUT,female,1989-03-05,1.72,63.0,golf,0,0,0 -121437333,Christinna Pedersen,DEN,female,1986-05-12,1.78,68.0,badminton,0,1,0 -971332873,Christodoulos Kolomvos,GRE,male,1988-10-26,1.86,103.0,aquatics,0,0,0 -85448954,Christoph Fildebrandt,GER,male,1989-05-27,1.93,80.0,aquatics,0,0,0 -501861079,Christoph Harting,GER,male,1990-04-10,2.07,123.0,athletics,1,0,0 -731592178,Christoph Martin Meier,LIE,male,1993-01-03,1.97,95.0,aquatics,0,0,0 -134449536,Christophe Lemaitre,FRA,male,1990-06-11,1.9,74.0,athletics,0,0,1 -447803085,Christophen McPHERSON,BRA,male,1984-06-19,1.85,77.0,hockey,0,0,0 -822832512,Christopher Brooks,USA,male,1986-12-19,1.73,74.0,gymnastics,0,0,0 -628857615,Christopher Burton,AUS,male,1981-11-22,1.8,70.0,equestrian,0,0,1 -401021506,Christopher Cargo,IRL,male,1986-02-18,1.82,79.0,hockey,0,0,0 -795060269,Christopher Froome,GBR,male,1985-05-20,1.85,69.0,cycling,0,0,1 -804679484,Christopher George,TTO,male,1983-12-25,,,judo,0,0,0 -489215473,Christopher Guccione,AUS,male,1985-07-30,2.0,91.0,tennis,0,0,0 -777119491,Christopher Harris,NZL,male,1985-10-19,1.87,89.0,rowing,0,0,0 -488320135,Christopher Juul Jensen,DEN,male,1989-07-06,1.85,75.0,cycling,0,0,0 -515467110,Christopher Linke,GER,male,1988-10-24,1.91,66.0,athletics,0,0,0 -375356120,Christopher Morgan,AUS,male,1982-12-15,1.91,90.0,rowing,0,0,0 -893971715,Christopher Patrick Reid,RSA,male,1996-01-10,1.98,98.0,aquatics,0,0,0 -390068721,Christopher Ruhr,GER,male,1993-12-19,1.8,77.0,hockey,0,0,1 -672777082,Christopher Valdez,DOM,male,1994-11-01,1.7,63.0,athletics,0,0,0 -741375117,Christopher Wesley,GER,male,1987-06-23,1.92,88.0,hockey,0,0,1 -491621394,Christos Afroudakis,GRE,male,1984-05-23,1.88,88.0,aquatics,0,0,0 -178446035,Christos Katrantzis,GRE,male,1992-03-30,1.88,78.0,aquatics,0,0,0 -416304981,Christos Volikakis,GRE,male,1988-03-25,1.7,85.0,cycling,0,0,0 -141489782,Chu-En Lai,TPE,male,1996-07-23,1.68,,boxing,0,0,0 -82062785,Chuluunbat Jargalsaikhan,MGL,male,1984-12-03,1.84,125.0,wrestling,0,0,0 -136590128,Chun Hei Reginald Lee,HKG,male,1994-01-25,1.7,72.0,badminton,0,0,0 -160814814,Chun Hing Chan,HKG,male,1981-04-24,1.7,70.0,cycling,0,0,0 -546606814,Chun Leung Michael Cheng,HKG,male,1994-04-30,1.82,74.0,sailing,0,0,0 -890198247,Chun Ting Wong,HKG,male,1991-09-07,1.7,70.0,table tennis,0,0,0 -932296367,Chun Wing Leung,HKG,male,1994-01-20,1.76,73.0,cycling,0,0,0 -361473378,Chun-Heng Wei,TPE,male,1994-07-06,1.8,74.0,archery,0,0,0 -972398061,Chun-Hsien Hsiang,TPE,male,1993-09-04,1.86,70.0,athletics,0,0,0 -74882200,Chunsong Shang,CHN,female,1996-03-18,1.43,34.0,gymnastics,0,0,1 -184720791,Chunxin Wang,CHN,male,1997-11-25,1.8,70.0,rowing,0,0,0 -748411041,Chunyu Wang,CHN,female,1995-01-17,1.75,55.0,athletics,0,0,0 -434268598,Churandy Martina,NED,male,1984-07-03,1.8,76.0,athletics,0,0,0 -364006539,Ciara Everard,IRL,female,1990-07-10,1.69,54.0,athletics,0,0,0 -784773877,Ciara Horne,GBR,female,1989-09-17,1.79,63.0,cycling,0,0,0 -425071947,Ciara Mageean,IRL,female,1992-03-12,1.7,55.0,athletics,0,0,0 -786574595,Cierra Runge,USA,female,1996-03-07,1.94,84.0,aquatics,1,0,0 -269551949,Cindy Billaud,FRA,female,1986-03-11,1.67,59.0,athletics,0,0,0 -808281174,Cindy Ofili,GBR,female,1994-08-05,1.78,68.0,athletics,0,0,0 -348165944,Cindy Roleder,GER,female,1989-08-21,1.78,70.0,athletics,0,0,0 -810690969,Ciril Grossklaus,SUI,male,1991-04-17,1.85,90.0,judo,0,0,0 -768959560,Cisiane Lopes,BRA,female,1983-02-17,1.59,49.0,athletics,0,0,0 -848614623,Claire Allan,GBR,female,1985-05-07,1.7,65.0,rugby sevens,0,0,0 -511358309,Claire Lambe,IRL,female,1990-05-16,1.78,57.0,rowing,0,0,0 -815034554,Claire Lavogez,FRA,female,1994-06-18,1.73,63.0,football,0,0,0 -516245173,Claire Michel,BEL,female,1988-10-13,1.68,54.0,triathlon,0,0,0 -716197496,Clara Espar Llaquet,ESP,female,1994-09-29,1.78,68.0,aquatics,0,0,0 -52347584,Clare Abbott,IRL,female,1986-07-28,1.67,58.0,equestrian,0,0,0 -317879815,Clare Polkinghorne,AUS,female,1989-02-01,1.73,67.0,football,0,0,0 -805523212,Clarence Munyai,RSA,male,1998-02-20,1.7,65.0,athletics,0,0,0 -175558091,Claressa Maria Shields,USA,female,1995-03-17,1.76,,boxing,1,0,0 -161104873,Clarisa Sagardia,ARG,female,1989-06-29,1.74,67.0,volleyball,0,0,0 -722629452,Clarissa Santos,BRA,female,1988-03-10,1.83,89.0,basketball,0,0,0 -610018581,Clarisse Agbegnenou,FRA,female,1992-10-25,1.64,66.0,judo,0,1,0 -989657937,Clarisse le Bihan,FRA,female,1994-12-14,1.72,64.0,football,0,0,0 -292197619,Clark Montgomery,USA,male,1981-05-20,1.83,70.0,equestrian,0,0,0 -736895804,Clark Smith,USA,male,1995-04-17,2.06,89.0,aquatics,1,0,0 -447342515,Clarke Johnstone,NZL,male,1987-04-26,1.94,80.0,equestrian,0,0,0 -792920244,Claudette Mukasakindi,RWA,female,1982-12-25,1.6,68.0,athletics,0,0,0 -772469777,Claudia Belderbos,NED,female,1985-01-23,1.75,72.0,rowing,0,0,0 -425434912,Claudia Bobocea,ROU,female,1992-06-11,1.76,53.0,athletics,0,0,0 -950213745,Claudia Cesarini,ITA,female,1986-08-04,1.77,60.0,modern pentathlon,0,0,0 -351914680,Claudia Fragapane,GBR,female,1997-10-24,1.37,43.0,gymnastics,0,0,0 -262947031,Claudia Jaqueline Teles,BRA,female,1992-01-02,1.66,57.0,rugby sevens,0,0,0 -130234491,Claudia Lau,HKG,female,1992-11-11,1.6,53.0,aquatics,0,0,0 -695148064,Claudia Lichtenberg,GER,female,1985-11-17,1.7,51.0,cycling,0,0,0 -688060472,Claudia Mandia,ITA,female,1992-10-21,1.7,74.0,archery,0,0,0 -132539493,Claudia Rath,GER,female,1986-04-25,1.75,65.0,athletics,0,0,0 -108261189,Claudia Rivas,MEX,female,1989-06-15,1.71,56.0,triathlon,0,0,0 -462976229,Claudia Stef,ROU,female,1978-02-25,1.7,57.0,athletics,0,0,0 -286903708,Claudio Castilla Ruiz,ESP,male,1983-05-30,1.73,66.0,equestrian,0,0,0 -222024929,Claudio Villanueva,ECU,male,1988-08-03,1.75,68.0,athletics,0,0,0 -952767996,Clayton Murphy,USA,male,1995-02-26,1.81,68.0,athletics,0,0,1 -692043872,Clelia Reuse-Rard,SUI,female,1988-08-01,1.7,57.0,athletics,0,0,0 -622017902,Clemens Doppler,AUT,male,1980-09-06,1.99,85.0,volleyball,0,0,0 -469894756,Clemens Rapp,GER,male,1989-07-14,1.93,85.0,aquatics,0,0,0 -112490441,Clement Mignon,FRA,male,1993-01-21,1.87,75.0,aquatics,0,1,0 -212851704,Clemente Russo,ITA,male,1982-07-27,1.81,,boxing,0,0,0 -526600784,Clemilda Fernandes Silva,BRA,female,1979-06-25,1.63,54.0,cycling,0,0,0 -406253452,Cleopatra Borel,TTO,female,1979-03-10,1.72,89.0,athletics,0,0,0 -62395737,Clive Pullen,JAM,male,1994-10-18,1.83,83.0,athletics,0,0,0 -81383299,Cloe Hache,FRA,female,1997-12-11,1.71,62.0,aquatics,0,0,0 -687327234,Coco Vandeweghe,USA,female,1991-12-06,1.86,70.0,tennis,0,0,0 -710695405,Cody Miller,USA,male,1992-01-09,1.81,79.0,aquatics,1,0,1 -844245742,Coen de Koning,NED,male,1983-04-05,1.83,74.0,sailing,0,0,0 -663469791,Colin Cheng,SIN,male,1989-09-10,1.75,80.0,sailing,0,0,0 -232134278,Colin Fleming,GBR,male,1984-08-13,1.88,78.0,tennis,0,0,0 -702982870,Colin Oates,GBR,male,1983-06-07,1.7,71.0,judo,0,0,0 -758562583,Colleen Furgeson,MHL,female,1998-11-21,1.76,65.0,aquatics,0,0,0 -989356382,Colleen Loach,CAN,female,1983-04-10,1.79,61.0,equestrian,0,0,0 -531594504,Colleen Quigley,USA,female,1992-11-20,1.76,61.0,athletics,0,0,0 -914233463,Collins Injera,KEN,male,1986-10-18,1.82,90.0,rugby sevens,0,0,0 -13487026,Colton Brown,USA,male,1991-10-08,1.83,90.0,judo,0,0,0 -600130363,Con Foley,AUS,male,1992-09-19,1.88,90.0,rugby sevens,0,0,0 -891831765,Concepcion Montaner,ESP,female,1981-01-14,1.7,59.0,athletics,0,0,0 -6156543,Cong Zhang,CHN,female,1990-05-03,1.76,62.0,aquatics,0,0,0 -669712048,Conlin McCABE,CAN,male,1990-08-20,2.05,100.0,rowing,0,0,0 -868434625,Connor Fields,USA,male,1992-09-14,1.83,88.0,cycling,1,0,0 -420887334,Connor Jaeger,USA,male,1991-04-30,1.86,77.0,aquatics,0,1,0 -988931818,Conny Wassmuth,GER,female,1983-04-13,1.67,64.0,canoe,0,0,0 -453971347,Conor Dwyer,USA,male,1989-01-10,1.96,88.0,aquatics,1,0,1 -893225332,Conor Harte,IRL,male,1988-04-03,1.96,93.0,hockey,0,0,0 -579847713,Conor McCullough,USA,male,1991-01-31,1.96,106.0,athletics,0,0,0 -766006270,Conseslus Kipruto,KEN,male,1994-12-08,1.67,58.0,athletics,1,0,0 -752876946,Constantijn Jonker,NED,male,1987-09-20,1.82,78.0,hockey,0,0,0 -861389806,Constantin Adam,ROU,male,1996-07-12,2.02,105.0,rowing,0,0,0 -299370363,Constantin Blaha,AUT,male,1987-12-01,1.78,77.0,aquatics,0,0,0 -779629172,Constantine Louloudis,GBR,male,1991-09-15,1.9,92.0,rowing,1,0,0 -606804046,Coralie Balmy,FRA,female,1987-06-02,1.8,67.0,aquatics,0,0,0 -752996196,Corben Sharrah,USA,male,1992-04-20,1.83,85.0,cycling,0,0,0 -37278485,Corey Cogdell,USA,female,1986-09-02,1.73,70.0,shooting,0,0,1 -473676473,Corey Main,NZL,male,1995-02-27,1.92,85.0,aquatics,0,0,0 -362762819,Corey Ollivierre,GRN,male,1997-03-16,,,aquatics,0,0,0 -267386705,Corina Caprioriu,ROU,female,1986-07-18,1.61,57.0,judo,0,0,0 -83666929,Corinna Kuhnle,AUT,female,1987-07-04,1.74,65.0,canoe,0,0,0 -931635380,Costanza Ferro,ITA,female,1993-07-05,1.69,56.0,aquatics,0,0,0 -567635337,Courtney Frerichs,USA,female,1993-01-18,1.71,53.0,athletics,0,0,0 -64284064,Courtney Hurley,USA,female,1990-09-30,1.73,79.0,fencing,0,0,0 -760942208,Courtney Mathewson,USA,female,1986-09-14,1.71,69.0,aquatics,1,0,0 -495348801,Courtney McGregor,NZL,female,1998-11-17,1.59,47.0,gymnastics,0,0,0 -771725452,Courtney Okolo,USA,female,1994-03-15,1.73,58.0,athletics,1,0,0 -650068927,Courtney Thompson,USA,female,1984-11-04,1.7,66.0,volleyball,0,0,1 -100145266,Craig Benson,GBR,male,1994-04-30,1.83,80.0,aquatics,0,0,0 -135156587,Craig Miller,NZL,male,1985-06-10,1.73,66.0,wrestling,0,0,0 -58627680,Crisanto Grajales,MEX,male,1987-05-06,1.66,60.0,triathlon,0,0,0 -592844746,Crispin Duenas,CAN,male,1986-01-05,1.7,81.0,archery,0,0,0 -710589299,Crista Cullen,GBR,female,1985-08-20,1.82,74.0,hockey,1,0,0 -662170808,Cristhian Pacheco,PER,male,1993-05-26,1.62,50.0,athletics,0,0,0 -227681295,Cristi-Ilie Pirghie,ROU,male,1992-07-20,2.02,108.0,rowing,0,0,0 -70420112,Cristian Bonilla,COL,male,1993-06-02,1.88,85.0,football,0,0,0 -243091497,Cristian Borja,COL,male,1993-02-18,1.79,72.0,football,0,0,0 -378249166,Cristian Espinoza,ARG,male,1995-04-03,1.67,70.0,football,0,0,0 -67716085,Cristian Pavon,ARG,male,1996-01-21,1.72,69.0,football,0,0,0 -781407492,Cristian Poglajen,ARG,male,1989-07-14,1.95,94.0,volleyball,0,0,0 -367878750,Cristian Quintero,VEN,male,1992-10-14,1.95,90.0,aquatics,0,0,0 -708503886,Cristian Toro,ESP,male,1992-04-29,1.88,90.0,canoe,1,0,0 -931912582,Cristiane,BRA,female,1985-05-15,1.76,69.0,football,0,0,0 -879488777,Cristiane Silva,BRA,female,1988-04-04,1.62,54.0,athletics,0,0,0 -999131085,Cristiano Felicio,BRA,male,1992-07-07,2.08,125.0,basketball,0,0,0 -900778069,Cristina Bujin,ROU,female,1988-04-12,1.72,56.0,athletics,0,0,0 -687865041,Cristina Chirichella,ITA,female,1994-02-10,1.95,73.0,volleyball,0,0,0 -487060100,Cristina Direito Branco,ANG,female,1985-03-15,1.72,68.0,handball,0,0,0 -733246969,Cristina Guinea,ESP,female,1992-07-31,1.66,61.0,hockey,0,0,0 -580737195,Cristina Neagu,ROU,female,1988-08-26,1.8,69.0,handball,0,0,0 -788552666,Cristina Sheehan,AUS,female,1998-09-26,1.63,49.0,aquatics,0,0,0 -301673145,Cristobal Grez Ahrens,CHI,male,1987-12-17,1.79,81.0,sailing,0,0,0 -611619985,Cristopher Joel Pavon Funes,HON,male,1993-04-18,1.73,93.0,weightlifting,0,0,0 -564187866,Cristy Nurse,CAN,female,1986-12-05,1.82,74.0,rowing,0,0,0 -607270987,Crystal Brittany Weekes,PUR,female,1998-01-14,1.78,73.0,taekwondo,0,0,0 -343383925,Crystal Dunn,USA,female,1992-07-03,1.57,54.0,football,0,0,0 -242394914,Crystal Emmanuel,CAN,female,1991-11-27,1.7,59.0,athletics,0,0,0 -679997450,Cuthbert Nyasango,ZIM,male,1982-09-17,1.63,56.0,athletics,0,0,0 -492543946,Cy Thompson,ISV,male,1988-07-01,,,sailing,0,0,0 -377141753,Cynthia Bolingo,BEL,female,1993-01-12,1.65,53.0,athletics,0,0,0 -778561493,Cynthia Meyer,CAN,female,1965-10-06,1.63,51.0,shooting,0,0,0 -354553687,Cynthia Vanessa Vescan,FRA,female,1992-02-07,1.7,74.0,wrestling,0,0,0 -884034589,Cyril Graff,FRA,male,1980-09-11,1.7,64.0,shooting,0,0,0 -733143674,Cyril Tommasone,FRA,male,1987-07-04,1.71,62.0,gymnastics,0,0,0 -13038806,Cyrille Carre,FRA,male,1984-04-11,1.84,75.0,canoe,0,0,0 -243473518,Cyrille Maret,FRA,male,1987-08-11,1.89,108.0,judo,0,0,1 -601293149,Cyrille Thiery,SUI,male,1990-09-27,1.79,67.0,cycling,0,0,0 -77303549,Cyrus Hostetler,USA,male,1986-08-08,1.88,95.0,athletics,0,0,0 -697656751,Céline van Gerner,NED,female,1994-12-01,1.58,51.0,gymnastics,0,0,0 -781080188,D J Forbes,NZL,male,1982-12-15,1.89,103.0,rugby sevens,0,0,0 -12021988,Daehoon Lee,KOR,male,1992-02-05,1.83,70.0,taekwondo,0,0,1 -392205384,Daemyung Lee,KOR,male,1988-09-14,1.82,78.0,shooting,0,0,0 -83363661,Dafne Schippers,NED,female,1992-06-15,1.79,70.0,athletics,0,1,0 -128148287,Dagmara Wozniak,USA,female,1988-07-01,1.73,81.0,fencing,0,0,1 -667178764,Dagnis Iljins,LAT,male,1992-08-20,1.78,82.0,canoe,0,0,0 -102532767,Daichi Sawano,JPN,male,1980-09-16,1.83,74.0,athletics,0,0,0 -519978075,Daichi Sugimoto,JPN,male,1993-07-15,1.86,79.0,football,0,0,0 -679515187,Daiene Marcal,BRA,female,1989-05-16,1.59,55.0,aquatics,0,0,0 -212523902,Daigo Hasegawa,JPN,male,1990-02-27,1.73,58.0,athletics,0,0,0 -567351741,Daigoro Timoncini,ITA,male,1985-12-13,1.8,98.0,wrestling,0,0,0 -836156960,Dailin Belmonte,CUB,female,1985-10-15,1.58,52.0,athletics,0,0,0 -648027000,Daina Levy,JAM,female,1993-05-27,1.65,98.0,athletics,0,0,0 -100107881,Dainis Upelnieks,LAT,male,1982-10-01,1.93,103.0,shooting,0,0,0 -268106399,Daisuke Fukushima,JPN,male,1977-09-20,1.78,68.0,equestrian,0,0,0 -46762119,Daisuke Matsunaga,JPN,male,1995-03-24,1.74,60.0,athletics,0,0,0 -429516855,Daisuke Narimatsu,JPN,male,1989-12-14,1.72,,boxing,0,0,0 -798836985,Daisurami Bonne,CUB,female,1988-03-09,1.71,60.0,athletics,0,0,0 -989560488,Daisy Cleverley,NZL,female,1997-04-30,1.7,59.0,football,0,0,0 -693678730,Daisy Kaitano,ZIM,female,1993-09-20,1.71,61.0,football,0,0,0 -242973427,Daiva Tuslaite,LTU,female,1986-06-18,1.72,58.0,cycling,0,0,0 -192954208,Daiya Seto,JPN,male,1994-05-24,1.74,73.0,aquatics,0,0,1 -902505168,Dajana Butulija,SRB,female,1986-02-23,1.75,65.0,basketball,0,0,1 -205886057,Dalal Mesfer Al Harith,QAT,female,1999-11-28,1.6,55.0,athletics,0,0,0 -576218781,Dalia Torrez,NCA,female,1990-03-29,1.74,70.0,aquatics,0,0,0 -441998277,Dalila Abdulkadir,BRN,female,1998-06-27,1.56,46.0,athletics,0,0,0 -209724463,Dalilah Muhammad,USA,female,1990-02-07,1.73,55.0,athletics,1,0,0 -360953664,Dalma Ruzicic Benedek,SRB,female,1982-02-21,1.65,65.0,canoe,0,0,0 -381413110,Dalma Sebestyen,HUN,female,1997-01-23,1.8,68.0,aquatics,0,0,0 -975197213,Daly Santana,PUR,female,1995-02-19,1.85,65.0,volleyball,0,0,0 -610645827,Damar Forbes,JAM,male,1990-09-11,1.88,82.0,athletics,0,0,0 -785182328,Damian Czykier,POL,male,1992-08-10,1.91,78.0,athletics,0,0,0 -969246730,Damian Martin,AUS,male,1984-09-05,1.86,90.0,basketball,0,0,0 -445781696,Damian Warner,CAN,male,1989-11-04,1.85,83.0,athletics,0,0,1 -227000230,Damian Wierling,GER,male,1996-02-13,1.96,74.0,aquatics,0,0,0 -112793044,Damian Zielinski,POL,male,1981-12-02,1.85,92.0,cycling,0,0,0 -315107389,Damiano Caruso,ITA,male,1987-10-12,1.79,65.0,cycling,0,0,0 -4074919,Damien Birkinhead,AUS,male,1993-04-08,1.9,140.0,athletics,0,0,0 -642505944,Damien Cler,FRA,male,1983-10-02,1.85,95.0,rugby sevens,0,0,0 -534791286,Damien Joly,FRA,male,1992-06-04,1.89,72.0,aquatics,0,0,0 -486111896,Damir Buric,CRO,male,1980-12-02,2.05,115.0,aquatics,0,1,0 -675408199,Damir Dugonjic,SLO,male,1988-02-21,2.02,105.0,aquatics,0,0,0 -901528927,Damir Dzumhur,BIH,male,1992-05-20,1.75,65.0,tennis,0,0,0 -971510831,Damir Martin,CRO,male,1988-07-14,1.89,97.0,rowing,0,1,0 -594844632,Damir Mikec,SRB,male,1984-03-31,1.79,79.0,shooting,0,0,0 -546700394,Damiris Dantas,BRA,female,1992-11-17,1.9,80.0,basketball,0,0,0 -751391017,Dan Bibby,GBR,male,1991-02-06,1.76,86.0,rugby sevens,0,1,0 -715044035,Dan Craven,NAM,male,1983-02-01,1.83,76.0,cycling,0,0,0 -619597624,Dan Froyliche,ISR,male,1992-11-18,1.82,73.0,sailing,0,0,0 -614482218,Dan Li,CHN,female,1988-09-18,1.55,48.0,gymnastics,0,0,1 -96353968,Dan Lin,CHN,male,1983-10-14,1.77,70.0,badminton,0,0,0 -721862825,Dan Norton,GBR,male,1988-03-22,1.8,85.0,rugby sevens,0,1,0 -765792716,Dan Wallace,GBR,male,1993-04-14,1.88,80.0,aquatics,0,1,0 -134936135,Dana Veldakova,SVK,female,1981-06-03,1.79,61.0,athletics,0,0,0 -622909202,Dana Vollmer,USA,female,1987-11-13,1.86,68.0,aquatics,1,1,1 -55407824,Danai Bhobho,ZIM,female,1992-12-01,1.63,59.0,football,0,0,0 -211024970,Danas Rapsys,LTU,male,1995-05-21,1.9,84.0,aquatics,0,0,0 -665814461,Dane Bird-Smith,AUS,male,1992-07-15,1.87,72.0,athletics,0,0,1 -772450203,Dane Sampson,AUS,male,1986-08-20,1.83,89.0,shooting,0,0,0 -453079347,Daneja Grandovec,SLO,female,1984-07-02,1.75,58.0,athletics,0,0,0 -527822094,Danell Leyva,USA,male,1991-10-30,1.73,72.0,gymnastics,0,2,0 -218424864,Dani Samuels,AUS,female,1988-05-26,1.82,82.0,athletics,0,0,0 -119961114,Daniah Hagul,LBA,female,1999-02-07,,,aquatics,0,0,0 -585194205,Danick Snelder,NED,female,1990-05-22,1.78,75.0,handball,0,0,0 -161929163,Daniel Akpeyi,NGR,male,1986-03-08,1.87,,football,0,0,1 -579693569,Daniel Allerstorfer,AUT,male,1992-12-04,1.83,130.0,judo,0,0,0 -574774102,Daniel Andujar,ESP,male,1994-05-14,1.82,78.0,athletics,0,0,0 -2865880,Daniel Asenov,BUL,male,1997-05-17,1.63,,boxing,0,0,0 -183503173,Daniel Bailey,ANT,male,1986-09-09,1.79,68.0,athletics,0,0,0 -189421989,Daniel Beale,AUS,male,1993-02-12,1.84,74.0,hockey,0,0,0 -568936910,Daniel Bluman,COL,male,1990-03-15,1.82,72.0,equestrian,0,0,0 -442179948,Daniel Bowker,AUS,male,1987-09-21,1.91,89.0,canoe,0,0,0 -719186892,Daniel Brodmeier,GER,male,1987-09-02,1.8,100.0,shooting,0,0,0 -436335387,Daniel Cornelius Jansen Vandoorn,CAN,male,1990-03-21,2.07,98.0,volleyball,0,0,0 -719022816,Daniel Corral,MEX,male,1990-01-25,1.75,64.0,gymnastics,0,0,0 -10083714,Daniel Dal Bo,ARG,male,1987-09-08,1.85,78.0,canoe,0,0,0 -16964420,Daniel Deusser,GER,male,1981-08-13,1.9,76.0,equestrian,0,0,1 -93209165,Daniel Diaz,ARG,male,1989-07-07,1.68,63.0,cycling,0,0,0 -254552114,Daniel Estrada,CHI,male,1990-02-27,1.73,61.0,athletics,0,0,0 -258029864,Daniel Flores,VEN,male,1981-10-17,1.8,73.0,sailing,0,0,0 -55281398,Daniel Fox,GBR,male,1983-03-03,1.83,78.0,hockey,0,0,0 -387284782,Daniel Gomez,MEX,male,1990-05-06,1.78,78.0,fencing,0,0,0 -542045752,Daniel Goodfellow,GBR,male,1996-10-19,1.67,58.0,aquatics,0,0,1 -140752728,Daniel Gorak,POL,male,1983-10-09,1.78,77.0,table tennis,0,0,0 -654450748,Daniel Gyurta,HUN,male,1989-05-04,1.85,76.0,aquatics,0,0,0 -582252675,Daniel Habesohn,AUT,male,1986-07-22,1.85,78.0,table tennis,0,0,0 -270915290,Daniel Havel,CZE,male,1991-08-10,1.78,79.0,canoe,0,0,1 -988381105,Daniel Jasinski,GER,male,1989-08-05,2.07,120.0,athletics,0,0,1 -738563409,Daniel Jason Lewis,AUS,male,1993-12-18,1.78,,boxing,0,0,0 -642681406,Daniel Jerent,FRA,male,1991-06-04,1.89,84.0,fencing,1,0,0 -679369077,Daniel Ligeti,HUN,male,1989-07-31,1.9,117.0,wrestling,0,0,0 -686181990,Daniel Lopez Pinedo,ESP,male,1980-07-16,1.9,88.0,aquatics,0,0,0 -337337344,Daniel Lowe,USA,male,1992-11-18,1.83,81.0,shooting,0,0,0 -173884124,Daniel Macovei,ROU,male,1992-09-15,1.85,71.0,aquatics,0,0,0 -635773855,Daniel Martin,IRL,male,1986-08-20,1.76,63.0,cycling,0,0,0 -108357874,Daniel McConnell,AUS,male,1985-08-09,1.8,70.0,cycling,0,0,0 -702422699,Daniel Narcisse,FRA,male,1979-12-16,1.89,93.0,handball,0,1,0 -306635640,Daniel Natea,ROU,male,1992-04-21,2.03,170.0,judo,0,0,0 -747184552,Daniel Nestor,CAN,male,1972-09-04,1.91,87.0,tennis,0,0,0 -287487601,Daniel Paul Dennis,USA,male,1986-09-24,1.66,56.0,wrestling,0,0,0 -12736710,Daniel Repacholi,AUS,male,1982-05-15,2.02,128.0,shooting,0,0,0 -469677612,Daniel Rezende Xavier,BRA,male,1982-08-31,1.91,81.0,archery,0,0,0 -165866962,Daniel Sancery,BRA,male,1994-05-27,1.82,90.0,rugby sevens,0,0,0 -10072667,Daniel Shingles,GBR,male,1986-07-05,1.84,84.0,hockey,0,0,0 -149524585,Daniel Skaaning,DEN,male,1993-06-22,1.8,79.0,aquatics,0,0,0 -463354597,Daniel Smith,AUS,male,1991-05-28,1.9,83.0,aquatics,0,0,0 -285374253,Daniel Stahl,SWE,male,1992-08-27,2.0,155.0,athletics,0,0,0 -937921024,Daniel Talbot,GBR,male,1991-05-01,1.84,73.0,athletics,0,0,0 -815557568,Daniel Teklehaimanot,ERI,male,1988-11-10,,,cycling,0,0,0 -689191404,Daniel Tihomirov Aleksandrov,BUL,male,1991-09-13,1.82,81.0,wrestling,0,0,0 -278950598,Daniel Trojanowski,POL,male,1982-07-24,1.7,55.0,rowing,0,0,0 -797414950,Daniel Varga,HUN,male,1983-09-25,2.01,96.0,aquatics,0,0,0 -275083561,Daniel Vargas,MEX,male,1986-09-01,1.97,94.0,volleyball,0,0,0 -986523075,Daniel Vargas,MEX,male,1984-03-06,1.62,53.0,athletics,0,0,0 -317345417,Daniel Wiederkehr,SUI,male,1989-05-15,1.83,73.0,rowing,0,0,0 -493335766,Daniel Willcox,NZL,male,1990-06-08,1.79,72.0,sailing,0,0,0 -490419163,Daniela Campuzano Chavez Peon,MEX,female,1986-10-21,1.73,56.0,cycling,0,0,0 -111341393,Daniela Cardoso,POR,female,1991-12-15,1.57,48.0,athletics,0,0,0 -893449687,Daniela Carlan,ROU,female,1980-09-18,1.64,47.0,athletics,0,0,0 -735809811,Daniela Druncea,ROU,female,1990-11-02,1.5,50.0,rowing,0,0,1 -540393824,Daniela Ferenz,GER,female,1990-08-03,1.71,61.0,athletics,0,0,0 -466134584,Daniela Matarazzo Carraro,BRA,female,1985-03-25,1.65,67.0,shooting,0,0,0 -793252386,Daniela Monteiro Dodean,ROU,female,1988-01-13,1.7,55.0,table tennis,0,0,0 -816970958,Daniela Piedade,BRA,female,1979-03-02,1.74,70.0,handball,0,0,0 -215890909,Daniela Potapova,GER,female,1996-01-17,1.66,48.0,gymnastics,0,0,0 -783504559,Daniele Garozzo,ITA,male,1992-08-04,1.77,65.0,fencing,1,0,0 -821159021,Daniele Hypolito,BRA,female,1984-09-08,1.47,40.0,gymnastics,0,0,0 -438701799,Daniele Lupo,ITA,male,1991-05-06,1.93,83.0,volleyball,0,1,0 -590118305,Daniele Meucci,ITA,male,1985-10-07,1.78,64.0,athletics,0,0,0 -207450764,Danielle Kettlewell,AUS,female,1992-11-17,1.75,65.0,aquatics,0,0,0 -699029368,Danielle Lins,BRA,female,1985-01-05,1.83,71.0,volleyball,0,0,0 -534901017,Danielle Page,SRB,female,1986-11-14,1.88,77.0,basketball,0,0,1 -193943106,Danielle Prince,AUS,female,1992-06-12,1.66,52.0,gymnastics,0,0,0 -426724791,Danielle Suzanne Lappage,CAN,female,1990-09-24,1.65,63.0,wrestling,0,0,0 -554312456,Danielle Villars,SUI,female,1993-03-06,1.78,62.0,aquatics,0,0,0 -655184576,Danielle Waterman,GBR,female,1985-01-20,1.65,64.0,rugby sevens,0,0,0 -726533168,Daniil Pakhomov,RUS,male,1998-08-05,1.89,77.0,aquatics,0,0,0 -932783327,Danijel Furtula,MNE,male,1992-07-31,1.93,118.0,athletics,0,0,0 -761545111,Danijel Saric,QAT,male,1977-06-27,1.95,92.0,handball,0,0,0 -153868407,Danila Izotov,RUS,male,1991-10-02,1.92,90.0,aquatics,0,0,0 -937365507,Danilo Caro Guarnieri,COL,male,1965-09-06,1.7,58.0,shooting,0,0,0 -782501864,Danish Mujtaba,IND,male,1988-12-20,1.68,66.0,hockey,0,0,0 -128511976,Daniyar Ismayilov,TUR,male,1992-02-03,1.73,69.0,weightlifting,0,1,0 -76405574,Daniyar Yeleussinov,KAZ,male,1991-03-13,1.78,,boxing,1,0,0 -692417877,Danka Bartekova,SVK,female,1984-10-19,1.7,55.0,shooting,0,0,0 -787754025,Danka Kovinic,MNE,female,1994-11-18,1.7,67.0,tennis,0,0,0 -828679930,Dannie Boyd,CAN,female,1990-05-23,1.78,71.0,sailing,0,0,0 -101678493,Danniel Thomas,JAM,female,1992-11-11,1.68,91.0,athletics,0,0,0 -502713645,Danny Barrett,USA,male,1990-03-23,1.88,102.0,rugby sevens,0,0,0 -210284579,Danny Chia,MAS,male,1972-11-29,1.7,75.0,golf,0,0,0 -540892576,Danny Lee,NZL,male,1990-07-24,1.8,79.0,golf,0,0,0 -879435329,Danny Pinheiro Rodrigues,FRA,male,1985-04-16,1.61,57.0,gymnastics,0,0,0 -12537963,Danny Willett,GBR,male,1987-10-03,1.8,79.0,golf,0,0,0 -552666305,Danuta Kozak,HUN,female,1987-01-11,1.68,63.0,canoe,3,0,0 -221668114,Danuta Urbanik,POL,female,1989-12-24,1.67,49.0,athletics,0,0,0 -729797574,Dapeng Wang,CHN,male,1996-12-03,1.84,95.0,archery,0,0,0 -828313980,Dara Hassanien,EGY,female,1996-04-01,1.69,59.0,aquatics,0,0,0 -479014574,Darcina Manuel,NZL,female,1992-09-24,1.62,57.0,judo,0,0,0 -490330541,Daria Chikunova,RUS,female,1999-04-12,1.77,59.0,aquatics,0,0,0 -358425644,Daria Dmitrieva,RUS,female,1995-08-09,1.78,74.0,handball,1,0,0 -592811889,Daria Gavrilova,AUS,female,1994-03-05,1.66,61.0,tennis,0,0,0 -971063935,Daria Iushko,UKR,female,1985-02-05,1.74,57.0,aquatics,0,0,0 -918679234,Daria Kasatkina,RUS,female,1997-05-07,1.7,62.0,tennis,0,0,0 -976263519,Daria Malygina,RUS,female,1994-04-04,2.02,82.0,volleyball,0,0,0 -201671999,Daria Mullakaeva,RUS,female,1998-06-18,1.72,61.0,aquatics,0,0,0 -183631297,Daria Pikulik,POL,female,1997-01-06,1.65,54.0,cycling,0,0,0 -981886723,Daria Pogorzelec,POL,female,1990-07-20,1.72,78.0,judo,0,0,0 -399441834,Daria Shmeleva,RUS,female,1994-10-26,1.64,64.0,cycling,0,1,0 -809722266,Daria Spiridonova,RUS,female,1998-07-08,1.56,45.0,gymnastics,0,1,0 -345015361,Daria Talanova,KGZ,female,1995-12-08,1.68,56.0,aquatics,0,0,0 -967665587,Daria Ustinova,RUS,female,1998-08-29,,,aquatics,0,0,0 -991531099,Daria Vdovina,RUS,female,1989-12-15,1.56,49.0,shooting,0,0,0 -881563866,Darian King,BAR,male,1992-04-26,1.72,70.0,tennis,0,0,0 -375255529,Darienn Ferrer Delis,CUB,male,1982-10-31,2.02,90.0,volleyball,0,0,0 -977866181,Dariga Shakimova,KAZ,female,1988-11-20,1.72,,boxing,0,0,1 -408213856,Dario Saric,CRO,male,1994-04-08,2.07,110.0,basketball,0,0,0 -113247488,Dariusz Radosz,POL,male,1986-08-13,1.99,91.0,rowing,0,0,0 -878416762,Dariya Derkach,ITA,female,1993-03-27,1.7,50.0,athletics,0,0,0 -689516934,Darko Brguljan,MNE,male,1990-11-05,1.8,97.0,aquatics,0,0,0 -670032549,Darko Cingesar,SLO,male,1990-07-25,1.87,93.0,handball,0,0,0 -756775265,Darko Planinic,CRO,male,1990-11-22,2.11,120.0,basketball,0,0,0 -996641471,Darlan Romani,BRA,male,1991-04-09,1.87,90.0,athletics,0,0,0 -177985963,Darlene,BRA,female,1990-01-11,1.73,59.0,football,0,0,0 -734837766,Darly Zoqbi,ESP,female,1982-08-25,1.78,70.0,handball,0,0,0 -549879032,Darrell Hill,USA,male,1993-08-17,1.91,145.0,athletics,0,0,0 -933764156,Darrell Wesh,HAI,male,1992-01-21,1.83,160.0,athletics,0,0,0 -366274936,Darwin Espinal,HON,male,1995-01-16,1.78,76.0,football,0,0,0 -369500376,Darya Klishina,RUS,female,1991-01-15,1.8,58.0,athletics,0,0,0 -373657197,Darya Maslova,KGZ,female,1995-05-06,1.65,44.0,athletics,0,0,0 -823394699,Darya Naumava,BLR,female,1995-08-26,1.65,75.0,weightlifting,0,1,0 -913687738,Darya Pachabut,BLR,female,1994-12-31,1.63,67.0,weightlifting,0,0,0 -380938305,Darya Semyonova,TKM,female,2002-05-28,1.7,50.0,aquatics,0,0,0 -444977271,Darya Skrypnik,BLR,female,1987-12-12,1.62,52.0,judo,0,0,0 -903300990,Darya Stepanyuk,UKR,female,1990-05-22,1.74,62.0,aquatics,0,0,0 -274289648,Daryl Homer,USA,male,1990-07-16,1.73,74.0,fencing,0,1,0 -590695708,Daryl Impey,RSA,male,1984-12-06,1.83,70.0,cycling,0,0,0 -849289031,Daryll Neita,GBR,female,1996-08-29,1.72,61.0,athletics,0,0,1 -793163437,Daryna Verkhogliad,UKR,female,1992-02-22,1.8,80.0,rowing,0,0,0 -164963140,Daryna Zevina,UKR,female,1994-09-01,1.78,67.0,aquatics,0,0,0 -916140092,Daseul Lee,KOR,female,1996-11-08,1.59,47.0,athletics,0,0,0 -771946742,Dattu Baban Bhokanal,IND,male,1991-04-05,1.89,81.0,rowing,0,0,0 -841861816,Daulet Shabanbay,KAZ,male,1983-08-09,1.9,125.0,wrestling,0,0,0 -769652662,Dave Hughes,USA,male,1978-01-22,1.86,71.0,sailing,0,0,0 -59605223,David Adley Smith Ii,PUR,male,1992-05-02,1.92,82.0,athletics,0,0,0 -301471859,David Alegre,ESP,male,1984-09-06,1.84,75.0,hockey,0,0,0 -606979268,David Ames,GBR,male,1989-06-25,1.88,82.0,hockey,0,0,0 -83570795,David Andersen,AUS,male,1980-06-23,2.1,102.0,basketball,0,0,0 -849872895,David Belyavskiy,RUS,male,1992-02-23,1.63,55.0,gymnastics,0,1,1 -643787184,David Boudia,USA,male,1989-04-24,1.76,72.0,aquatics,0,1,1 -52887085,David Brandl,AUT,male,1987-04-19,1.88,85.0,aquatics,0,0,0 -721958244,David Bustos,ESP,male,1990-08-25,1.82,64.0,athletics,0,0,0 -135006716,David Carter,CAN,male,1981-11-04,1.75,79.0,hockey,0,0,0 -529354305,David Carver,MRI,male,1987-09-05,1.83,68.0,athletics,0,0,0 -640388256,David Chapman,AUS,male,1965-03-22,1.67,72.0,shooting,0,0,0 -89844900,David Condon,GBR,male,1991-07-06,1.8,79.0,hockey,0,0,0 -806548008,David Cubillan,VEN,male,1987-07-27,1.83,79.0,basketball,0,0,0 -557960426,David Fernandes,POR,male,1983-09-08,1.81,82.0,canoe,0,0,0 -570007062,David Ferrer,ESP,male,1982-04-02,1.75,73.0,tennis,0,0,0 -926634435,David Florence,GBR,male,1982-08-08,1.88,76.0,canoe,0,1,0 -502550042,David Foldhazi,HUN,male,1995-01-06,1.89,84.0,aquatics,0,0,0 -971955364,David Goffin,BEL,male,1990-12-07,1.8,69.0,tennis,0,0,0 -231429156,David Graf,GER,male,1989-01-12,1.91,,boxing,0,0,0 -146589026,David Graf,SUI,male,1989-09-08,1.8,79.0,cycling,0,0,0 -584460736,David Harte,IRL,male,1988-04-03,1.95,94.0,hockey,0,0,0 -748526567,David Hearn,CAN,male,1979-06-17,1.85,77.0,golf,0,0,0 -487593687,David Higgins,USA,male,1994-06-27,1.76,68.0,shooting,0,0,0 -98040137,David Horvath,HUN,male,1996-05-16,1.75,73.0,aquatics,0,0,0 -695436665,David Hunt,RSA,male,1991-02-01,1.97,90.0,rowing,0,0,0 -733541995,David Jessen,CZE,male,1996-12-05,1.75,68.0,gymnastics,0,0,0 -440645107,David Joao Serralheiro Rosa,POR,male,1986-11-12,1.65,52.0,cycling,0,0,0 -553564024,David Katoatau,KIR,male,1984-07-17,1.7,105.0,weightlifting,0,0,0 -823262201,David Kostelecky,CZE,male,1975-05-12,1.9,93.0,shooting,0,0,0 -336599329,David Lee,USA,male,1982-03-08,2.03,105.0,volleyball,0,0,1 -807043500,David Lekuta Rudisha,KEN,male,1988-12-17,1.9,76.0,athletics,1,0,0 -125537512,David Lingmerth,SWE,male,1987-07-22,1.7,80.0,golf,0,0,0 -206952063,David Mauricio Mier Y Teran Cuevas,MEX,male,1978-08-07,1.72,68.0,sailing,0,0,0 -906932951,David McKeon,AUS,male,1992-07-25,1.95,85.0,aquatics,0,0,0 -186269723,David McNeill,AUS,male,1986-10-06,1.75,53.0,athletics,0,0,0 -916000056,David Miklavcic,SLO,male,1983-01-29,1.96,100.0,handball,0,0,0 -881131530,David Morgan,AUS,male,1994-01-01,1.84,82.0,aquatics,0,0,1 -26934524,David Munoz Hidalgo,PAN,male,1964-09-29,1.7,70.0,shooting,0,0,0 -764731011,David Obernosterer,AUT,male,1989-05-30,1.83,80.0,badminton,0,0,0 -845122813,David Oliver Joyce,IRL,male,1987-02-12,1.71,,boxing,0,0,0 -679505903,David Pasqualucci,ITA,male,1996-06-27,1.81,81.0,archery,0,0,0 -971131011,David Plummer,USA,male,1985-10-09,1.91,95.0,aquatics,1,0,1 -387925121,David Powell,AUS,male,1991-04-08,1.75,67.0,table tennis,0,0,0 -735843682,David Sanchez Lopez,ESP,male,1994-07-20,1.66,69.0,weightlifting,0,0,0 -440461124,David Smith,USA,male,1985-05-15,2.01,86.0,volleyball,0,0,1 -665678036,David Soderberg,FIN,male,1979-08-11,1.85,96.0,athletics,0,0,0 -517039410,David Storl,GER,male,1990-07-27,1.98,117.0,athletics,0,0,0 -582202703,David Svoboda,CZE,male,1985-03-19,1.83,76.0,modern pentathlon,0,0,0 -9174337,David Sylvere Patrick Boui,CAF,male,1988-06-28,1.87,68.0,taekwondo,0,0,0 -63423995,David Torrence,PER,male,1985-11-26,1.9,65.0,athletics,0,0,0 -643201191,David Valero Serrano,ESP,male,1988-12-27,1.89,76.0,cycling,0,0,0 -507639455,David Verburg,USA,male,1991-05-14,1.73,70.0,athletics,1,0,0 -277572925,David Verraszto,HUN,male,1988-08-22,1.8,76.0,aquatics,0,0,0 -301376496,David Watts,AUS,male,1992-02-05,1.91,96.0,rowing,0,0,0 -661068918,David van der Colff,BOT,male,1997-04-29,,,aquatics,0,0,0 -919210003,Davide Manenti,ITA,male,1989-04-16,1.77,80.0,athletics,0,0,0 -244933358,Davide Uccellari,ITA,male,1991-10-11,1.85,64.0,triathlon,0,0,0 -636525680,Davie Selke,GER,male,1995-01-20,1.92,82.0,football,0,1,0 -382116248,Davilson dos Santos Morais,CPV,male,1989-02-03,1.88,,boxing,0,0,0 -397002318,Davit Chakvetadze,RUS,male,1992-10-18,1.74,85.0,wrestling,1,0,0 -259093930,Daviti Kharazishvili,GEO,male,1992-04-24,1.71,68.0,athletics,0,0,0 -688799432,Davor Stefanek,SRB,male,1985-09-12,1.7,66.0,wrestling,1,0,0 -409683971,Dawid Konarski,POL,male,1989-08-31,1.98,93.0,volleyball,0,0,0 -92392185,Dawit Seyaum,ETH,female,1996-07-27,1.61,49.0,athletics,0,0,0 -145653962,Dawit Wolde,ETH,male,1991-05-19,1.8,64.0,athletics,0,0,0 -337539240,Dayaris Mestre Alvarez,CUB,female,1986-11-20,1.5,48.0,judo,0,0,0 -625400298,Daynara de Paula,BRA,female,1989-07-25,1.63,55.0,aquatics,0,0,0 -777458812,DeAndre Jordan,USA,male,1988-07-21,2.11,120.0,basketball,1,0,0 -170161629,DeMarcus Cousins,USA,male,1990-08-13,2.1,122.0,basketball,1,0,0 -996704082,Deajah Stevens,USA,female,1995-05-19,,,athletics,0,0,0 -491112299,Dean Bombac,SLO,male,1989-04-04,1.89,94.0,handball,0,0,0 -275797773,Deanna Price,USA,female,1993-06-08,1.73,99.0,athletics,0,0,0 -612473315,Deanne Rose,CAN,female,1999-03-03,1.63,62.0,football,0,0,1 -378684511,Debashree Mazumdar,IND,female,1991-04-06,1.64,53.0,athletics,0,0,0 -119019224,Debby Stam-Pilon,NED,female,1984-07-24,1.84,68.0,volleyball,0,0,0 -254113146,Debby Susanto,INA,female,1989-05-03,1.61,51.0,badminton,0,0,0 -428138281,Debinha,BRA,female,1991-10-20,1.57,55.0,football,0,0,0 -844929047,Debora Seilhamer,PUR,female,1985-10-04,1.66,61.0,volleyball,0,0,0 -127202687,Deborah Rodriguez,URU,female,1992-12-02,1.75,61.0,athletics,0,0,0 -871793969,Deborah Tsai,AUS,female,1994-12-18,1.62,52.0,aquatics,0,0,0 -930773141,Debra Daniel,FSM,female,1991-03-04,1.53,68.0,aquatics,0,0,0 -679759459,Deep Ekka,IND,female,1994-06-03,1.58,63.0,hockey,0,0,0 -947558770,Deepika Deepika,IND,female,1987-02-07,1.59,61.0,hockey,0,0,0 -326907106,Deepika Kumari,IND,female,1994-06-13,1.63,61.0,archery,0,0,0 -892176470,Deiver Machado,COL,male,1993-09-02,1.79,71.0,football,0,0,0 -532865663,Deividas Margevicius,LTU,male,1995-04-26,1.87,80.0,aquatics,0,0,0 -859863879,Deivy Balanta,COL,male,1993-02-09,1.84,77.0,football,0,0,0 -295848758,Dejan Pajic,SRB,male,1989-08-15,1.78,78.0,canoe,0,0,0 -884920098,Dejen Gebremeskel,ETH,male,1989-11-24,1.8,58.0,athletics,0,0,0 -750143193,Delanno Williams,GBR,male,1993-12-23,1.8,73.0,athletics,0,0,0 -797004979,Delfina Merino,ARG,female,1989-10-15,1.69,59.0,hockey,0,0,0 -86795736,Delphine Lansac,FRA,female,1995-07-18,1.69,60.0,badminton,0,0,0 -174042478,Demar DeRozan,USA,male,1989-08-07,2.01,99.0,basketball,1,0,0 -306671782,Demetrius Pinder,BAH,male,1989-02-13,1.78,70.0,athletics,0,0,0 -224926689,Demian Gonzalez,ARG,male,1983-02-21,1.92,82.0,volleyball,0,0,0 -675018716,Demita Vega de Lille,MEX,female,1983-07-21,1.72,60.0,sailing,0,0,0 -493263577,Denes Varga,HUN,male,1987-03-29,1.93,96.0,aquatics,0,0,0 -398783638,Deni,INA,male,1989-07-26,1.65,69.0,weightlifting,0,0,0 -13630067,Denia Caballero,CUB,female,1990-01-13,1.75,70.0,athletics,0,0,1 -95013242,Denika Kassim,COM,female,1997-08-08,1.57,44.0,athletics,0,0,0 -634903913,Denis Abliazin,RUS,male,1992-08-03,1.6,62.0,gymnastics,0,2,1 -503917240,Denis Dmitriev,RUS,male,1986-03-23,1.77,90.0,cycling,0,0,1 -384593486,Denis Gargaud Chanut,FRA,male,1987-07-22,1.81,76.0,canoe,1,0,0 -245021648,Denis Gribanov,RUS,male,1986-06-03,1.9,74.0,sailing,0,0,0 -69215365,Denis Iartcev,RUS,male,1990-09-18,1.76,73.0,judo,0,0,0 -286228746,Denis Istomin,UZB,male,1986-09-07,1.87,85.0,tennis,0,0,0 -873793608,Denis Koulakov,RUS,male,1982-11-21,1.74,80.0,shooting,0,0,0 -521906110,Denis Kudla,USA,male,1992-08-17,1.81,79.0,tennis,0,0,0 -509497804,Denis Maksymilian Kudla,GER,male,1994-12-24,1.84,90.0,wrestling,0,0,1 -46394870,Denis Mysak,SVK,male,1995-11-30,1.89,90.0,canoe,0,1,0 -598284598,Denis Petrashov,KGZ,male,2000-02-01,,,aquatics,0,0,0 -168293636,Denis Ulanov,KAZ,male,1993-10-28,1.75,85.0,weightlifting,0,0,0 -63343858,Denisa Dedu,ROU,female,1994-09-27,1.82,73.0,handball,0,0,0 -913453330,Denisa Rosolova,CZE,female,1986-08-21,1.75,63.0,athletics,0,0,0 -4129853,Denise Lim,SIN,female,1991-09-14,1.55,57.0,sailing,0,0,0 -692229320,Deniss Karpak,EST,male,1986-07-18,2.0,100.0,sailing,0,0,0 -378680975,Deniz Cinar,TUR,male,1984-12-08,1.72,65.0,sailing,0,0,0 -33695982,Dennis Goossens,BEL,male,1993-12-16,1.6,65.0,gymnastics,0,0,0 -844014981,Dennis Ombachi,KEN,male,1994-12-14,1.85,98.0,rugby sevens,0,0,0 -432632544,Denys Kostyuk,UKR,male,1982-03-13,1.75,62.0,cycling,0,0,0 -107760935,Denys Molchanov,UKR,male,1987-05-16,1.9,82.0,tennis,0,0,0 -556851081,Denys Solonenko,UKR,male,1992-10-25,1.79,,boxing,0,0,0 -343364504,Deo Gracia Ngokaba,CGO,male,1997-05-17,1.9,140.0,judo,0,0,0 -510782872,Deokhyeon Kim,KOR,male,1985-12-08,1.8,71.0,athletics,0,0,0 -278528807,Deolin Mekoa,RSA,male,1993-08-10,1.75,67.0,football,0,0,0 -682281793,Deon Lendore,TTO,male,1992-10-28,1.79,75.0,athletics,0,0,0 -2593240,Deonise Fachinello,BRA,female,1983-06-20,1.8,73.0,handball,0,0,0 -849646461,Derek Drouin,CAN,male,1990-03-06,1.96,83.0,athletics,1,0,0 -789423999,Derek Hawkins,GBR,male,1989-04-29,1.8,68.0,athletics,0,0,0 -368262898,Derek Sua,SAM,male,1987-12-29,,,judo,0,0,0 -970701386,Derlys Ayala,PAR,male,1990-01-07,1.78,75.0,athletics,0,0,0 -484914459,Desiree Henry,GBR,female,1995-08-26,1.71,64.0,athletics,0,0,1 -525844968,Desiree Linden,USA,female,1983-07-26,1.55,43.0,athletics,0,0,0 -781892650,Desiree Rossit,ITA,female,1994-03-19,1.79,51.0,athletics,0,0,0 -535106472,Desiree Scott,CAN,female,1987-07-31,1.6,63.0,football,0,0,1 -639727915,Desler Puggaard,DEN,male,1995-02-19,1.87,71.0,football,0,0,0 -670754445,Deuce Carter,JAM,male,1990-09-28,1.83,82.0,athletics,0,0,0 -640447595,Devery Karz,USA,female,1988-02-18,1.73,58.0,rowing,0,0,0 -360434724,Devid Safaryan,ARM,male,1989-08-01,1.7,65.0,wrestling,0,0,0 -831917950,Devin McEwan,USA,male,1984-10-11,1.78,77.0,canoe,0,0,0 -211396920,Devindar Walmiki,IND,male,1992-05-28,1.78,69.0,hockey,0,0,0 -94495645,Devohn Teixeira,CAN,male,1989-02-09,1.65,61.0,hockey,0,0,0 -355897271,Devon Allen,USA,male,1994-12-12,1.86,83.0,athletics,0,0,0 -974219288,Devon Manchester,NZL,male,1989-11-11,1.78,78.0,hockey,0,0,0 -92529957,Devon Myles William Brown,RSA,male,1992-05-21,1.88,80.0,aquatics,0,0,0 -799132600,Dewi Safitri,INA,female,1993-02-10,1.51,53.0,weightlifting,0,0,0 -921638208,Dewi Yuliawati,INA,female,1997-06-02,1.66,65.0,rowing,0,0,0 -438551437,Dex Elmont,NED,male,1984-01-10,1.75,73.0,judo,0,0,0 -912033914,Dhurgham Ismael,IRQ,male,1994-05-23,1.77,70.0,football,0,0,0 -203504788,Di Wu,CHN,female,1993-10-27,1.82,75.0,basketball,0,0,0 -10438447,Diaaeldin Kamal Gouda Abdelmottaleb,EGY,male,1993-05-02,1.77,125.0,wrestling,0,0,0 -856659914,Diamara Planell,PUR,female,1993-02-16,1.8,67.0,athletics,0,0,0 -877009812,Diana Abla,BRA,female,1995-07-29,1.75,75.0,aquatics,0,0,0 -723783485,Diana Aydosova,KAZ,female,1995-09-05,1.58,50.0,athletics,0,0,0 -681626502,Diana Bacosi,ITA,female,1983-07-13,1.75,85.0,shooting,1,0,0 -266313114,Diana Khubeseryan,ARM,female,1994-05-05,1.7,60.0,athletics,0,0,0 -767005117,Diana Lobacevske,LTU,female,1980-08-07,1.74,57.0,athletics,0,0,0 -572629331,Diana Martin,ESP,female,1981-04-01,1.63,50.0,athletics,0,0,0 -48306282,Diana Matheson,CAN,female,1984-04-06,1.53,53.0,football,0,0,1 -812031749,Diana Ospina,COL,female,1989-03-03,1.57,58.0,football,0,0,0 -392473268,Diana Pineda,COL,female,1984-09-06,1.67,55.0,aquatics,0,0,0 -77686741,Diana Reyes,PUR,female,1993-04-29,1.91,80.0,volleyball,0,0,0 -910190523,Diana Sujew,GER,female,1990-11-02,1.66,53.0,athletics,0,0,0 -352310155,Diana Taurasi,USA,female,1982-06-11,1.82,70.0,basketball,1,0,0 -423391587,Diane Nukuri,BDI,female,1984-12-01,1.83,59.0,athletics,0,0,0 -123911422,Dianelys Perez,CUB,female,1988-06-28,1.49,59.0,shooting,0,0,0 -987878989,Didar Khamza,KAZ,male,1997-02-15,1.75,73.0,judo,0,0,0 -629941187,Didier Kiki,BEN,male,1995-11-30,1.85,84.0,athletics,0,0,0 -355496444,Diederik van Silfhout,NED,male,1988-04-20,1.9,87.0,equestrian,0,0,0 -109933404,Diego Botin Le Chever,ESP,male,1993-12-25,1.83,81.0,sailing,0,0,0 -210454399,Diego Colorado,COL,male,1973-08-31,1.69,62.0,athletics,0,0,0 -796700277,Diego Hypolito,BRA,male,1986-06-19,1.7,68.0,gymnastics,0,1,0 -39686227,Diego Milan Jimenez,DOM,male,1985-07-10,1.79,67.0,cycling,0,0,0 -5063397,Diego Occhiuzzi,ITA,male,1981-04-30,1.8,76.0,fencing,0,0,0 -578628315,Diego Palomeque,COL,male,1993-12-05,1.78,67.0,athletics,0,0,0 -820045789,Diego Rosa,ITA,male,1989-03-27,1.78,65.0,cycling,0,0,0 -309429588,Diego del Real,MEX,male,1994-03-06,1.85,100.0,athletics,0,0,0 -689489031,Dienov Andres Koka,CGO,male,1996-08-02,1.73,68.0,aquatics,0,0,0 -119737320,Dieter Dekoninck,BEL,male,1991-01-28,1.9,85.0,aquatics,0,0,0 -316835810,Dieudonne Wilfred Seyi Ntsengue,CMR,male,1998-01-23,1.83,,boxing,0,0,0 -249259020,Dilara Lokmanhekim,TUR,female,1994-04-18,1.65,48.0,judo,0,0,0 -549822679,Dilara Uralp,TUR,female,1995-11-16,1.63,54.0,sailing,0,0,0 -227184360,Diletta Carli,ITA,female,1996-05-07,1.7,61.0,aquatics,0,0,0 -847147736,Dilshod Nazarov,TJK,male,1982-05-06,1.87,120.0,athletics,1,0,0 -146607194,Dilshodjon Turdiev,UZB,male,1991-10-19,1.72,75.0,wrestling,0,0,0 -776618424,Dimitar Angelov Kumchev,BUL,male,1980-04-20,1.8,125.0,wrestling,0,0,0 -119683900,Dimitar Krastanov,BUL,male,1994-01-31,1.81,71.0,modern pentathlon,0,0,0 -536351159,Dimitri Bascou,FRA,male,1987-07-20,1.81,80.0,athletics,0,0,1 -913943115,Dimitri Juliet,NED,male,1996-03-28,1.81,80.0,athletics,0,0,0 -173674343,Dimitriana Surdu,MDA,female,1994-04-12,1.74,93.0,athletics,0,0,0 -158604912,Dimitrij Ovtcharov,GER,male,1988-09-02,1.86,78.0,table tennis,0,0,1 -391014162,Dimitrije Grgic,SRB,male,1984-06-22,1.91,82.0,shooting,0,0,0 -173609909,Dimitrios Antoniadis,GRE,male,1992-07-29,1.8,69.0,cycling,0,0,0 -791397203,Dimitrios Chondrokoukis,CYP,male,1988-01-26,1.94,74.0,athletics,0,0,0 -989537292,Dimitrios Dimitriou,GRE,male,1997-07-31,1.79,72.0,aquatics,0,0,0 -575914740,Dimitrios Koulouris,GRE,male,1991-04-22,1.83,79.0,aquatics,0,0,0 -761663615,Dimitriy Timchenko,UKR,male,1983-04-01,1.9,98.0,wrestling,0,0,0 -894060489,Dimitry Volkov,RUS,male,1995-05-25,2.01,88.0,volleyball,0,0,0 -871164091,Dina Asher-Smith,GBR,female,1995-12-04,1.64,55.0,athletics,0,0,1 -265627311,Dina Lebo Phalula,RSA,female,1983-12-09,1.65,46.0,athletics,0,0,0 -705775098,Dina Meshref,EGY,female,1994-03-10,1.66,59.0,table tennis,0,0,0 -717089687,Ding Chen,CHN,male,1992-08-05,1.75,62.0,athletics,0,0,0 -350064004,Diodio Diouf,SEN,female,1984-12-15,1.7,70.0,basketball,0,0,0 -176134705,Diogo Abreu,POR,male,1993-09-05,1.84,75.0,gymnastics,0,0,0 -989520284,Diogo Carvalho,POR,male,1988-03-26,1.84,75.0,aquatics,0,0,0 -709072877,Diogo Kent Hubner,BRA,male,1983-01-30,1.83,93.0,handball,0,0,0 -202401166,Diogo Sclebin,BRA,male,1982-05-06,1.9,80.0,triathlon,0,0,0 -778633221,Dion Dreesens,NED,male,1993-04-30,1.95,88.0,aquatics,0,0,0 -521343807,Dionisio Augustine Ii,FSM,male,1992-06-16,1.53,65.0,aquatics,0,0,0 -215603505,Dionysios Angelopoulos,GRE,male,1992-08-05,1.89,91.0,rowing,0,0,0 -727060365,Dipa Karmakar,IND,female,1993-08-09,1.51,47.0,gymnastics,0,0,0 -984475243,Dirk Uittenbogaard,NED,male,1990-05-08,1.99,93.0,rowing,0,0,1 -460186785,Dirk van Tichelt,BEL,male,1984-06-10,1.73,73.0,judo,0,0,1 -276444388,Dirngulbai Misech,PLW,female,1997-09-27,1.65,51.0,aquatics,0,0,0 -158299289,Dival Forele Malonga Dzalamou,CGO,male,1995-04-18,,,boxing,0,0,0 -410843536,Diyorbek Urozboev,UZB,male,1993-08-17,1.72,60.0,judo,0,0,1 -640654202,Djenebou Dante,MLI,female,1989-08-07,1.76,73.0,athletics,0,0,0 -208714960,Dmitri Barsuk,RUS,male,1980-01-20,1.94,96.0,volleyball,0,0,0 -486721254,Dmitrii Ushakov,RUS,male,1988-08-15,1.77,65.0,gymnastics,0,0,0 -980454993,Dmitrij Prokopcov,CZE,male,1980-01-05,1.85,78.0,table tennis,0,0,0 -217286137,Dmitriy Balandin,KAZ,male,1995-04-04,1.95,90.0,aquatics,1,0,0 -291341996,Dmitriy Koblov,KAZ,male,1992-11-30,1.83,82.0,athletics,0,0,0 -625508487,Dmitriy Shokin,UZB,male,1992-05-30,1.93,97.0,taekwondo,0,0,0 -19397250,Dmitry Kroyter,ISR,male,1993-02-18,1.87,73.0,athletics,0,0,0 -130360259,Dmitry Larionov,RUS,male,1985-12-22,1.78,84.0,canoe,0,0,0 -735957750,Dmitry Polyanskiy,RUS,male,1986-11-19,1.82,69.0,triathlon,0,0,0 -213236578,Dmitry Zherebchenko,RUS,male,1989-06-27,1.85,78.0,fencing,0,0,0 -130401664,Dmytro Chumak,UKR,male,1990-07-11,1.71,94.0,weightlifting,0,0,0 -168429293,Dmytro Ianchuk,UKR,male,1992-11-14,1.84,75.0,canoe,0,0,1 -834111099,Dmytro Karyuchenko,UKR,male,1980-01-15,1.79,79.0,fencing,0,0,0 -271182574,Dmytro Kosynskyy,UKR,male,1989-03-31,1.98,105.0,athletics,0,0,0 -229107456,Dmytro Mikhay,UKR,male,1990-02-27,1.95,96.0,rowing,0,0,0 -744288493,Dmytro Mytrofanov,UKR,male,1989-11-08,1.73,,boxing,0,0,0 -860594469,Dmytro Oseledets,UKR,male,1994-11-23,1.87,82.0,aquatics,0,0,0 -460602290,Dmytro Yakovenko,UKR,male,1992-09-17,1.92,70.0,athletics,0,0,0 -608020695,Doaa Elghobashy,EGY,female,1996-11-08,,,volleyball,0,0,0 -838189137,Dolores Hernandez,MEX,female,1997-05-21,1.62,62.0,aquatics,0,0,0 -932071837,Dolores Moreira Fraschini,URU,female,1999-02-16,1.68,63.0,sailing,0,0,0 -657952342,Domagoj Duvnjak,CRO,male,1988-06-01,1.98,100.0,handball,0,0,0 -733397080,Domantas Sabonis,LTU,male,1996-05-03,2.08,109.0,basketball,0,0,0 -451025767,Domenic Weinstein,GER,male,1994-08-27,1.88,83.0,cycling,0,0,0 -840838621,Domenico Montrone,ITA,male,1986-05-01,1.89,97.0,rowing,0,0,1 -209270839,Dominic Dugasse,SEY,male,1985-04-19,1.8,99.0,judo,0,0,0 -263640327,Dominic Inglot,GBR,male,1986-03-06,1.98,95.0,tennis,0,0,0 -840913101,Dominic King,GBR,male,1983-05-30,1.78,69.0,athletics,0,0,0 -342699829,Dominik Distelberger,AUT,male,1990-03-16,1.86,81.0,athletics,0,0,0 -330603205,Dominik Kozma,HUN,male,1991-04-10,1.91,90.0,aquatics,0,0,0 -726300964,Dominique Bouchard,CAN,female,1991-05-29,1.74,61.0,aquatics,0,0,0 -70068464,Dominique Scott,RSA,female,1992-06-24,1.66,52.0,athletics,0,0,0 -152688631,Dominykas Jancionis,LTU,male,1993-02-28,1.92,95.0,rowing,0,0,0 -960157104,Domonic Bedggood,AUS,male,1994-09-18,1.63,55.0,aquatics,0,0,0 -671503207,Donald Blair-Sanford,ISR,male,1987-02-05,1.96,81.0,athletics,0,0,0 -93583120,Donald Cabral,USA,male,1989-12-12,1.78,68.0,athletics,0,0,0 -714613268,Donald Thomas,BAH,male,1984-07-10,1.91,81.0,athletics,0,0,0 -971791407,Donata Rimshaite,RUS,female,1988-01-29,1.76,62.0,modern pentathlon,0,0,0 -215897620,Donata Vistartaite,LTU,female,1989-06-11,1.7,62.0,rowing,0,0,1 -658816074,Dong Dong,CHN,male,1989-04-13,1.68,56.0,gymnastics,0,1,0 -825718298,Dong Keun Lee,KOR,male,1990-11-20,1.83,78.0,badminton,0,0,0 -200290928,Donghan Gwak,KOR,male,1992-04-20,1.83,90.0,judo,0,0,1 -531161609,Donghyen Shin,KOR,male,1989-09-23,1.66,,gymnastics,0,0,0 -78993724,Dongjin Kang,KOR,male,1987-12-23,1.68,76.0,cycling,0,0,0 -929646844,Dongjin Park,KOR,male,1994-12-10,1.82,73.0,football,0,0,0 -282895929,Dongju Yu,KOR,male,1993-08-19,1.75,84.0,weightlifting,0,0,0 -580113738,Dongjun Kim,KOR,male,1994-12-19,1.88,84.0,football,0,0,0 -48515460,Donglun Song,CHN,female,1991-04-28,1.78,83.0,aquatics,0,0,0 -375732071,Dongmin Cha,KOR,male,1986-08-24,1.9,91.0,taekwondo,0,0,1 -896828538,Dongna Li,CHN,female,1988-12-06,1.7,58.0,football,0,0,0 -321880190,Dongseon Kim,KOR,male,1989-05-30,1.89,80.0,equestrian,0,0,0 -709580436,Dongxiao Li,CHN,female,1987-11-26,1.75,60.0,hockey,0,0,0 -259066180,Dongyan Huang,CHN,female,1993-12-14,1.72,63.0,cycling,0,0,0 -108660563,Dongyong Kim,KOR,male,1990-12-12,1.89,91.0,rowing,0,0,0 -328804448,Donis Escober,HON,male,1981-02-03,1.8,85.0,football,0,0,0 -87257453,Donna Vakalis,CAN,female,1979-12-30,1.64,53.0,modern pentathlon,0,0,0 -798030855,Dora Antal,HUN,female,1993-09-09,1.69,62.0,aquatics,0,0,0 -963025022,Dora Csabai,HUN,female,1989-04-20,1.75,63.0,aquatics,0,0,0 -702170084,Dora Czigany,HUN,female,1992-10-23,1.73,60.0,aquatics,0,0,0 -690232757,Dorcas Gyimah,GHA,female,1992-02-02,,,athletics,0,0,0 -853951269,Doreen Amata,NGR,female,1988-05-06,1.91,63.0,athletics,0,0,0 -669652246,Doreen Nziwa,KEN,female,1982-07-04,1.57,70.0,rugby sevens,0,0,0 -772686625,Dorian Coninx,FRA,male,1994-01-28,1.81,70.0,triathlon,0,0,0 -810821583,Dorian McMenemy Taylor,DOM,female,1996-10-28,1.86,55.0,aquatics,0,0,0 -151172719,Dorian Mortelette,FRA,male,1983-11-24,1.95,90.0,rowing,0,0,0 -863768656,Dorian van Rijsselberghe,NED,male,1988-11-24,1.89,75.0,sailing,1,0,0 -803889541,Doris Esmid Patino Marin,COL,female,1986-05-01,1.65,56.0,taekwondo,0,0,0 -606690650,Dorlan Pabon,COL,male,1988-01-24,1.68,68.0,football,0,0,0 -705883917,Dorothea Brandt,GER,female,1984-03-05,1.79,67.0,aquatics,0,0,0 -307716893,Dorothee Schneider,GER,female,1969-02-17,1.69,63.0,equestrian,1,0,0 -632435029,Dorothy Erzsebet Yeats,CAN,female,1993-07-29,1.6,71.0,wrestling,0,0,0 -494719089,Doston Yokubov,UZB,male,1995-04-05,1.58,69.0,weightlifting,0,0,0 -673086205,Doszhan Kartikov,KAZ,male,1989-05-24,1.73,75.0,wrestling,0,0,0 -723331849,Douglas Correia de Souza,BRA,male,1995-08-20,1.99,75.0,volleyball,1,0,0 -281803950,Douglas John Erasmus,RSA,male,1990-04-04,1.82,78.0,aquatics,0,0,0 -456548285,Douglas Santos,BRA,male,1994-03-22,1.73,69.0,football,1,0,0 -825814008,Dovydas Nemeravicius,LTU,male,1996-12-11,1.93,98.0,rowing,0,0,0 -285011612,Dragana Stankovic,SRB,female,1995-01-18,1.95,73.0,basketball,0,0,1 -225958644,Dragana Tomasevic,SRB,female,1982-06-04,1.75,80.0,athletics,0,0,0 -402567927,Drasko Brguljan,MNE,male,1984-12-27,1.94,92.0,aquatics,0,0,0 -66675675,Draymond Green,USA,male,1990-03-04,2.0,104.0,basketball,1,0,0 -86318895,Driss Lahrichi,MAR,male,1997-12-02,1.9,83.0,aquatics,0,0,0 -388102468,Drita Islami,MKD,female,1996-08-01,1.66,56.0,athletics,0,0,0 -226875071,Duanbin Ma,CHN,male,1990-03-28,1.74,66.0,judo,0,0,0 -236093323,Duane da Rocha Marce,ESP,female,1988-01-07,1.8,59.0,aquatics,0,0,0 -421676120,Dudi Sela,ISR,male,1985-04-04,1.75,67.0,tennis,0,0,0 -754252198,Dulguun Batsaikhan,MGL,male,1986-10-26,1.79,72.0,aquatics,0,0,0 -219999872,Dumitru Captari,ROU,male,1989-07-12,1.68,77.0,weightlifting,0,0,0 -327966166,Duncan Scott,GBR,male,1997-05-06,1.91,74.0,aquatics,0,2,0 -643802255,Dunhan Xiong,CHN,female,1998-11-11,1.81,83.0,aquatics,0,0,0 -154604483,Duo Shen,CHN,female,1997-06-09,1.81,74.0,aquatics,0,0,0 -3901892,Duobujie,CHN,male,1994-02-16,1.75,55.0,athletics,0,0,0 -131271973,Durdina Jaukovic,MNE,female,1997-02-24,1.86,86.0,handball,0,0,0 -596370468,Dusan Majdan,SVK,male,1987-09-08,1.8,67.0,athletics,0,0,0 -265536386,Dusan Mandic,SRB,male,1994-06-16,2.02,105.0,aquatics,1,0,0 -386288896,Dusko Pijetlovic,SRB,male,1985-04-25,1.97,97.0,aquatics,1,0,0 -521744837,Dustin Brown,GER,male,1984-12-08,1.96,78.0,tennis,0,0,0 -417996132,Dustin Tynes,BAH,male,1996-03-07,1.85,75.0,aquatics,0,0,0 -737107607,Dutee Chand,IND,female,1996-02-03,1.6,50.0,athletics,0,0,0 -47572256,Duygu Aynaci,TUR,female,1996-06-26,1.7,69.0,weightlifting,0,0,0 -578322269,Dwight Lewis,VEN,male,1987-10-07,1.98,,basketball,0,0,0 -870355208,Dylan Borlee,BEL,male,1992-09-20,1.9,78.0,athletics,0,0,0 -806170062,Dylan Bosch,RSA,male,1993-07-17,1.78,75.0,aquatics,0,0,0 -348929342,Dylan Carter,TTO,male,1996-01-30,,,aquatics,0,0,0 -829969108,Dylan Fletcher-Scott,GBR,male,1988-04-03,1.76,78.0,sailing,0,0,0 -965509678,Dylan Kennett,NZL,male,1994-12-08,1.78,75.0,cycling,0,0,0 -168131726,Dylan Sage,RSA,male,1992-01-24,1.88,96.0,rugby sevens,0,0,1 -5064348,Dylan Schmidt,NZL,male,1997-01-07,1.69,69.0,gymnastics,0,0,0 -140395363,Dzianis Mihal,BLR,male,1985-10-05,1.97,102.0,rowing,0,0,0 -186360775,Dzianis Simanovich,BLR,male,1987-04-20,1.78,64.0,athletics,0,0,0 -733134503,Dzmitry Asanau,BLR,male,1996-05-18,1.76,,boxing,0,0,0 -165309696,Dzmitry Nabokau,BLR,male,1996-01-20,1.86,69.0,athletics,0,0,0 -811378549,Dzmitry Platnitski,BLR,male,1988-08-26,1.91,83.0,athletics,0,0,0 -890120852,Dzmitry Shershan,BLR,male,1988-12-28,1.7,66.0,judo,0,0,0 -259130059,Dzsenifer Marozsan,GER,female,1992-04-18,1.71,55.0,football,1,0,0 -557320685,Earvin Ngapeth,FRA,male,1991-02-12,1.94,101.0,volleyball,0,0,0 -340813269,Ebi Ere,NGR,male,1981-08-03,1.96,97.0,basketball,0,0,0 -559696143,Ebtissam Mohamed,EGY,female,1996-09-25,1.63,54.0,cycling,0,0,0 -609618131,Ecaterina Guica,CAN,female,1993-10-09,1.65,52.0,judo,0,0,0 -507856515,Ed Jenkins,AUS,male,1986-05-26,1.84,96.0,rugby sevens,0,0,0 -250017195,Eddie Lovett,ISV,male,1992-06-25,1.8,73.0,athletics,0,0,0 -532630723,Eddy Yusof,SUI,male,1994-10-02,1.65,63.0,gymnastics,0,0,0 -159991654,Edel R. Amores,CUB,male,1998-10-05,1.81,72.0,athletics,0,0,0 -207619796,Eder Antonio Souza,BRA,male,1986-10-15,1.89,89.0,athletics,0,0,0 -87070724,Eder Carbonera,BRA,male,1983-10-19,2.05,107.0,volleyball,1,0,0 -605245129,Edgar Contreras,VEN,male,1992-07-16,1.78,67.0,taekwondo,0,0,0 -659584369,Edgar Crespo,PAN,male,1989-05-11,1.78,83.0,aquatics,0,0,0 -870599137,Edgar Ie,POR,male,1994-05-01,1.88,80.0,football,0,0,0 -487798358,Edgar Pineda Zeta,GUA,male,1997-08-17,1.62,56.0,weightlifting,0,0,0 -684979118,Edgar Ramon Munoz,VEN,male,1983-12-22,1.84,,boxing,0,0,0 -795195154,Edgar Rivera,MEX,male,1991-02-13,1.98,84.0,athletics,0,0,0 -709871920,Edgaras Venckaitis,LTU,male,1985-12-12,1.71,70.0,wrestling,0,0,0 -334864528,Edidiong Ofonime Odiong,BRN,female,1997-03-13,1.67,58.0,athletics,0,0,0 -285211262,Edigerson Gomes,DEN,male,1988-11-17,1.92,91.0,football,0,0,0 -32938884,Edina Gangl,HUN,female,1990-06-25,1.81,64.0,aquatics,0,0,0 -889715438,Edina Knapek,HUN,female,1977-10-05,1.66,53.0,fencing,0,0,0 -937748261,Edna Carrillo,MEX,female,1991-11-12,1.52,48.0,judo,0,0,0 -36037343,Edna Santini,BRA,female,1992-07-15,1.53,54.0,rugby sevens,0,0,0 -82126746,Edouard Joseph,HAI,male,1989-12-24,1.52,60.0,weightlifting,0,0,0 -34157460,Edson Isaias Freitas da Silva,BRA,male,1982-03-25,1.7,83.0,canoe,0,0,0 -906489293,Eduard Popp,GER,male,1991-06-16,1.9,128.0,wrestling,0,0,0 -475508680,Eduard Soghomonyan,BRA,male,1990-02-19,1.9,120.0,wrestling,0,0,0 -129556229,Eduarda Taleska,BRA,female,1986-09-23,1.86,84.0,handball,0,0,0 -757748599,Eduardo Alvarez Aznar,ESP,male,1984-01-01,1.73,67.0,equestrian,0,0,0 -454899916,Eduardo Jose Lorenzo,DOM,male,1966-08-31,1.71,84.0,shooting,0,0,0 -543622278,Eduardo Menezes,BRA,male,1980-05-01,1.93,84.0,equestrian,0,0,0 -495156314,Eduardo Rubio Rodriguez,CUB,male,1986-11-13,1.87,88.0,rowing,0,0,0 -367849834,Eduardo Sepulveda,ARG,male,1991-06-13,1.73,63.0,cycling,0,0,0 -943462468,Eduardo Solaeche Gomez,ESP,male,1993-11-22,1.83,79.0,aquatics,0,0,0 -706338154,Edvald Boasson Hagen,NOR,male,1987-05-17,,,cycling,0,0,0 -490010499,Edvinas Ramanauskas,LTU,male,1985-08-18,1.84,90.0,canoe,0,0,1 -687926847,Edward Araya,CHI,male,1986-02-14,1.76,62.0,athletics,0,0,0 -128111311,Edward Clancy,GBR,male,1985-03-12,1.85,79.0,cycling,1,0,0 -461207068,Edward Dawkins,NZL,male,1989-07-11,1.85,93.0,cycling,0,1,0 -430473716,Edward Gal,NED,male,1970-03-04,1.82,72.0,equestrian,0,0,0 -906594925,Edward King,USA,male,1989-06-14,1.94,77.0,rowing,0,0,0 -773652223,Edward Ling,GBR,male,1983-03-07,1.8,80.0,shooting,0,0,1 -596469056,Edward Ockenden,AUS,male,1987-04-03,1.8,73.0,hockey,0,0,0 -466051917,Edwige Gwend,ITA,female,1990-03-11,1.63,63.0,judo,0,0,0 -148140488,Edwin Orlando Mosquera Roa,COL,male,1985-07-26,1.65,69.0,weightlifting,0,0,0 -40681851,Edwina Bone,AUS,female,1988-04-29,1.7,70.0,hockey,0,0,0 -776649642,Edwina Tops-Alexander,AUS,female,1974-03-29,1.64,50.0,equestrian,0,0,0 -103003016,Edyta Dzieniszewska-Kierkla,POL,female,1986-05-05,1.7,72.0,canoe,0,0,0 -32569642,Edyta Jasinska,POL,female,1986-11-28,1.77,68.0,cycling,0,0,0 -594266311,Edzus Treimanis,LAT,male,1988-04-21,1.83,82.0,cycling,0,0,0 -249357704,Eefje Muskens,NED,female,1989-06-17,1.64,60.0,badminton,0,0,0 -416257556,Eelco Sintnicolaas,NED,male,1987-04-07,1.86,80.0,athletics,0,0,0 -583999455,Eeseul Baek,KOR,female,1994-10-04,1.75,72.0,hockey,0,0,0 -293548179,Efe Ajagba,NGR,male,1994-04-22,1.9,,boxing,0,0,0 -850581667,Efthimios Mitas,GRE,male,1985-05-15,1.9,105.0,shooting,0,0,0 -647057378,Egle Balciunaite,LTU,female,1988-10-31,1.76,63.0,athletics,0,0,0 -798534608,Eglys Yahima de la Cruz,CUB,female,1980-04-12,1.59,63.0,shooting,0,0,0 -238009548,Egor Kliuka,RUS,male,1995-06-15,2.08,93.0,volleyball,0,0,0 -878921230,Ehsan Hadadi,IRI,male,1985-01-20,1.92,120.0,athletics,0,0,0 -184575330,Ehsan Rouzbahani,IRI,male,1988-06-23,1.84,,boxing,0,0,0 -181030927,Ei Ei Thet,MYA,female,1992-12-13,,,aquatics,0,0,0 -797663283,Eider Arevalo,COL,male,1993-03-09,1.65,60.0,athletics,0,0,0 -966174315,Eike Onnen,GER,male,1982-08-03,1.97,92.0,athletics,0,0,0 -407225209,Eiki Takahashi,JPN,male,1992-11-19,1.76,58.0,athletics,0,0,0 -246556488,Eileen Grench,PAN,female,1986-08-05,1.52,59.0,fencing,0,0,0 -787671034,Eilidh Doyle,GBR,female,1987-02-20,1.7,60.0,athletics,0,0,1 -449582675,Eilish McColgan,GBR,female,1990-11-25,1.76,53.0,athletics,0,0,0 -974051662,Eimantas Stanionis,LTU,male,1994-08-17,1.73,,boxing,0,0,0 -58235529,Eirini-Marina Alexandri,AUT,female,1997-09-15,1.71,50.0,aquatics,0,0,0 -395251162,Eita Mori,JPN,male,1983-04-13,1.73,76.0,shooting,0,0,0 -381623095,Ejowvokoghene Oduduru,NGR,male,1996-10-07,,70.0,athletics,0,0,0 -786450061,Ekaterina Birlova,RUS,female,1987-08-11,1.8,62.0,volleyball,0,0,0 -882206907,Ekaterina Bukina,RUS,female,1987-05-05,1.74,75.0,wrestling,0,0,1 -358786472,Ekaterina Dyachenko,RUS,female,1987-08-31,1.67,53.0,fencing,1,0,0 -409581513,Ekaterina Ilina,RUS,female,1991-03-07,1.74,60.0,handball,1,0,0 -323828574,Ekaterina Ivanova Avramova,TUR,female,1991-11-12,1.8,69.0,aquatics,0,0,0 -516714653,Ekaterina Karsten,BLR,female,1972-06-02,1.85,81.0,rowing,0,0,0 -121281813,Ekaterina Khilko,UZB,female,1982-03-25,1.63,50.0,gymnastics,0,0,0 -905380582,Ekaterina Korshunova,RUS,female,1988-05-24,1.61,69.0,shooting,0,0,0 -335759270,Ekaterina Kosianenko,RUS,female,1990-02-02,1.78,64.0,volleyball,0,0,0 -392249714,Ekaterina Levina,ISR,female,1997-02-01,1.68,52.0,gymnastics,0,0,0 -443958002,Ekaterina Lisunova,RUS,female,1989-10-06,1.75,64.0,aquatics,0,0,1 -257253481,Ekaterina Makarova,RUS,female,1988-06-07,1.8,67.0,tennis,1,0,0 -472046862,Ekaterina Marennikova,RUS,female,1982-04-29,1.76,70.0,handball,1,0,0 -892190184,Ekaterina Petukhova,RUS,female,1996-06-16,1.63,48.0,aquatics,0,0,0 -121727222,Ekaterina Poplavskaya,BLR,female,1987-05-07,1.69,61.0,athletics,0,0,0 -814297240,Ekaterina Prokofyeva,RUS,female,1991-03-13,1.76,70.0,aquatics,0,0,1 -894255038,Ekaterina Rabaya,RUS,female,1993-11-06,1.58,60.0,shooting,0,0,0 -271247492,Ekaterina Tunguskova,UZB,female,1988-05-10,,,athletics,0,0,0 -11854539,Ekaterina Valkova,RUS,female,1991-05-17,1.67,63.0,judo,0,0,0 -366635571,Ekaterina Volkova,FIN,female,1997-07-02,1.68,48.0,gymnastics,0,0,0 -881845310,Ekaterina Voronina,UZB,female,1992-02-16,1.73,65.0,athletics,0,0,0 -226774163,Ekaterini Stefanidi,GRE,female,1990-02-04,1.73,59.0,athletics,1,0,0 -359490730,Ekenechukwu Ibekwe,NGR,male,1985-07-19,2.1,100.0,basketball,0,0,0 -967379890,Eko Yuli Irawan,INA,male,1989-07-24,1.54,62.0,weightlifting,0,1,0 -45507177,El Hadi Laameche,ALG,male,1990-03-05,1.65,60.0,athletics,0,0,0 -964251417,El Hassan Elabbassi,BRN,male,1984-04-13,1.71,61.0,athletics,0,0,0 -55190469,El Mahadi Messaoudi,MAR,male,1990-02-12,1.68,59.0,wrestling,0,0,0 -153066900,Elaheh Ahmadi,IRI,female,1982-05-31,1.6,62.0,shooting,0,0,0 -715359899,Elaine Thompson,JAM,female,1992-06-28,1.67,57.0,athletics,2,1,0 -320218359,Eldar Memisevic,QAT,male,1992-06-21,1.78,77.0,handball,0,0,0 -673973096,Elder Torres,HON,male,1995-04-14,1.76,69.0,football,0,0,0 -969535461,Eldred Henry,IVB,male,1994-09-18,,95.0,athletics,0,0,0 -777815800,Elea Mariama Diarra,FRA,female,1990-03-08,1.76,56.0,athletics,0,0,0 -205100723,Eleanor Bezzina,MLT,female,1977-03-25,1.54,,shooting,0,0,0 -513927497,Eleanor Harvey,CAN,female,1995-01-14,1.72,56.0,fencing,0,0,0 -518148894,Eleanor Logan,USA,female,1987-12-27,1.88,86.0,rowing,1,0,0 -473616854,Eleanor Patterson,AUS,female,1996-05-22,1.82,65.0,athletics,0,0,0 -846855602,Eleanor Watton,GBR,female,1989-06-10,1.67,64.0,hockey,0,0,0 -686764746,Eleftherios Petrounias,GRE,male,1990-11-30,1.64,62.0,gymnastics,1,0,0 -82665159,Elena Allen,GBR,female,1972-07-12,1.69,68.0,shooting,0,0,0 -222839663,Elena Aniushina,RUS,female,1993-12-08,,,canoe,0,0,0 -365519305,Elena Berta,ITA,female,1992-07-15,1.71,58.0,sailing,0,0,0 -869725095,Elena Cecchini,ITA,female,1992-05-25,1.68,55.0,cycling,0,0,0 -648308415,Elena Delle Donne,USA,female,1989-09-05,1.95,86.0,basketball,1,0,0 -13567083,Elena Ezhova,RUS,female,1977-08-14,1.78,69.0,volleyball,0,0,0 -137876590,Elena Galiabovitch,AUS,female,1989-11-13,1.77,,shooting,0,0,0 -240603445,Elena Lopez,ESP,female,1994-10-04,1.69,51.0,gymnastics,0,1,0 -971991593,Elena Maria Bonfanti,ITA,female,1988-07-09,1.72,52.0,athletics,0,0,0 -338111433,Elena Panturoiu,ROU,female,1995-02-24,1.7,57.0,athletics,0,0,0 -806464803,Elena Potapenko,KAZ,female,1993-04-20,1.65,53.0,modern pentathlon,0,0,0 -38343268,Elena Prokofyeva,RUS,female,1994-08-02,1.69,54.0,aquatics,1,0,0 -763314251,Elena Sergey Pirozhkova,USA,female,1986-10-13,1.68,71.0,wrestling,0,0,0 -160971394,Elena Vesnina,RUS,female,1986-08-01,1.76,66.0,tennis,1,0,0 -290451814,Elena Wassen,GER,female,2000-11-01,1.74,63.0,aquatics,0,0,0 -347167586,Elena-Lavinia Tarlea,ROU,female,1993-05-11,1.82,76.0,rowing,0,0,0 -173424444,Eleni Artymata,CYP,female,1986-05-16,1.77,62.0,athletics,0,0,0 -820277604,Eleni Doika,GRE,female,1995-11-15,1.7,55.0,gymnastics,0,0,0 -178710310,Eleonora Giorgi,ITA,female,1989-09-14,1.62,52.0,athletics,0,0,0 -541439110,Eleonora Lo Bianco,ITA,female,1979-12-22,1.71,67.0,volleyball,0,0,0 -985507855,Eli Dershwitz,USA,male,1995-09-23,1.86,77.0,fencing,0,0,0 -307747512,Elia Viviani,ITA,male,1989-02-07,1.77,70.0,cycling,1,0,0 -471696699,Eliane Martins,BRA,female,1986-05-26,1.6,49.0,athletics,0,0,0 -28171198,Eliane Saholinirina,MAD,female,1982-03-20,1.54,46.0,athletics,0,0,0 -15776102,Elias Eliseo Emigdio Abarca,MEX,male,1991-06-13,1.65,,boxing,0,0,0 -141627848,Elias Malave,VEN,male,1989-10-26,1.8,87.0,archery,0,0,0 -592765424,Elie Konki,FRA,male,1992-04-06,1.7,,boxing,0,0,0 -92990093,Eliecith Palacios,COL,female,1987-09-15,1.7,64.0,athletics,0,0,0 -789410278,Elif Jale Yesilirmak,TUR,female,1986-07-30,1.64,58.0,wrestling,0,0,0 -301067341,Elijah Motonei Manangoi,KEN,male,1993-01-05,1.85,62.0,athletics,0,0,0 -678119617,Elin Johansson,SWE,female,1990-08-05,1.76,67.0,taekwondo,0,0,0 -875252627,Elin Rubensson,SWE,female,1993-05-11,1.66,59.0,football,0,1,0 -172321951,Elina Mikhina,KAZ,female,1994-07-16,1.7,55.0,athletics,0,0,0 -198751525,Elina Svitolina,UKR,female,1994-09-12,1.74,60.0,tennis,0,0,0 -731624234,Elinah Phillip,IVB,female,2000-04-03,1.64,50.0,aquatics,0,0,0 -460751397,Elinor Barker,GBR,female,1994-09-07,1.68,56.0,cycling,1,0,0 -287409075,Elios Manzi,ITA,male,1996-03-28,1.55,60.0,judo,0,0,0 -768759366,Elis Guri,BUL,male,1983-07-06,1.89,100.0,wrestling,0,0,0 -613098347,Elis Ligtlee,NED,female,1994-06-28,1.85,90.0,cycling,1,0,0 -564832940,Elisa Bozzo,ITA,female,1987-05-08,1.7,59.0,aquatics,0,0,0 -757900456,Elisa Longo Borghini,ITA,female,1991-12-10,1.7,59.0,cycling,0,0,1 -12802833,Elisa Meneghini,ITA,female,1997-07-24,1.5,42.0,gymnastics,0,0,0 -732211683,Elisa Queirolo,ITA,female,1991-03-06,1.68,61.0,aquatics,0,1,0 -322088639,Elisa Rigaudo,ITA,female,1980-06-17,1.68,53.0,athletics,0,0,0 -7294908,Elisa Vania Ravololoniaina,MAD,female,1992-02-24,1.65,62.0,weightlifting,0,0,0 -668488790,Elisa di Francisca,ITA,female,1982-12-12,1.77,65.0,fencing,0,1,0 -299509346,Elisabet Martinez,ESP,female,1988-06-13,1.67,73.0,rugby sevens,0,0,0 -587801561,Elisabeth Baldauf,AUT,female,1990-08-03,1.75,62.0,badminton,0,0,0 -695086866,Elisabeth Mandaba,CAF,female,1989-06-07,1.33,53.0,athletics,0,0,0 -4144486,Elisabeth Seitz,GER,female,1993-11-04,1.61,57.0,gymnastics,0,0,0 -880954077,Elisavet Pesiridou,GRE,female,1992-02-12,1.75,58.0,athletics,0,0,0 -608164942,Elisbet Games,CUB,female,1997-01-17,1.63,57.0,aquatics,0,0,0 -855475470,Elise Bussaglia,FRA,female,1985-09-24,1.63,54.0,football,0,0,0 -721445990,Elise Kellond-Knight,AUS,female,1990-08-10,1.65,60.0,football,0,0,0 -606790581,Eliska Klucinova,CZE,female,1988-04-14,1.78,70.0,athletics,0,0,0 -626906176,Elissa Downie,GBR,female,1999-07-20,1.58,58.0,gymnastics,0,0,0 -163054373,Elitsa Atanasova Yankova,BUL,female,1994-09-18,1.51,48.0,wrestling,0,0,1 -348703893,Eliud Kipchoge,KEN,male,1984-11-05,1.67,57.0,athletics,1,0,0 -900146374,Eliza Buceschi,ROU,female,1993-08-01,1.78,70.0,handball,0,0,0 -388879004,Eliza McCartney,NZL,female,1996-12-11,1.79,66.0,athletics,0,0,1 -826192606,Elizabet Chavez,ESP,female,1990-11-17,1.92,81.0,handball,0,0,0 -148313722,Elizabeta Samara,ROU,female,1989-04-15,1.71,56.0,table tennis,0,0,0 -56829234,Elizabeth Armitstead,GBR,female,1988-12-18,1.68,55.0,cycling,0,0,0 -993687711,Elizabeth Beisel,USA,female,1992-08-18,1.68,63.0,aquatics,0,0,0 -602061131,Elizabeth Bravo,ECU,female,1987-01-30,1.6,49.0,triathlon,0,0,0 -972005688,Elizabeth Cambage,AUS,female,1991-08-18,2.03,98.0,basketball,0,0,0 -50104802,Elizabeth Cui,NZL,female,1997-08-12,1.59,58.0,aquatics,0,0,0 -218669819,Elizabeth Gleadle,CAN,female,1988-12-05,1.85,79.0,athletics,0,0,0 -800526632,Elizabeth Gunson,NZL,female,1989-07-09,1.62,62.0,hockey,0,0,0 -180787419,Elizabeth Keddell,NZL,female,1994-01-31,1.69,67.0,hockey,0,0,0 -33326551,Elizabeth Madden,USA,female,1963-11-20,1.68,63.0,equestrian,0,1,0 -942647059,Elizabeth Pinedo,ESP,female,1981-05-13,1.75,68.0,handball,0,0,0 -819052934,Elizabeth Thompson,NZL,female,1994-12-08,1.75,71.0,hockey,0,0,0 -340978439,Elizabeth Yin,SIN,female,1991-08-08,1.67,70.0,sailing,0,0,0 -87758456,Elizbar Odikadze,GEO,male,1989-06-14,1.87,105.0,wrestling,0,0,0 -442945772,Elke Karsten,ARG,female,1995-05-15,1.75,65.0,handball,0,0,0 -184528790,Elke Vanhoof,BEL,female,1991-12-16,1.63,60.0,cycling,0,0,0 -1009342,Ella Nelson,AUS,female,1994-05-10,1.69,58.0,athletics,0,0,0 -657233215,Ella Nicholas,COK,female,1990-12-15,1.6,62.0,canoe,0,0,0 -312672556,Ellen Hogerwerf,NED,female,1989-02-10,1.83,72.0,rowing,0,0,0 -769513585,Ellen Hoog,NED,female,1986-03-25,1.64,54.0,hockey,0,1,0 -970232379,Ellen Sprunger,SUI,female,1986-08-05,1.72,62.0,athletics,0,0,0 -667380122,Ellen Tomek,USA,female,1984-05-01,1.78,72.0,rowing,0,0,0 -322522581,Ellen van Dijk,NED,female,1987-02-11,1.82,71.0,cycling,0,0,0 -579116180,Ellia Green,AUS,female,1993-02-20,1.72,75.0,rugby sevens,1,0,0 -163208975,Ellie Carpenter,AUS,female,2000-04-28,1.65,60.0,football,0,0,0 -191092318,Ellie Faulkner,GBR,female,1993-01-05,1.65,68.0,aquatics,0,0,0 -164454907,Elliot Giles,GBR,male,1994-05-26,1.83,77.0,athletics,0,0,0 -986117521,Elliot van Strydonck,BEL,male,1988-07-21,1.85,75.0,hockey,0,1,0 -933444753,Ellis Oreilly,IRL,female,1998-02-23,1.64,63.0,gymnastics,0,0,0 -579582876,Elmar Gasimov,AZE,male,1990-11-02,1.88,100.0,judo,0,1,0 -803655195,Elmira Syzdykova,KAZ,female,1992-02-05,1.72,69.0,wrestling,0,0,1 -434777707,Elmo Jankari,FIN,male,1992-10-13,,,equestrian,0,0,0 -285578745,Elmurat Tasmuradov,UZB,male,1991-12-12,1.6,59.0,wrestling,0,0,1 -699007358,Elodie Clouvel,FRA,female,1989-01-14,1.82,69.0,modern pentathlon,0,1,0 -557773110,Elodie Guiglion,FRA,female,1990-01-28,1.66,63.0,rugby sevens,0,0,0 -839282607,Elodie Ravera-Scaramozzino,FRA,female,1995-09-19,1.77,70.0,rowing,0,0,0 -310110976,Elodie Thomis,FRA,female,1986-08-13,1.68,60.0,football,0,0,0 -786667098,Eloi Imaniraguha,RWA,male,1995-01-01,,,aquatics,0,0,0 -366176086,Eloise Wellings,AUS,female,1982-11-09,1.72,52.0,athletics,0,0,0 -339293257,Elroy Gelant,RSA,male,1986-08-25,1.75,61.0,athletics,0,0,0 -95400560,Els Rens,BEL,female,1983-02-19,1.6,51.0,athletics,0,0,0 -584020714,Elsa Baquerizo McMillan,ESP,female,1987-06-25,1.81,68.0,volleyball,0,0,0 -746327154,Elsabeth Black,CAN,female,1995-09-08,1.55,56.0,gymnastics,0,0,0 -31920396,Elshod Rasulov,UZB,male,1986-03-07,1.84,,boxing,0,0,0 -388539796,Elsie Uwamahoro,BDI,female,1988-10-23,1.65,54.0,aquatics,0,0,0 -396471467,Elson Brechtefeld,NRU,male,1994-03-02,1.55,56.0,weightlifting,0,0,0 -821149858,Elvin Mamishzada,AZE,male,1991-12-17,1.65,,boxing,0,0,0 -405623057,Elvin Mursaliyev,AZE,male,1988-08-17,1.78,75.0,wrestling,0,0,0 -357059083,Elvina Karimova,RUS,female,1994-03-25,1.66,62.0,aquatics,0,0,1 -95609795,Elvismar Rodriguez,VEN,female,1997-02-14,1.78,70.0,judo,0,0,0 -584799839,Elyane Boal,CPV,female,1998-04-26,1.7,58.0,gymnastics,0,0,0 -228262342,Ema Ramusovic,MNE,female,1996-11-28,1.83,78.0,handball,0,0,0 -772515939,Emanuel Andrade,VEN,male,1996-09-11,1.89,76.0,equestrian,0,0,0 -829728385,Emanuel Buchmann,GER,male,1992-11-18,1.81,61.0,cycling,0,0,0 -655933510,Emanuel Silva,POR,male,1985-12-04,1.86,87.0,canoe,0,0,0 -937016443,Emanuele Birarelli,ITA,male,1981-02-08,2.02,95.0,volleyball,0,1,0 -728574987,Emanuele Gaudiano,ITA,male,1986-06-30,1.75,70.0,equestrian,0,0,0 -268422927,Emanuele Liuzzi,ITA,male,1990-12-22,1.91,85.0,rowing,0,0,0 -677858337,Emanuelle Lima,BRA,female,1996-05-03,1.67,55.0,gymnastics,0,0,0 -865724111,Emel Dereli,TUR,female,1996-02-25,1.81,110.0,athletics,0,0,0 -987023518,Emelda Piata Zessi,CMR,female,1997-04-08,1.9,65.0,volleyball,0,0,0 -588334076,Emelie Lundberg,SWE,female,1993-03-10,1.73,74.0,football,0,0,0 -468726382,Emerric Kpegba,TOG,male,1999-05-29,,,aquatics,0,0,0 -174328356,Emerson Duarte,BRA,male,1971-10-18,1.82,80.0,shooting,0,0,0 -414726536,Emese Szasz,HUN,female,1982-09-07,1.76,71.0,fencing,1,0,0 -852099973,Emi Nishikori,JPN,female,1993-01-09,1.6,53.0,hockey,0,0,0 -87680964,Emil Larsen,DEN,male,1991-06-22,1.83,79.0,football,0,0,0 -514462575,Emil Milev,USA,male,1968-05-02,1.78,83.0,shooting,0,0,0 -974556928,Emilce Sosa,ARG,female,1987-09-11,1.77,72.0,volleyball,0,0,0 -782571227,Emilee Cherry,AUS,female,1992-11-02,1.68,70.0,rugby sevens,1,0,0 -126099613,Emilia Alina Vuc,ROU,female,1993-10-04,1.54,50.0,wrestling,0,0,0 -348572893,Emilia Ankiewicz,POL,female,1990-11-22,1.78,64.0,athletics,0,0,0 -202538392,Emilia Appelqvist,SWE,female,1990-02-11,1.68,65.0,football,0,1,0 -43289045,Emilia Fahlin,SWE,female,1988-10-24,1.76,62.0,cycling,0,0,0 -136493766,Emilia Pikkarainen,FIN,female,1992-10-11,1.73,56.0,aquatics,0,0,0 -743555148,Emiliano Grillo,ARG,male,1992-09-14,1.78,78.0,golf,0,0,0 -699500168,Emiliano Lasa,URU,male,1990-01-25,1.8,75.0,athletics,0,0,0 -561775789,Emilie Andeol,FRA,female,1987-10-30,1.7,97.0,judo,1,0,0 -339081349,Emilie Fournel,CAN,female,1986-10-26,1.55,59.0,canoe,0,0,0 -542195691,Emilie Hegh Arntzen,NOR,female,1994-01-01,1.83,,handball,0,0,1 -466069449,Emilie Menuet,FRA,female,1991-09-27,1.55,44.0,athletics,0,0,0 -702824407,Emily Batty,CAN,female,1988-06-16,1.61,48.0,cycling,0,0,0 -146940230,Emily Diamond,GBR,female,1991-06-11,1.73,58.0,athletics,0,0,1 -838354626,Emily Gielnik,AUS,female,1992-05-13,,,football,0,0,0 -265521340,Emily Infeld,USA,female,1990-03-21,1.63,49.0,athletics,0,0,0 -746574483,Emily Morley,BAH,female,1993-12-06,1.53,70.0,rowing,0,0,0 -736663132,Emily Overholt,CAN,female,1997-10-04,1.7,60.0,aquatics,0,0,1 -345975199,Emily Regan,USA,female,1988-06-10,1.88,80.0,rowing,1,0,0 -905464042,Emily Rogers,AUS,female,1998-03-25,1.73,52.0,aquatics,0,0,0 -651596941,Emily Scarratt,GBR,female,1990-02-08,1.81,79.0,rugby sevens,0,0,0 -817845839,Emily Scott,GBR,female,1992-06-30,1.65,60.0,rugby sevens,0,0,0 -75025673,Emily Seebohm,AUS,female,1992-06-05,1.8,70.0,aquatics,0,1,0 -763142277,Emily Smith,AUS,female,1992-07-28,1.59,55.0,hockey,0,0,0 -688367620,Emily Sonnett,USA,female,1993-11-25,1.68,60.0,football,0,0,0 -914565471,Emily van Egmond,AUS,female,1993-07-12,1.79,70.0,football,0,0,0 -297385649,Emma Berglund,SWE,female,1988-12-19,1.72,63.0,football,0,1,0 -469541944,Emma Coburn,USA,female,1990-10-19,1.73,54.0,athletics,0,0,1 -325186756,Emma Dyke,NZL,female,1995-06-30,1.81,68.0,rowing,0,0,0 -763495268,Emma Hinze,GER,female,1997-09-17,1.68,63.0,cycling,0,0,0 -87473289,Emma Johansson,SWE,female,1983-09-23,1.68,54.0,cycling,0,1,0 -322314005,Emma Jorgensen,DEN,female,1996-01-30,1.69,70.0,canoe,0,1,0 -717137601,Emma Larsson,SWE,female,1998-11-15,1.47,40.0,gymnastics,0,0,0 -661638106,Emma McKeon,AUS,female,1994-05-24,1.8,60.0,aquatics,1,2,1 -833886353,Emma Moffatt,AUS,female,1984-09-07,1.71,57.0,triathlon,0,0,0 -493087174,Emma Pooley,GBR,female,1982-10-03,1.57,50.0,cycling,0,0,0 -79453290,Emma Robinson,NZL,female,1994-09-26,1.8,72.0,aquatics,0,0,0 -327389917,Emma Tonegato,AUS,female,1995-03-20,1.65,63.0,rugby sevens,1,0,0 -831117796,Emma Twigg,NZL,female,1987-03-01,1.82,76.0,rowing,0,0,0 -737526128,Emmaculate Msipa,ZIM,female,1992-06-07,1.68,59.0,football,0,0,0 -867271718,Emmanouil Mylonakis,GRE,male,1985-04-09,1.85,75.0,aquatics,0,0,0 -796025213,Emmanuel Callender,TTO,male,1984-05-10,1.89,86.0,athletics,0,0,0 -102727571,Emmanuel Daniel,NGR,male,1993-12-17,1.74,,football,0,0,1 -545212161,Emmanuel Dasor,GHA,male,1995-09-14,,,athletics,0,0,0 -585502953,Emmanuel Lebesson,FRA,male,1988-04-24,1.8,75.0,table tennis,0,0,0 -904221545,Emmanuel Lucenti,ARG,male,1984-11-23,1.73,81.0,judo,0,0,0 -364575203,Emmanuel Matadi,LBR,male,1991-04-15,,,athletics,0,0,0 -686447847,Emmanuel Stockbroekx,BEL,male,1993-12-23,1.89,88.0,hockey,0,1,0 -291735162,Emmanuel Vanluchene,BEL,male,1992-12-09,1.8,75.0,aquatics,0,0,0 -578388383,Emmanuel Zapata,ARG,male,1986-10-07,1.8,80.0,modern pentathlon,0,0,0 -238544524,Emre Zafer Barnes,TUR,male,1988-11-07,1.78,73.0,athletics,0,0,0 -333123827,Enas Mostafa Youssef Ahmed,EGY,female,1989-01-01,1.65,69.0,wrestling,0,0,0 -450857033,Endry Jose Saavedra,VEN,male,1991-05-14,1.8,,boxing,0,0,0 -282037284,Endy Miyem,FRA,female,1988-05-15,1.88,87.0,basketball,0,0,0 -166163342,English Gardner,USA,female,1992-04-22,1.68,54.0,athletics,1,0,0 -786298586,Enkelejda Shehaj Bekurti,USA,female,1969-01-23,1.63,57.0,shooting,0,0,0 -957160839,Enkh-Amar Kharkhuu,MGL,male,1992-08-05,1.7,,boxing,0,0,0 -62584278,Enrico D'Aniello,ITA,male,1995-12-06,1.52,53.0,rowing,0,0,0 -686586839,Enrico Garozzo,ITA,male,1989-06-21,1.9,70.0,fencing,0,1,0 -3678441,Enrico Lacruz,NED,male,1993-08-31,1.81,,boxing,0,0,0 -561159124,Enrique Brol,GUA,male,1978-10-09,1.81,73.0,shooting,0,0,0 -445189278,Enrique Jose Arathoon Pacas,ESA,male,1992-01-18,1.8,78.0,sailing,0,0,0 -968294058,Enzo Khasz,FRA,male,1993-08-13,2.03,105.0,aquatics,0,0,0 -484934440,Enzo Lefort,FRA,male,1991-09-29,1.91,80.0,fencing,0,1,0 -533594099,Enzo Yanez,CHI,male,1985-09-13,1.74,63.0,athletics,0,0,0 -57332594,Eoin Coughlan,AUS,male,1992-03-31,1.86,81.0,judo,0,0,0 -246404947,Epke Zonderland,NED,male,1986-04-16,1.73,69.0,gymnastics,0,0,0 -628232189,Epp Mae,EST,female,1992-04-02,1.7,76.0,wrestling,0,0,0 -800797383,Ercan Muslu,TUR,male,1988-12-01,1.6,52.0,athletics,0,0,0 -577556820,Erdinc Kebapci,TUR,male,1993-06-27,1.91,77.0,shooting,0,0,0 -897791102,Eri Hozumi,JPN,female,1994-02-17,1.67,60.0,tennis,0,0,0 -41617234,Eri Tosaka,JPN,female,1993-08-30,1.52,53.0,wrestling,1,0,0 -370045491,Eri Yonamine,JPN,female,1991-04-25,1.6,51.0,cycling,0,0,0 -121824937,Eric Alejandro,PUR,male,1986-04-15,1.8,81.0,athletics,0,0,0 -365293202,Eric Cray,PHI,male,1988-11-06,1.76,70.0,athletics,0,0,0 -250274348,Eric Delaunay,FRA,male,1987-12-04,1.78,83.0,shooting,0,0,0 -569855647,Eric Gillis,CAN,male,1980-03-08,1.72,58.0,athletics,0,0,0 -952826532,Eric Johannesen,GER,male,1988-07-16,1.93,100.0,rowing,0,1,0 -317561671,Eric Lamaze,CAN,male,1968-04-17,1.7,65.0,equestrian,0,0,1 -281183141,Eric Murray,NZL,male,1982-05-06,1.95,98.0,rowing,1,0,0 -92531992,Eric Oelschlaegel,GER,male,1995-09-19,1.93,87.0,football,0,1,0 -720596790,Eric Woelfl,CAN,male,1989-07-18,1.93,69.0,rowing,0,0,0 -289444580,Erica Elizabeth Wiebe,CAN,female,1989-06-13,1.75,75.0,wrestling,1,0,0 -43783175,Erica de Sena,BRA,female,1985-05-03,1.68,55.0,athletics,0,0,0 -431001912,Erick Aguirre,MEX,male,1997-02-23,1.7,60.0,football,0,0,0 -703944277,Erick Barrondo,GUA,male,1991-06-14,1.78,60.0,athletics,0,0,0 -239360706,Erick Gutierrez,MEX,male,1995-06-15,1.76,70.0,football,0,0,0 -493508166,Erick Rodriguez,NCA,male,1990-06-01,1.73,59.0,athletics,0,0,0 -577570329,Erick Torres,MEX,male,1993-01-19,1.83,79.0,football,0,0,0 -438356215,Erik Heil,GER,male,1989-08-10,1.85,80.0,sailing,0,0,1 -571952374,Erik Kynard,USA,male,1991-02-03,1.94,89.0,athletics,0,0,0 -421842394,Erik Persson,SWE,male,1994-01-12,1.81,75.0,aquatics,0,0,0 -184812086,Erik Pfeifer,GER,male,1987-01-22,1.91,,boxing,0,0,0 -474166352,Erik Shoji,USA,male,1989-08-24,1.84,83.0,volleyball,0,0,1 -476898873,Erik Tysse,NOR,male,1980-12-04,,,athletics,0,0,0 -296441839,Erik Varga,SVK,male,1976-06-09,1.84,90.0,shooting,0,0,0 -939093162,Erik Vlcek,SVK,male,1981-12-29,1.89,89.0,canoe,0,1,0 -898845676,Erika,BRA,female,1988-02-04,1.72,60.0,football,0,0,0 -594663400,Erika Abril,COL,female,1978-03-29,1.64,52.0,athletics,0,0,0 -938043431,Erika Araki,JPN,female,1984-08-03,1.86,78.0,volleyball,0,0,0 -876370845,Erika Fasana,ITA,female,1996-02-17,1.49,44.0,gymnastics,0,0,0 -699027822,Erika Ferraioli,ITA,female,1986-03-23,1.8,65.0,aquatics,0,0,0 -574116085,Erika Kinsey,SWE,female,1988-03-10,1.85,68.0,athletics,0,0,0 -907121538,Erika Kirpu,EST,female,1992-06-22,1.74,61.0,fencing,0,0,0 -136448459,Erika Miranda,BRA,female,1987-06-04,1.62,52.0,judo,0,0,0 -630444522,Erika Olivera,CHI,female,1976-01-04,1.63,55.0,athletics,0,0,0 -121922663,Erika Seltenreich-Hodgson,CAN,female,1995-04-24,1.7,65.0,aquatics,0,0,0 -923211927,Erika Souza,BRA,female,1982-03-09,1.97,92.0,basketball,0,0,0 -854947298,Erika Villaecija garcia,ESP,female,1984-06-02,1.77,61.0,aquatics,0,0,0 -861627176,Erin Densham,AUS,female,1985-05-03,1.65,52.0,triathlon,0,0,0 -988480190,Erin Nayler,NZL,female,1992-04-17,1.77,68.0,football,0,0,0 -582093558,Erin Phillips,AUS,female,1985-05-19,1.73,70.0,basketball,0,0,0 -940555509,Erin Rafuse,CAN,female,1988-12-02,1.73,60.0,sailing,0,0,0 -468726001,Erin Teschuk,CAN,female,1994-10-25,1.63,52.0,athletics,0,0,0 -654439782,Erina Jeke,ZIM,female,1990-09-16,1.63,53.0,football,0,0,0 -738743977,Erislandy Savon,CUB,male,1990-07-21,1.92,,boxing,0,0,1 -626115327,Erkin Adylbek Uulu,KGZ,male,1991-02-14,1.9,,boxing,0,0,0 -591654381,Erlon de Souza Silva,BRA,male,1991-06-23,1.75,78.0,canoe,0,1,0 -92210230,Ernesto Andres Zamora,URU,male,1983-04-13,1.8,63.0,athletics,0,0,0 -996057548,Ernesto Boardman,MEX,male,1993-02-23,1.75,92.0,archery,0,0,0 -301160522,Ernesto Reve,CUB,male,1992-02-26,1.82,79.0,athletics,0,0,0 -964710866,Ernst Rost-Onnes,BRA,male,1985-12-05,1.84,76.0,hockey,0,0,0 -840577893,Ersin Tacir,TUR,male,1985-04-01,1.7,61.0,athletics,0,0,0 -585533638,Erwan le Pechoux,FRA,male,1982-01-13,1.71,65.0,fencing,0,1,0 -794735118,Erwin Jose Caraballo Cabrera,VEN,male,1981-07-21,1.85,130.0,wrestling,0,0,0 -200848465,Erwin Maldonado,VEN,male,1983-07-25,1.7,70.0,aquatics,0,0,0 -138493384,Ese Brume,NGR,female,1996-01-20,1.67,58.0,athletics,0,0,0 -78109140,Eseosa Desalu,ITA,male,1994-02-19,1.8,67.0,athletics,0,0,0 -812990220,Eslam Eissa,EGY,male,1988-07-02,1.85,80.0,handball,0,0,0 -140388998,Esma Aydemir,TUR,female,1992-01-01,1.6,48.0,athletics,0,0,0 -276263740,Esmee Vermeulen,NED,female,1996-04-21,1.79,64.0,aquatics,0,0,0 -11916654,Espen Kofstad,NOR,male,1987-08-11,,,golf,0,0,0 -240797974,Esra Ural,TUR,female,1991-08-18,1.98,100.0,basketball,0,0,0 -535348868,Esraa Ahmed,EGY,female,1998-11-21,1.5,62.0,weightlifting,0,0,0 -54624746,Esref Apak,TUR,male,1982-01-03,1.84,120.0,athletics,0,0,0 -243334151,Estavana Polman,NED,female,1992-08-05,1.73,65.0,handball,0,0,0 -246336777,Esteban Enderica,ECU,male,1990-10-30,1.77,71.0,aquatics,0,0,0 -969516375,Esteban Grimalt,CHI,male,1991-01-09,1.9,88.0,volleyball,0,0,0 -962423324,Estefania Alvarez Piedrahita,COL,female,1994-08-25,1.62,60.0,aquatics,0,0,0 -318230176,Estefania Garcia,ECU,female,1988-05-13,1.65,63.0,judo,0,0,0 -61187111,Estefania Ramirez,COL,female,1991-09-10,1.68,69.0,rugby sevens,0,0,0 -776130602,Estela Garcia,ESP,female,1989-03-20,1.7,56.0,athletics,0,0,0 -438033327,Estela Navascues,ESP,female,1981-02-03,1.63,50.0,athletics,0,0,0 -208192255,Estellah Fils Rabetsara,MAD,female,1994-05-29,1.67,59.0,aquatics,0,0,0 -317848923,Estelle Mossely,FRA,female,1992-08-19,1.68,,boxing,1,0,0 -38750795,Estelle Nze-Minko,FRA,female,1991-08-11,1.78,67.0,handball,0,1,0 -789081554,Esther Barrugues Alvina,AND,female,1980-05-16,1.64,60.0,shooting,0,0,0 -982554606,Esther Guerrero,ESP,female,1990-02-07,1.68,60.0,athletics,0,0,0 -411235848,Esther Qin,AUS,female,1991-11-18,1.68,58.0,aquatics,0,0,0 -29928369,Esther Stam,GEO,female,1987-03-11,1.74,75.0,judo,0,0,0 -928379539,Etel Sanchez,ARG,female,1989-08-23,1.71,59.0,aquatics,0,0,0 -572871860,Etenesh Diro,ETH,female,1991-05-10,1.68,49.0,athletics,0,0,0 -711536725,Ethan Mitchell,NZL,male,1991-02-19,1.8,83.0,cycling,0,1,0 -724574659,Etiene Medeiros,BRA,female,1991-05-24,1.69,60.0,aquatics,0,0,0 -182669270,Etienne Hubert,FRA,male,1988-01-27,1.85,86.0,canoe,0,0,0 -17751725,Etimoni Timuani,TUV,male,1991-10-14,1.84,72.0,athletics,0,0,0 -777342251,Eugene Magee,IRL,male,1986-04-01,1.77,83.0,hockey,0,0,0 -855597933,Eugene Wang,CAN,male,1985-11-13,1.74,80.0,table tennis,0,0,0 -479811363,Eugenie Bouchard,CAN,female,1994-02-25,1.78,58.0,tennis,0,0,0 -446528894,Eugenie le Sommer,FRA,female,1989-05-18,1.61,58.0,football,0,0,0 -182815845,Eugenio Rossi,SMR,male,1992-03-06,1.92,75.0,athletics,0,0,0 -841004916,Eun Hee Ryu,KOR,female,1990-02-24,1.8,76.0,handball,0,0,0 -910783696,Eun Ju Lee,KOR,female,1999-03-05,1.48,,gymnastics,0,0,0 -465103196,Eunbi Cheon,KOR,female,1992-02-07,1.65,59.0,hockey,0,0,0 -879574989,Eunbi Lee,KOR,female,1990-10-23,1.63,58.0,handball,0,0,0 -53526732,Eunhye Kim,KOR,female,1987-05-08,1.61,50.0,shooting,0,0,0 -11561844,Eunice Chibanda,ZIM,female,1993-03-26,1.63,61.0,football,0,0,0 -289758508,Eunice Jepkirui Kirwa,BRN,female,1984-05-20,1.55,49.0,athletics,0,1,0 -1089850,Eunice Jepkoech Sum,KEN,female,1988-04-10,1.7,54.0,athletics,0,0,0 -847401073,Eunsook Choi,KOR,female,1986-02-28,1.69,59.0,fencing,0,0,0 -126463635,Eva Alicia Gurrola Ortiz,MEX,female,1994-05-17,1.55,63.0,weightlifting,0,0,0 -779677104,Eva Calvo Gomez,ESP,female,1991-07-29,1.76,57.0,taekwondo,0,1,0 -18347,Eva Csernoviczki,HUN,female,1986-10-16,1.6,51.0,judo,0,0,0 -133712250,Eva Hovenkamp,NED,female,1996-07-19,1.73,65.0,athletics,0,0,0 -810831172,Eva Lechner,ITA,female,1985-07-01,1.65,50.0,cycling,0,0,0 -239566230,Eva Lee,USA,female,1986-08-07,1.68,58.0,badminton,0,0,0 -876737211,Eva Odorova,SVK,female,1979-11-22,1.75,60.0,table tennis,0,0,0 -797157508,Eva Risztov,HUN,female,1985-08-30,1.73,64.0,aquatics,0,0,0 -934827181,Eva Roesken,GER,female,1984-07-05,1.65,70.0,shooting,0,0,0 -565124998,Eva Vrabcova Nyvltova,CZE,female,1986-02-06,1.62,48.0,athletics,0,0,0 -832639547,Eva de Goede,NED,female,1989-03-23,1.7,61.0,hockey,0,1,0 -103773001,Evagjelia Veli,ALB,female,1991-07-16,1.6,52.0,weightlifting,0,0,0 -509893044,Evaldas Petrauskas,LTU,male,1992-03-19,1.64,,boxing,0,0,0 -270191422,Evan Dunfee,CAN,male,1990-09-28,1.86,65.0,athletics,0,0,0 -707333933,Evan Jager,USA,male,1989-03-08,1.88,65.0,athletics,0,1,0 -355432500,Evan Van Moerkerke,CAN,male,1993-08-16,2.05,110.0,aquatics,0,0,0 -854422751,Evandro Goncalves Oliveira Junior,BRA,male,1990-07-17,2.1,105.0,volleyball,0,0,0 -861327414,Evandro M. Guerra,BRA,male,1981-12-27,2.07,103.0,volleyball,1,0,0 -842357019,Evangelia Papazoglou,GRE,female,1995-01-14,1.72,60.0,aquatics,0,0,0 -171585760,Evangelia Platanioti,GRE,female,1994-08-09,1.7,52.0,aquatics,0,0,0 -714691544,Evangelia Psarra,GRE,female,1974-06-17,1.72,63.0,archery,0,0,0 -135668596,Evangelos Ioannis Delakas,GRE,male,1985-02-08,1.89,90.0,aquatics,0,0,0 -507584476,Evania Pelite,AUS,female,1995-07-12,1.69,67.0,rugby sevens,1,0,0 -302915787,Evans Kiplagat Barkowet,AZE,male,1988-03-05,1.7,60.0,athletics,0,0,0 -91128162,Eve Macfarlane,NZL,female,1992-09-27,1.86,75.0,rowing,0,0,0 -132983811,Evelina Afoa,SAM,female,1998-09-13,1.62,60.0,aquatics,0,0,0 -700402174,Evelis Aguilar,COL,female,1993-01-03,1.73,64.0,athletics,0,0,0 -94836813,Evelyn Cipriano,CUB,female,1995-12-24,,,athletics,0,0,0 -241380224,Evelyn Rivera,COL,female,1997-12-03,1.57,54.0,athletics,0,0,0 -176808861,Evelyn Stevens,USA,female,1983-05-09,1.66,55.0,cycling,0,0,0 -508215341,Evelyn Verraszto,HUN,female,1989-07-17,1.73,62.0,aquatics,0,0,0 -822616386,Evelyne Tschopp,SUI,female,1991-06-19,1.62,55.0,judo,0,0,0 -924396982,Ever Palma,MEX,male,1992-03-18,1.66,62.0,athletics,0,0,0 -447422012,Evgenia Ukolova,RUS,female,1989-05-17,1.81,66.0,volleyball,0,0,0 -674582235,Evgenii Drattcev,RUS,male,1983-01-24,1.8,74.0,aquatics,0,0,0 -491370876,Evgenii Kuznetsov,RUS,male,1990-04-12,1.73,65.0,aquatics,0,0,0 -355775573,Evgenii Lukantsov,RUS,male,1991-12-05,1.87,91.0,canoe,0,0,0 -863520431,Evgeniia Soboleva,RUS,female,1988-08-26,1.8,75.0,aquatics,0,0,1 -277821751,Evgeniya Ivanova,RUS,female,1987-07-26,1.76,67.0,aquatics,0,0,1 -475190404,Evgeniya Ovchinnikova,RUS,female,1985-04-07,1.7,49.0,equestrian,0,0,0 -222720821,Evgeny Donskoy,RUS,male,1990-05-09,1.84,76.0,tennis,0,0,0 -474060754,Evgeny Komarov,RUS,male,1988-11-08,1.75,78.0,cycling,0,0,0 -475233950,Evgeny Koptelov,RUS,male,1993-11-24,1.91,74.0,aquatics,0,0,0 -968996509,Evgeny Rylov,RUS,male,1996-09-23,1.84,73.0,aquatics,0,0,1 -93776802,Evgeny Tishchenko,RUS,male,1991-07-15,1.96,,boxing,1,0,0 -569106125,Evgheni Nedealco,MDA,male,1990-01-03,1.79,79.0,wrestling,0,0,0 -449788118,Evi van Acker,BEL,female,1985-09-23,1.72,70.0,sailing,0,0,0 -664096617,Evita Leter,SUR,female,1995-07-05,,,aquatics,0,0,0 -224043471,Ewa Swoboda,POL,female,1997-07-26,1.67,60.0,athletics,0,0,0 -671544226,Ewelina Ptak,POL,female,1987-03-20,1.65,57.0,athletics,0,0,0 -644393700,Ewelina Wojnarowska,POL,female,1986-12-13,1.71,72.0,canoe,0,0,0 -524481891,Eyal Levine,ISR,male,1986-08-27,1.7,60.0,sailing,0,0,0 -317494421,Eyglo Gustafsdottir,ISL,female,1995-02-01,1.87,82.0,aquatics,0,0,0 -483053147,Eythora Thorsdottir,NED,female,1998-08-10,1.6,47.0,gymnastics,0,0,0 -455154602,Ezekiel Kemboi,KEN,male,1982-05-25,1.67,52.0,athletics,0,0,0 -724615961,Ezequiel Palacios,ARG,male,1992-10-02,1.98,95.0,volleyball,0,0,0 -560065840,Ezequiel Unsain,ARG,male,1995-03-09,1.76,75.0,football,0,0,0 -340239700,Ezinne Okparaebo,NOR,female,1988-03-03,,,athletics,0,0,0 -390665654,Fa Quan Bai,CHN,male,1986-03-18,1.73,66.0,triathlon,0,0,0 -940097096,Fabian Cancellara,SUI,male,1981-03-18,1.86,81.0,cycling,1,0,0 -315207567,Fabian Drzyzga,POL,male,1990-01-03,1.96,90.0,volleyball,0,0,0 -986859469,Fabian Florant,NED,male,1983-02-01,1.8,74.0,athletics,0,0,0 -136342210,Fabian Gomez,ARG,male,1978-10-27,1.73,80.0,golf,0,0,0 -630689816,Fabian Hambuechen,GER,male,1987-10-25,1.64,68.0,gymnastics,1,0,0 -893322663,Fabian Heinle,GER,male,1994-05-14,1.87,72.0,athletics,0,0,0 -3921925,Fabian Hernando Puerta Zapata,COL,male,1991-07-12,1.82,90.0,cycling,0,0,0 -646375341,Fabian Kauter,SUI,male,1985-09-22,1.81,83.0,fencing,0,0,0 -357914952,Fabian Wiede,GER,male,1994-02-08,1.94,94.0,handball,0,0,1 -463895086,Fabiana,BRA,female,1989-08-04,1.61,57.0,football,0,0,0 -978433851,Fabiana Claudino,BRA,female,1985-01-24,1.93,76.0,volleyball,0,0,0 -708498437,Fabiana Diniz,BRA,female,1981-05-13,1.83,71.0,handball,0,0,0 -716396610,Fabiana Moraes,BRA,female,1986-06-05,1.7,55.0,athletics,0,0,0 -415759115,Fabiana Murer,BRA,female,1981-03-16,1.72,58.0,athletics,0,0,0 -391451002,Fabiano Joseph,TAN,male,1985-12-24,,50.0,athletics,0,0,0 -146876427,Fabien Gilot,FRA,male,1984-04-27,1.92,87.0,aquatics,0,1,0 -377151461,Fabienne In-Albon,SUI,female,1986-09-05,1.64,63.0,golf,0,0,0 -903291591,Fabienne Kohlmann,GER,female,1989-11-06,1.7,57.0,athletics,0,0,0 -218223862,Fabienne Schlumpf,SUI,female,1990-11-17,1.83,63.0,athletics,0,0,0 -586833716,Fabienne St Louis,MRI,female,1988-03-22,1.71,55.0,triathlon,0,0,0 -748389052,Fabio Aru,ITA,male,1990-07-03,1.83,65.0,cycling,0,0,0 -425939081,Fabio Basile,ITA,male,1994-10-07,1.6,66.0,judo,1,0,0 -606133637,Fabio Chiuffa,BRA,male,1989-03-10,1.85,85.0,handball,0,0,0 -578637850,Fabio Fognini,ITA,male,1987-05-24,1.8,74.0,tennis,0,0,0 -902180423,Fabio Infimo,ITA,male,1988-07-18,1.94,86.0,rowing,0,0,0 -33201075,Fabio Wyss,SUI,male,1989-06-09,1.85,85.0,canoe,0,0,0 -683014073,Fabrice Dabla,TOG,male,1992-11-20,,,athletics,0,0,0 -124241338,Fabrice Lapierre,AUS,male,1983-10-17,1.79,69.0,athletics,0,0,0 -96784555,Fabrice Zango Hugues,BUR,male,1993-06-25,1.8,78.0,athletics,0,0,0 -797399696,Fabrizio Donato,ITA,male,1976-08-14,1.89,83.0,athletics,0,0,0 -244377433,Fabrizio Zanotti,PAR,male,1983-05-21,1.69,70.0,golf,0,0,0 -549448963,Facundo Callioni,ARG,male,1985-10-09,1.83,77.0,hockey,1,0,0 -933200790,Facundo Campazzo,ARG,male,1991-03-23,1.81,85.0,basketball,0,0,0 -121940121,Facundo Conte,ARG,male,1989-08-25,1.97,88.0,volleyball,0,0,0 -866117672,Facundo Olezza Bazan,ARG,male,1994-08-30,1.87,97.0,sailing,0,0,0 -951494220,Fadwa Sidi Madane,MAR,female,1994-11-20,1.75,56.0,athletics,0,0,0 -252487063,Fahad Talib,IRQ,male,1994-10-21,1.9,84.0,football,0,0,0 -203217392,Fahem Hammachi,ALG,male,1992-03-07,1.78,,boxing,0,0,0 -442721,Faicel Jaballah,TUN,male,1988-05-01,1.96,,judo,0,0,0 -932376029,Faith Chepngetich Kipyegon,KEN,female,1994-01-10,1.57,43.0,athletics,1,0,0 -511266323,Faleh Suwead Al Ajami,QAT,male,1986-06-05,1.65,60.0,equestrian,0,0,0 -80472413,Fan Wang,CHN,female,1994-01-27,1.88,72.0,volleyball,0,0,0 -667913216,Fang Chen,CHN,female,1983-10-19,1.71,60.0,shooting,0,0,0 -129560075,Fangxu Yang,CHN,female,1994-10-06,1.9,71.0,volleyball,1,0,0 -775503680,Fanny Deberghes,FRA,female,1994-02-21,1.7,65.0,aquatics,0,0,0 -816581601,Fanny Horta,FRA,female,1986-01-22,1.66,63.0,rugby sevens,0,0,0 -337880291,Fanny Lecluyse,BEL,female,1992-03-11,1.77,64.0,aquatics,0,0,0 -742843800,Fantine Lesaffre,FRA,female,1994-11-10,1.8,57.0,aquatics,0,0,0 -465963657,Farah Boufadene,ALG,female,1999-03-11,1.55,55.0,gymnastics,0,0,0 -140298788,Farah Jacques,CAN,female,1990-02-08,1.74,59.0,athletics,0,0,0 -112237470,Fares Ferjani,TUN,male,1997-07-22,1.75,60.0,fencing,0,0,0 -924635327,Fares Ibrahim E. H. Elbakh,QAT,male,1998-06-04,1.75,85.0,weightlifting,0,0,0 -351843996,Farhad Ghaemi,IRI,male,1989-08-28,1.97,73.0,volleyball,0,0,0 -261146293,Farhan Farhan,BRN,male,1996-10-24,1.79,67.0,aquatics,0,0,0 -591480442,Farid Chaal,ALG,male,1994-07-03,1.9,80.0,football,0,0,0 -892804755,Farida Azizova,AZE,female,1995-06-06,1.73,67.0,taekwondo,0,0,0 -382788770,Farida Osman,EGY,female,1995-01-18,1.73,63.0,aquatics,0,0,0 -738027974,Farkhad Kharki,KAZ,male,1991-04-20,1.6,62.0,weightlifting,0,0,1 -848404130,Farzan Ashourzadeh Fallah,IRI,male,1996-11-25,1.85,58.0,taekwondo,0,0,0 -335487444,Fatehah Mustapa,MAS,female,1989-03-12,1.63,64.0,cycling,0,0,0 -283411647,Fatema Almahmeed,BRN,female,1999-06-14,1.67,60.0,aquatics,0,0,0 -91359398,Fatima Alkaramova,AZE,female,2002-06-26,1.75,60.0,aquatics,0,0,0 -300063217,Fatima Gallardo Carapeto,ESP,female,1997-05-24,1.8,60.0,aquatics,0,0,0 -577411448,Fatima Galvez,ESP,female,1987-01-19,1.66,60.0,shooting,0,0,0 -457192080,Fatma El Sharnouby,EGY,female,1997-11-18,,,athletics,0,0,0 -158999852,Fatou Dieng,SEN,female,1983-08-18,1.67,60.0,basketball,0,0,0 -708809781,Fatoumata Samassekou,MLI,female,1987-12-31,1.68,74.0,aquatics,0,0,0 -930422859,Faye Husain,IOA,female,1994-10-20,,,aquatics,0,0,0 -471409647,Faye Njie,GAM,male,1993-11-23,,,judo,0,0,0 -454377713,Fazliddin Gaibnazarov,UZB,male,1991-06-16,1.69,,boxing,1,0,0 -156730444,Federica Pellegrini,ITA,female,1988-08-05,1.79,65.0,aquatics,0,0,0 -131020655,Federica Radicchi,ITA,female,1988-12-21,1.7,70.0,aquatics,0,1,0 -536316359,Federico Bocchia,ITA,male,1986-10-24,1.97,93.0,aquatics,0,0,0 -384768641,Federico Bruno,ARG,male,1993-06-18,1.85,66.0,athletics,0,0,0 -735031995,Federico Delbonis,ARG,male,1990-10-05,1.93,89.0,tennis,0,0,0 -856887683,Federico Fernandez,ARG,male,1989-10-17,1.91,88.0,handball,0,0,0 -429655510,Federico Gil,ARG,male,1988-04-29,1.75,78.0,shooting,0,0,0 -959504694,Federico Grabich,ARG,male,1990-03-26,1.93,92.0,aquatics,0,0,0 -589818101,Federico Pizarro,ARG,male,1986-09-07,1.83,84.0,handball,0,0,0 -51952214,Federico Turrini,ITA,male,1987-07-21,1.93,87.0,aquatics,0,0,0 -695896806,Federico Vanelli,ITA,male,1991-03-09,1.8,75.0,aquatics,0,0,0 -38463047,Federico Vieyra,ARG,male,1988-07-21,1.92,90.0,handball,0,0,0 -742848038,Fedor Vlasov,RUS,male,1984-03-28,1.79,93.0,shooting,0,0,0 -388453546,Fedrick Dacres,JAM,male,1994-02-28,1.91,104.0,athletics,0,0,0 -238428792,Fehaid Aldeehani,IOA,male,1966-10-11,1.76,95.0,shooting,1,0,0 -302682488,Fei Peng,CHN,male,1992-03-06,1.84,89.0,wrestling,0,0,0 -759792291,Feihong Pan,CHN,female,1989-07-17,1.73,57.0,rowing,0,0,1 -54244371,Feilian Mao,CHN,male,1993-07-30,1.85,74.0,aquatics,0,0,0 -600249643,Felice Chow,TTO,female,1977-06-15,1.75,70.0,rowing,0,0,0 -184330822,Felice Mueller,USA,female,1989-10-15,1.86,72.0,rowing,0,0,0 -426841600,Felipe AMARAL,BRA,male,1990-10-09,,,equestrian,0,0,0 -932918802,Felipe Aguilar,COL,male,1993-01-20,1.91,80.0,football,0,0,0 -715303809,Felipe Aguilar,CHI,male,1974-11-07,1.7,72.0,golf,0,0,0 -671103389,Felipe Almeida Wu,BRA,male,1992-06-11,1.69,69.0,shooting,0,1,0 -390089818,Felipe Anderson,BRA,male,1993-04-15,1.75,69.0,football,1,0,0 -854679340,Felipe Borges,BRA,male,1994-11-16,1.85,72.0,canoe,0,0,0 -959719489,Felipe Cardenas Morales,CHI,male,1991-07-22,1.83,75.0,rowing,0,0,0 -619195032,Felipe Carmo,BRA,male,1997-03-12,,,football,0,0,0 -279016707,Felipe Claro,BRA,male,1986-02-28,1.72,83.0,rugby sevens,0,0,0 -549441884,Felipe Franca,BRA,male,1987-05-14,1.85,95.0,aquatics,0,0,0 -476489532,Felipe Kitadai,BRA,male,1989-07-28,1.64,60.0,judo,0,0,0 -245089124,Felipe Nascimento,BRA,male,1993-07-05,1.72,68.0,modern pentathlon,0,0,0 -452820051,Felipe Perrone,BRA,male,1986-02-27,1.83,95.0,aquatics,0,0,0 -420641230,Felipe Reyes,ESP,male,1980-03-16,2.04,109.0,basketball,0,0,1 -518637481,Felipe Sancery,BRA,male,1994-05-27,1.82,90.0,rugby sevens,0,0,0 -921036132,Felipe Tapia,CHI,male,1995-04-25,1.76,70.0,aquatics,0,0,0 -581423407,Felipe da Costa E Silva,BRA,male,1984-08-08,1.96,103.0,aquatics,0,0,0 -709441023,Felisha Johnson,USA,female,1989-07-24,1.86,127.0,athletics,0,0,0 -556954247,Felistas Muzongondi,ZIM,female,1986-03-22,1.67,53.0,football,0,0,0 -773046456,Felix Auboeck,AUT,male,1996-12-19,1.98,85.0,aquatics,0,0,0 -576762169,Felix Denayer,BEL,male,1990-01-31,1.9,85.0,hockey,0,1,0 -52397385,Felix Drahotta,GER,male,1989-01-01,2.0,102.0,rowing,0,1,0 -303731329,Felix Vogg,SUI,male,1990-06-19,1.8,68.0,equestrian,0,0,0 -641453872,Felix Wimberger,GER,male,1990-02-28,1.9,92.0,rowing,0,0,0 -534846589,Femi Ogunode,QAT,male,1991-05-15,1.85,85.0,athletics,0,0,0 -13738320,Femke Heemskerk,NED,female,1987-09-21,1.8,68.0,aquatics,0,0,0 -474653253,Femke Pluim,NED,female,1994-05-10,1.8,61.0,athletics,0,0,0 -281630482,Femke Stoltenborg,NED,female,1991-07-30,1.89,82.0,volleyball,0,0,0 -679334702,Fen Li,SWE,female,1976-08-25,1.64,60.0,table tennis,0,0,0 -934739694,Feng Chen,SIN,male,1994-03-24,1.7,65.0,table tennis,0,0,0 -887907683,Feng Zhou,CHN,female,1993-09-12,1.75,71.0,wrestling,0,0,0 -481744691,Fengkai Yu,CHN,male,1995-03-13,1.93,,boxing,0,0,0 -991490353,Fengliu Zhang,CHN,female,1989-11-15,1.72,77.0,wrestling,0,0,1 -870522604,Fengyue Pang,CHN,female,1989-01-19,1.65,59.0,football,0,0,0 -65705163,Ferdinand Gerz,GER,male,1988-11-17,1.76,62.0,sailing,0,0,0 -256517204,Ferenc Szekszardi,AUS,male,1979-09-22,1.78,79.0,canoe,0,0,0 -451152705,Ferenc Szentirmai,UKR,male,1983-11-29,1.8,75.0,equestrian,0,0,0 -562337692,Fergus Kavanagh,AUS,male,1985-05-21,1.82,75.0,hockey,0,0,0 -794973951,Ferguson Cheruiyot Rotich,KEN,male,1989-11-30,1.85,74.0,athletics,0,0,0 -352965099,Ferhat Arican,TUR,male,1993-07-28,1.78,68.0,gymnastics,0,0,0 -596003773,Fernanda Decnop,BRA,female,1987-06-19,1.72,67.0,sailing,0,0,0 -828133435,Fernanda Ferreira,BRA,female,1985-01-25,1.63,58.0,rowing,0,0,0 -269045350,Fernanda Franca da Silva,BRA,female,1989-09-25,1.76,68.0,handball,0,0,0 -114303596,Fernanda Martins,BRA,female,1988-07-26,1.75,85.0,athletics,0,0,0 -529630572,Fernanda Oliveira,BRA,female,1980-12-19,1.61,54.0,sailing,0,0,0 -999389505,Fernanda Rodrigues,BRA,female,1986-05-10,1.81,75.0,volleyball,0,0,0 -649243319,Fernanda Russo,ARG,female,1999-10-02,1.66,60.0,shooting,0,0,0 -404382736,Fernando,POR,male,1997-03-14,1.82,75.0,football,0,0,0 -813873984,Fernando Alarza,ESP,male,1991-03-23,1.78,67.0,triathlon,0,0,0 -52514011,Fernando Borello,ARG,male,1980-02-12,1.88,92.0,shooting,0,0,0 -288552617,Fernando Carro,ESP,male,1992-04-01,1.7,60.0,athletics,0,0,0 -175585720,Fernando Daniel Martinez,ARG,male,1991-07-18,1.65,,boxing,0,0,0 -713904003,Fernando Dayan Jorge,CUB,male,1998-12-03,1.73,70.0,canoe,0,0,0 -812666692,Fernando Echavarri Erasun,ESP,male,1972-08-13,1.8,76.0,sailing,0,0,0 -611572399,Fernando Garcia,ARG,male,1981-08-31,1.9,91.0,handball,0,0,0 -392897095,Fernando Gaviria Rendon,COL,male,1994-08-19,1.8,70.0,cycling,0,0,0 -724928421,Fernando Luna,ARG,male,1990-05-12,1.82,80.0,rugby sevens,0,0,0 -976575378,Fernando Pimenta,POR,male,1989-08-13,1.78,81.0,canoe,0,0,0 -201491381,Fernando Prass,BRA,male,1978-07-09,,,football,0,0,0 -344465413,Fernando Salas Manguis,ECU,male,1988-02-10,1.86,163.0,weightlifting,0,0,0 -427314005,Fernando Saraiva Reis,BRA,male,1990-03-10,1.85,155.0,weightlifting,0,0,0 -108222132,Fernando Scavasin,BRA,male,1984-11-24,1.84,80.0,fencing,0,0,0 -559608161,Ferry Weertman,NED,male,1992-06-27,1.89,86.0,aquatics,1,0,0 -282237207,Feyisa Lilesa,ETH,male,1990-02-01,1.75,67.0,athletics,0,1,0 -397905004,Fidel Antonio Vargas,CUB,male,1992-07-28,1.86,91.0,canoe,0,0,0 -263607639,Fie Udby Erichsen,DEN,female,1985-04-23,1.84,79.0,rowing,0,0,0 -990339292,Filip Dvorak,CZE,male,1988-07-30,1.89,89.0,canoe,0,0,0 -198261943,Filip Filipovic,SRB,male,1987-05-01,1.96,101.0,aquatics,1,0,0 -95290064,Filip Grgic,CRO,male,1989-10-25,1.73,70.0,taekwondo,0,0,0 -155085294,Filip Hrgovic,CRO,male,1992-06-04,1.97,,boxing,0,0,1 -797627457,Filip Ingebrigtsen,NOR,male,1993-04-20,,,athletics,0,0,0 -838040702,Filip KLIKOVAC,MNE,male,1989-02-07,1.9,118.0,aquatics,0,0,0 -296668459,Filip Kruslin,CRO,male,1989-03-18,1.99,93.0,basketball,0,0,0 -591154202,Filip Mihaljevic,CRO,male,1994-07-31,2.01,114.0,athletics,0,0,0 -426939907,Filip Nepejchal,CZE,male,1999-07-08,1.77,70.0,shooting,0,0,0 -818684530,Filip Svab,CZE,male,1983-04-28,1.73,78.0,canoe,0,0,0 -607425902,Filip Ude,CRO,male,1986-06-03,1.7,68.0,gymnastics,0,0,0 -789252320,Filip Wypych,POL,male,1991-04-20,1.83,75.0,aquatics,0,0,0 -877016251,Filip Zaborowski,POL,male,1994-07-25,1.88,77.0,aquatics,0,0,0 -351078300,Filipa Martins,POR,female,1996-01-09,1.63,55.0,gymnastics,0,0,0 -52190306,Filipe Baravilala,FIJ,male,1994-11-25,1.7,72.0,football,0,0,0 -760757270,Filippa Idehn,SWE,female,1990-08-15,1.83,80.0,handball,0,0,0 -14229029,Filippo Ganna,ITA,male,1996-07-25,1.95,75.0,cycling,0,0,0 -70452508,Filippo Lanza,ITA,male,1991-03-03,1.98,98.0,volleyball,0,1,0 -659248254,Filippo Magnini,ITA,male,1982-02-02,1.87,80.0,aquatics,0,0,0 -773729376,Finn Lemke,GER,male,1992-04-30,2.1,115.0,handball,0,0,1 -613119800,Finn Lynch,IRL,male,1996-04-23,1.82,82.0,sailing,0,0,0 -562480751,Fiona Albert,AUS,female,1990-12-12,1.75,75.0,rowing,0,0,0 -62874382,Fiona Bigwood,GBR,female,1976-04-24,1.73,75.0,equestrian,0,1,0 -571267730,Fiona Doyle,IRL,female,1991-10-04,1.72,63.0,aquatics,0,0,0 -239478517,Fiona Pennie,GBR,female,1982-11-09,1.69,70.0,canoe,0,0,0 -263535671,Fionnuala McCormack,IRL,female,1984-09-24,1.59,46.0,athletics,0,0,0 -858638156,Fiorella Francesca Cueva Uribe,PER,female,1998-02-04,1.5,48.0,weightlifting,0,0,0 -432731417,Fitzroy Dunkley,JAM,male,1993-05-20,1.96,80.0,athletics,0,1,0 -933661571,Flavia Oliveira,BRA,female,1981-10-27,1.56,46.0,cycling,0,0,0 -777180519,Flavia Saraiva,BRA,female,1999-09-30,1.33,31.0,gymnastics,0,0,0 -3922992,Flavia Tartaglini,ITA,female,1985-02-02,1.71,59.0,sailing,0,0,0 -545887341,Flavia de Lima,BRA,female,1993-07-01,1.76,65.0,athletics,0,0,0 -166844486,Flings Owusu-Agyapong,GHA,female,1988-10-16,,,athletics,0,0,0 -8505611,Flor Ruiz,COL,female,1991-01-29,1.72,59.0,athletics,0,0,0 -692948530,Flora Duffy,BER,female,1987-09-30,1.63,57.0,triathlon,0,0,0 -798849737,Flora Molnar,HUN,female,1998-03-02,1.75,65.0,aquatics,0,0,0 -191528927,Florence Allan,CAY,female,1998-05-26,1.68,57.0,sailing,0,0,0 -54068929,Florencia Habif,ARG,female,1993-08-22,1.65,55.0,hockey,0,0,0 -724902915,Florencia Natasha Busquets Reyes,ARG,female,1989-06-27,1.92,68.0,volleyball,0,0,0 -352775267,Florent Caelen,BEL,male,1989-03-01,1.75,54.0,athletics,0,0,0 -28413973,Florent Manaudou,FRA,male,1990-11-12,1.99,99.0,aquatics,0,2,0 -838021254,Florent Pietrus,FRA,male,1981-01-19,2.01,103.0,basketball,0,0,0 -396808426,Florent Van Aubel,BEL,male,1991-10-25,1.78,73.0,hockey,0,1,0 -686164081,Floria Guei,FRA,female,1990-05-02,1.66,53.0,athletics,0,0,0 -819262216,Florian Carvalho,FRA,male,1989-03-09,1.83,70.0,athletics,0,0,0 -725121470,Florian Floto,GER,male,1988-04-12,1.88,105.0,archery,0,0,0 -112934359,Florian Fuchs,GER,male,1991-11-10,1.85,74.0,hockey,0,0,1 -479481012,Florian Orth,GER,male,1989-07-24,1.81,63.0,athletics,0,0,0 -399561932,Florian Reichstaedter,AUT,male,1980-07-03,1.8,71.0,sailing,0,0,0 -950445859,Florian Skilang Temengil,PLW,male,1986-11-04,1.53,125.0,wrestling,0,0,0 -526727141,Florian Vogel,GER,male,1994-09-02,1.8,70.0,aquatics,0,0,0 -628381035,Florian Wellbrock,GER,male,1997-08-19,1.85,70.0,aquatics,0,0,0 -463142259,Floriane Gnafoua,FRA,female,1996-01-30,1.58,60.0,athletics,0,0,0 -287019022,Florin Mergea,ROU,male,1985-01-26,1.82,79.0,tennis,0,1,0 -765345310,Florina Chintoan,ROU,female,1985-12-06,1.78,67.0,handball,0,0,0 -929035723,Florina Pierdevara,ROU,female,1990-03-29,1.72,53.0,athletics,0,0,0 -605847359,Florina-Sorina Hulpan,ROU,female,1997-03-07,1.58,68.0,weightlifting,0,0,0 -429153076,Folau Niua,USA,male,1985-01-27,1.83,88.0,rugby sevens,0,0,0 -241899445,Foluke Akinradewo,USA,female,1987-10-05,1.91,79.0,volleyball,0,0,1 -312586740,Formiga,BRA,female,1978-03-03,1.62,57.0,football,0,0,0 -371445769,Fouad Elkaam,MAR,male,1988-05-27,1.88,70.0,athletics,0,0,0 -861278504,Francelina Cabral,TLS,female,1985-03-23,1.65,,cycling,0,0,0 -407138612,Francena McCorory,USA,female,1988-10-20,1.71,68.0,athletics,1,0,0 -513279514,Frances Houghton,GBR,female,1980-09-19,1.93,80.0,rowing,0,1,0 -23123206,Francesca Clapcich,ITA,female,1988-01-28,1.77,68.0,sailing,0,0,0 -278586505,Francesca Dallape',ITA,female,1986-06-24,1.64,58.0,aquatics,0,1,0 -421533477,Francesca Deidda,ITA,female,1992-01-16,1.64,52.0,aquatics,0,0,0 -870034029,Francesca Halsall,GBR,female,1990-04-12,1.71,64.0,aquatics,0,0,0 -535720666,Francesca Pattaro,ITA,female,1995-03-12,1.7,58.0,cycling,0,0,0 -136274448,Francesca Pomeri,ITA,female,1993-02-18,1.74,76.0,aquatics,0,1,0 -328463358,Francesco Fossi,ITA,male,1988-04-15,1.99,98.0,rowing,0,0,0 -720788958,Francesco Lamon,ITA,male,1994-02-05,1.73,60.0,cycling,0,0,0 -960345798,Francesco Marrai,ITA,male,1993-02-04,1.87,82.0,sailing,0,0,0 -564896831,Francesco di Fulvio,ITA,male,1993-08-15,1.9,88.0,aquatics,0,0,1 -162383071,Francie Turner,NZL,female,1992-04-06,1.59,50.0,rowing,0,0,0 -270335453,Franciela Krasucki,BRA,female,1988-04-26,1.68,63.0,athletics,0,0,0 -827083171,Francielle Rocha,BRA,female,1992-06-10,1.66,58.0,handball,0,0,0 -919748247,Francielly Pereira,BRA,female,1995-11-10,1.66,47.0,gymnastics,0,0,0 -129990979,Francine Niyonsaba,BDI,female,1993-05-05,1.66,60.0,athletics,0,1,0 -425631423,Francisca Crovetto Chadid,CHI,female,1990-04-27,1.6,54.0,shooting,0,0,0 -444157559,Francisca Laia,POR,female,1994-05-31,1.63,58.0,canoe,0,0,0 -576262342,Francisco Arcilla,ESP,male,1984-01-14,1.71,58.0,athletics,0,0,0 -300060819,Francisco Barretto Junior,BRA,male,1989-10-31,1.75,72.0,gymnastics,0,0,0 -955682483,Francisco Boza,PER,male,1964-09-19,1.8,96.0,shooting,0,0,0 -550687254,Francisco Cortes,ESP,male,1983-03-29,1.81,80.0,hockey,0,0,0 -478446820,Francisco Ducasse,CHI,male,1996-12-03,1.75,68.0,sailing,0,0,0 -896116528,Francisco Fernandez Miranda,ESP,male,1986-06-21,1.85,83.0,aquatics,0,0,0 -235643467,Francisco Garrigos,ESP,male,1994-12-09,1.6,60.0,judo,0,0,0 -679536913,Francisco Hernandez,ESP,male,1988-10-28,1.73,78.0,rugby sevens,0,0,0 -28575944,Francisco Limardo,VEN,male,1987-03-27,1.8,75.0,fencing,0,0,0 -246262074,Franck Elemba,CGO,male,1990-07-21,1.98,130.0,athletics,0,0,0 -76848119,Franck Lafitte,FRA,male,1989-03-08,2.03,94.0,volleyball,0,0,0 -101987317,Franck Solforosi,FRA,male,1984-09-10,1.84,70.0,rowing,0,0,1 -470889706,Francky Mbotto,CAF,male,1997-09-02,1.81,68.0,athletics,0,0,0 -62538812,Franco Donato,EGY,male,1981-09-08,1.65,64.0,shooting,0,0,0 -116672628,Franco Sabato,ARG,male,1990-01-13,1.86,81.0,rugby sevens,0,0,0 -547550475,Francois Heersbrandt,BEL,male,1989-12-12,1.79,76.0,aquatics,0,0,0 -794323734,Francois Hougaard,RSA,male,1988-04-06,1.79,93.0,rugby sevens,0,0,1 -410698560,Francois Pervis,FRA,male,1984-10-16,1.8,88.0,cycling,0,0,1 -81861765,Frank Aniello Molinaro,USA,male,1988-12-27,1.68,68.0,wrestling,0,0,0 -570873065,Frank Chamizo Marquez,ITA,male,1992-07-10,1.72,65.0,wrestling,0,0,1 -226825899,Frank Rijken,NED,male,1996-11-24,1.69,67.0,gymnastics,0,0,0 -715378085,Frank Schleck,LUX,male,1980-04-15,1.85,63.0,cycling,0,0,0 -266716820,Frank Staebler,GER,male,1989-06-27,1.74,74.0,wrestling,0,0,0 -226195365,Frank Thompson,USA,male,1988-03-11,1.81,79.0,shooting,0,0,0 -388800940,Frank de Wit,NED,male,1996-02-13,1.84,81.0,judo,0,0,0 -240687498,Franklin Gomez,PUR,male,1986-08-05,1.77,65.0,wrestling,0,0,0 -883176756,Frantz Dorsainvil,HAI,male,1991-07-02,1.7,59.0,aquatics,0,0,0 -324206155,Franz Anton,GER,male,1989-10-23,1.73,70.0,canoe,0,0,0 -887418902,Franzisca Hauke,GER,female,1989-09-10,1.72,63.0,hockey,0,0,1 -271428664,Franziska Hentke,GER,female,1989-06-04,1.72,62.0,aquatics,0,0,0 -688191947,Franziska Weber,GER,female,1989-05-24,1.76,70.0,canoe,0,2,0 -481935974,Freddie Woodward,GBR,male,1995-06-23,1.78,75.0,aquatics,0,0,0 -464256317,Freddy Figueroa,ECU,male,1994-11-26,1.92,,judo,0,0,0 -720635670,Freddy Mezones,VEN,male,1987-09-24,1.7,68.0,athletics,0,0,0 -676653591,Frederic Winters,CAN,male,1982-09-25,1.95,98.0,volleyball,0,0,0 -870194844,Frederick Bousquet,FRA,male,1981-04-08,1.88,84.0,aquatics,0,0,0 -82901717,Frederico Venancio,POR,male,1993-02-04,1.86,77.0,football,0,0,0 -491481795,Frederik Borsting,DEN,male,1995-02-13,1.84,78.0,football,0,0,0 -675256468,Frederik Madsen,DEN,male,1998-01-22,1.87,73.0,cycling,0,0,1 -760097709,Fredrik Bergstrom,SWE,male,1990-07-09,1.76,64.0,sailing,0,0,0 -276164244,Fredrik Petersen,SWE,male,1983-08-27,1.88,80.0,handball,0,0,0 -895184846,Frida Andersen,SWE,female,1990-06-09,1.64,67.0,equestrian,0,0,0 -732593711,Frida Tegstedt,SWE,female,1987-07-17,1.8,80.0,handball,0,0,0 -426661879,Fridolina Rolfo,SWE,female,1993-11-24,1.78,74.0,football,0,0,0 -156371008,Friederike Mohlenkamp,GER,female,1992-11-19,1.76,63.0,athletics,0,0,0 -438695638,Fu Yu,POR,female,1978-11-29,1.73,60.0,table tennis,0,0,0 -422530035,Furkan Sen,TUR,male,1994-02-08,1.75,74.0,athletics,0,0,0 -719720246,Fusheng Zhang,CHN,male,1993-09-20,1.84,68.0,shooting,0,0,0 -549844503,Gaber Mohamed,EGY,male,1985-09-01,1.8,105.0,weightlifting,0,0,0 -483544931,Gabor Balog,HUN,male,1990-09-02,1.86,83.0,aquatics,0,0,0 -450662056,Gabor Boczko,HUN,male,1977-04-01,1.92,90.0,fencing,0,0,1 -349248644,Gabor Faldum,HUN,male,1988-06-24,1.72,64.0,triathlon,0,0,0 -391312706,Gabor Jozsa,HUN,male,1983-08-18,1.7,59.0,athletics,0,0,0 -680668744,Gabor Kis,HUN,male,1982-09-27,1.94,115.0,aquatics,0,0,0 -14191210,Gabriel Barbosa,BRA,male,1996-08-30,1.76,68.0,football,1,0,0 -662078869,Gabriel Borges,BRA,male,1992-02-24,1.8,73.0,sailing,0,0,0 -630061946,Gabriel Deck,ARG,male,1995-02-08,1.97,107.0,basketball,0,0,0 -72582764,Gabriel Ho-Garcia,CAN,male,1993-05-19,1.7,70.0,hockey,0,0,0 -250938129,Gabriel Jesus,BRA,male,1997-04-03,1.75,68.0,football,1,0,0 -590308057,Gabriel Maestre,VEN,male,1986-09-22,1.79,,boxing,0,0,0 -948998710,Gabriel Mvumvure,ZIM,male,1988-02-23,1.77,70.0,athletics,0,0,0 -243636258,Gabriel Santos,BRA,male,1996-05-04,1.84,83.0,aquatics,0,0,0 -563040295,Gabriel Sincraian,ROU,male,1988-12-21,1.74,84.0,weightlifting,0,0,1 -836852876,Gabriela Aguirre,ARG,female,1986-02-19,1.64,58.0,hockey,0,0,0 -472170307,Gabriela Bayardo,MEX,female,1994-02-18,1.68,73.0,archery,0,0,0 -297118288,Gabriela Braga Guimaraes,BRA,female,1994-05-19,1.76,59.0,volleyball,0,0,0 -848116325,Gabriela Dabrowski,CAN,female,1992-04-01,1.8,84.0,tennis,0,0,0 -930649410,Gabriela Mantellato,BRA,female,1991-10-28,1.75,76.0,aquatics,0,0,0 -556512285,Gabriela Mosqueira,PAR,female,1990-04-05,1.63,60.0,rowing,0,0,0 -563204393,Gabriela Perianu,ROU,female,1994-06-20,1.87,80.0,handball,0,0,0 -101409692,Gabriela Petrova,BUL,female,1992-06-29,1.67,58.0,athletics,0,0,0 -901521061,Gabriela Stafford,CAN,female,1995-09-13,1.63,53.0,athletics,0,0,0 -951509811,Gabriela Stoeva,BUL,female,1994-07-15,1.69,63.0,badminton,0,0,0 -188364932,Gabriele Detti,ITA,male,1994-08-29,1.84,79.0,aquatics,0,0,2 -208944185,Gabriele Rossetti,ITA,male,1995-03-07,1.76,65.0,shooting,1,0,0 -697532444,Gabriella Doueihy,LIB,female,1999-04-30,,,aquatics,0,0,0 -99788705,Gabriella Szabo,HUN,female,1986-08-14,1.7,62.0,canoe,2,0,0 -754381040,Gabriella Szucs,HUN,female,1988-03-07,1.83,74.0,aquatics,0,0,0 -787338951,Gabriella Szucs,ROU,female,1984-08-31,1.84,78.0,handball,0,0,0 -463517978,Gabrielle Adcock,GBR,female,1990-09-30,1.67,,badminton,0,0,0 -862130996,Gabrielle Carle,CAN,female,1998-10-12,1.67,55.0,football,0,0,0 -995739090,Gabrielle Douglas,USA,female,1995-12-31,1.5,49.0,gymnastics,1,0,0 -596713145,Gabrielle Moraes da Silva,BRA,female,1997-03-04,1.64,50.0,gymnastics,0,0,0 -509202637,Gabrielle Nance,AUS,female,1994-07-29,1.69,55.0,hockey,0,0,0 -993305509,Gabrielle Roncatto,BRA,female,1998-07-19,1.65,60.0,aquatics,0,0,0 -243788789,Gaby Diana Ahrens,NAM,female,1981-03-15,1.69,60.0,shooting,0,0,0 -589149765,Gaby Lopez,MEX,female,1993-11-09,1.66,58.0,golf,0,0,0 -116766804,Gael Monfils,FRA,male,1986-09-01,1.93,84.0,tennis,0,0,0 -902039181,Gael Suter,SUI,male,1992-03-23,1.76,70.0,cycling,0,0,0 -329610435,Gaelle Mys,BEL,female,1991-11-16,1.44,57.0,gymnastics,0,0,0 -767593736,Gaelle Skrela,FRA,female,1983-01-24,1.77,70.0,basketball,0,0,0 -941626270,Gaelle Verlaine Nayo Ketchanke,FRA,female,1988-04-20,1.74,74.0,weightlifting,0,0,0 -341647204,Gagan Narang,IND,male,1983-05-06,1.8,115.0,shooting,0,0,0 -629247598,Gakuto Notsuda,JPN,male,1994-06-06,1.75,70.0,football,0,0,0 -324536211,Gal Nevo,ISR,male,1987-06-29,1.8,80.0,aquatics,0,0,0 -866485713,Galal Yafai,GBR,male,1992-12-11,1.58,,boxing,0,0,0 -106376682,Galen Rupp,USA,male,1986-05-08,1.81,61.0,athletics,0,0,1 -711261984,Galia Dvorak,ESP,female,1988-04-01,,,table tennis,0,0,0 -915389920,Galina Voskoboeva,KAZ,female,1984-12-18,1.83,67.0,tennis,0,0,0 -802472247,Galymzhan Usserbayev,KAZ,male,1988-12-19,1.73,74.0,wrestling,0,0,0 -272456931,Galyna Obleshchuk,UKR,female,1989-02-23,1.77,94.0,athletics,0,0,0 -781481729,Gan-Erdene Gankhuyag,MGL,male,1993-03-29,1.62,,boxing,0,0,0 -897822303,Ganapathi Krishnan,IND,male,1989-06-29,,,athletics,0,0,0 -343445816,Ganna Krasnoshlyk,UKR,female,1996-03-06,1.71,59.0,aquatics,0,0,0 -995606281,Ganna Rizatdinova,UKR,female,1993-07-16,1.73,47.0,gymnastics,0,0,1 -656968895,Ganna Solovei,UKR,female,1992-01-31,1.67,56.0,cycling,0,0,0 -526876218,Gantugs Jantsan,MGL,male,1972-04-12,1.75,73.0,archery,0,0,0 -298278410,Gantulga Dambadarjaa,MGL,male,1989-02-03,1.7,58.0,athletics,0,0,0 -538703926,Gaone Leaname Maotoanong,BOT,male,1991-05-07,,,athletics,0,0,0 -745281987,Garbine Muguruza,ESP,female,1993-10-08,1.84,72.0,tennis,0,0,0 -892862270,Garnik Mnatsakanyan,ARM,male,1989-11-07,1.61,57.0,wrestling,0,0,0 -622386960,Garrett Bender,USA,male,1991-12-02,1.94,104.0,rugby sevens,0,0,0 -800177354,Gary O'Donovan,IRL,male,1992-12-30,1.72,70.0,rowing,0,1,0 -249661772,Gary Russell,USA,male,1996-06-14,1.73,,boxing,0,0,0 -830687385,Gaspar Csere,HUN,male,1991-08-12,1.71,55.0,athletics,0,0,0 -12459891,Gastao Elias,POR,male,1990-11-24,1.82,75.0,tennis,0,0,0 -456762500,Gaston Revol,ARG,male,1986-11-26,1.7,76.0,rugby sevens,0,0,0 -32924852,Gaurika Singh,NEP,female,2002-11-26,1.55,45.0,aquatics,0,0,0 -996740873,Gauthier Boccard,BEL,male,1991-08-26,1.86,79.0,hockey,0,1,0 -918357805,Gauthier Grumier,FRA,male,1984-05-29,1.91,83.0,fencing,1,0,1 -694977579,Gauthier Klauss,FRA,male,1987-12-17,1.71,65.0,canoe,0,0,1 -956777430,Gavin Ben Sutherland,ZIM,male,1979-06-26,1.86,78.0,archery,0,0,0 -811772770,Gavin Kyle Green,MAS,male,1993-12-28,1.87,91.0,golf,0,0,0 -213614173,Gavin Mogopa,BOT,male,1996-04-02,1.79,60.0,judo,0,0,0 -71978236,Gavin Schmitt,CAN,male,1986-01-27,2.08,106.0,volleyball,0,0,0 -620621814,Gayane Chiloyan,ARM,female,2000-09-27,1.64,54.0,athletics,0,0,0 -91833372,Gayle Broughton,NZL,female,1996-06-05,1.74,70.0,rugby sevens,0,1,0 -608678057,Gbahi Gwladys Sakoa,CIV,female,1992-12-03,1.71,64.0,fencing,0,0,0 -248516313,Geisa Aparecida Coutinho,BRA,female,1980-06-01,1.61,55.0,athletics,0,0,0 -117544722,Geisa Arcanjo,BRA,female,1991-09-19,1.6,60.0,athletics,0,0,0 -286940521,Gelena Topilina,RUS,female,1994-01-11,1.75,56.0,aquatics,1,0,0 -809586183,Gelete Burka,ETH,female,1986-01-23,1.6,43.0,athletics,0,0,0 -12235602,Gelly Skarlatou,GRE,female,1976-01-28,1.65,57.0,sailing,0,0,0 -671357684,Gemma Acheampong,GHA,female,1993-02-13,1.63,54.0,athletics,0,0,0 -566383744,Gemma Beadsworth,AUS,female,1987-07-17,1.8,78.0,aquatics,0,0,0 -141739381,Gemma Etheridge,AUS,female,1986-12-01,1.69,66.0,rugby sevens,1,0,0 -893302726,Gemma Flynn,NZL,female,1990-05-02,1.68,60.0,hockey,0,0,0 -528547946,Gemma Jones,NZL,female,1994-01-07,1.65,59.0,sailing,0,0,0 -836429010,Gemma Mengual,ESP,female,1977-04-12,1.73,56.0,aquatics,0,0,0 -447374604,Gemma Tattersall,GBR,female,1985-03-12,1.65,69.0,equestrian,0,0,0 -533972369,Gen Li,CHN,male,1988-08-15,1.96,110.0,basketball,0,0,0 -974257669,Genevieve Behrent,NZL,female,1990-09-25,1.83,73.0,rowing,0,1,0 -966530207,Genevieve Horton,AUS,female,1995-01-06,1.79,72.0,rowing,0,0,0 -316560437,Genevieve Lacaze,AUS,female,1989-08-04,1.64,53.0,athletics,0,0,0 -564185674,Genevieve Lalonde,CAN,female,1991-09-05,1.7,58.0,athletics,0,0,0 -476981341,Genevieve Orton,CAN,female,1984-05-13,1.7,62.0,canoe,0,0,0 -682915910,Genevra Stone,USA,female,1985-07-11,1.83,71.0,rowing,0,1,0 -586682679,Geno Petriashvili,GEO,male,1994-04-01,1.98,125.0,wrestling,0,0,1 -557935891,Genzebe Dibaba,ETH,female,1991-02-08,1.68,52.0,athletics,0,1,0 -289855181,Geoffrey Butler,CAY,male,1995-12-07,1.88,84.0,aquatics,0,0,0 -211177901,Geoffrey Cheah,HKG,male,1990-11-10,1.85,77.0,aquatics,0,0,0 -242198158,Geoffrey Kipsang Kamworor,KEN,male,1992-11-22,1.72,58.0,athletics,0,0,0 -962442780,Georcy Thiffeault Picard,CAN,female,1991-02-08,1.76,78.0,archery,0,0,0 -814234541,Georg Preidler,AUT,male,1990-06-17,1.89,70.0,cycling,0,0,0 -378090753,George Bennett,NZL,male,1990-04-07,1.81,58.0,cycling,0,0,0 -373349430,George Bovell Iii,TTO,male,1983-07-18,1.96,74.0,aquatics,0,0,0 -853409022,George Bridgewater,NZL,male,1983-01-18,2.0,97.0,rowing,0,0,0 -529194033,George Ford,AUS,male,1993-02-24,1.92,95.0,aquatics,0,0,0 -962074065,George Nash,GBR,male,1989-10-02,1.95,96.0,rowing,1,0,0 -146249662,George Palamariu,ROU,male,1991-03-17,1.97,106.0,rowing,0,0,0 -422133225,George Pinner,GBR,male,1987-01-18,1.92,92.0,hockey,0,0,0 -94884308,Georgi Bozhilov,BUL,male,1989-04-09,2.01,107.0,rowing,0,0,0 -867155948,Georgi Ivanov,BUL,male,1985-03-13,1.88,138.0,athletics,0,0,0 -872775850,Georgi Ivanov Ivanov,BUL,male,1989-11-11,1.7,78.0,wrestling,0,0,0 -513877606,Georgi Tsonov,BUL,male,1993-05-02,1.76,70.0,athletics,0,0,0 -974930725,Georgia Baker,AUS,female,1994-09-21,1.79,66.0,cycling,0,0,0 -730944195,Georgia Bohl,AUS,female,1997-04-11,1.67,54.0,aquatics,0,0,0 -55412300,Georgia Coates,GBR,female,1999-02-19,1.74,63.0,aquatics,0,0,0 -129677889,Georgia Davies,GBR,female,1990-10-11,1.75,64.0,aquatics,0,0,0 -661775435,Georgia Nanscawen,AUS,female,1992-05-27,1.6,59.0,hockey,0,0,0 -405138190,Georgia Simmerling,CAN,female,1989-03-11,1.72,67.0,cycling,0,0,1 -304591948,Georgia Williams,NZL,female,1993-08-25,1.7,60.0,cycling,0,0,0 -289207914,Georgii Ketoev,ARM,male,1985-11-19,1.9,100.0,wrestling,0,0,0 -932124601,Georgii Zantaraia,UKR,male,1987-10-21,1.71,66.0,judo,0,0,0 -827810713,Georgina Klug,ARG,female,1984-06-11,1.72,64.0,volleyball,0,0,0 -13264832,Georgina Morgan,AUS,female,1993-05-15,1.79,70.0,hockey,0,0,0 -218184673,Georgina Oliva,ESP,female,1990-07-18,1.6,53.0,hockey,0,0,0 -308752334,Georgina Parker,AUS,female,1989-04-26,1.59,68.0,hockey,0,0,0 -325691946,Georgina Pota,HUN,female,1985-01-13,1.73,63.0,table tennis,0,0,0 -559477577,Georgina Twigg,GBR,female,1990-11-21,1.6,62.0,hockey,1,0,0 -87130611,Georgios Dervisis,GRE,male,1994-10-30,1.95,92.0,aquatics,0,0,0 -749506704,Georgios Tziallas,GRE,male,1987-07-14,1.89,91.0,rowing,0,0,0 -217536800,Georgiy Sheiko,KAZ,male,1989-08-24,1.84,68.0,athletics,0,0,0 -679229547,Geraint Thomas,GBR,male,1986-05-25,1.83,70.0,cycling,0,0,0 -895148675,Gerald Giraldo,COL,male,1989-03-21,1.75,60.0,athletics,0,0,0 -473977731,Gerald Phiri,ZAM,male,1988-10-06,1.78,79.0,athletics,0,0,0 -301656142,Gerardo Menendez Mieres,ESP,male,1976-09-11,1.85,80.0,equestrian,0,0,0 -913327626,Gerasim Kochnev,UZB,male,1987-03-20,1.78,85.0,canoe,0,0,0 -426364755,Gerco Schroder,NED,male,1978-07-28,1.65,72.0,equestrian,0,0,0 -807429022,Gerd Kanter,EST,male,1979-05-06,1.96,125.0,athletics,0,0,0 -859175581,Gerek Meinhardt,USA,male,1990-07-27,1.83,79.0,fencing,0,0,1 -145487888,Gergely Gyurta,HUN,male,1991-09-12,1.75,70.0,aquatics,0,0,0 -293724411,Gergo Kis,HUN,male,1988-01-19,1.83,80.0,aquatics,0,0,0 -996218071,Gergo Zalanki,HUN,male,1995-02-26,1.92,85.0,aquatics,0,0,0 -60549448,Gerina Piller,USA,female,1985-03-29,1.71,64.0,golf,0,0,0 -285564872,Germain Chardin,FRA,male,1983-05-15,1.95,90.0,rowing,0,0,0 -140439112,German Chiaraviglio,ARG,male,1987-04-16,1.95,84.0,athletics,0,0,0 -588470797,German Lauro,ARG,male,1984-04-02,1.86,127.0,athletics,0,0,0 -995047699,German Sanchez,MEX,male,1992-06-24,1.65,60.0,aquatics,0,1,0 -151532302,German Schulz,ARG,male,1994-02-05,1.88,95.0,rugby sevens,0,0,0 -482136504,Gernot Rumpler,AUT,male,1994-02-21,1.89,86.0,shooting,0,0,0 -423925127,Geronimo Rulli,ARG,male,1992-05-20,1.75,74.0,football,0,0,0 -377536681,Gesa Felicitas Krause,GER,female,1992-08-03,1.67,51.0,athletics,0,0,0 -477754355,Geumyoung Jang,KOR,female,1980-05-04,1.63,51.0,shooting,0,0,0 -947906397,Gevrise Emane,FRA,female,1982-07-27,1.62,70.0,judo,0,0,0 -882847570,Geza Imre,HUN,male,1974-12-23,1.84,75.0,fencing,0,1,1 -962431612,Ghader Mizbani Iranagh,IRI,male,1975-12-06,1.75,67.0,cycling,0,0,0 -331492734,Ghasem Gholamreza Rezaei,IRI,male,1985-08-18,1.85,100.0,wrestling,0,0,1 -827807718,Ghirmay Ghebreslassie,ERI,male,1995-11-14,,,athletics,0,0,0 -173887489,Ghislain Perrier,BRA,male,1987-05-17,1.79,75.0,fencing,0,0,0 -195640295,Ghislaine Landry,CAN,female,1988-04-27,1.63,65.0,rugby sevens,0,0,1 -545908968,Ghofran Ahmed,EGY,male,1992-09-30,1.92,68.0,taekwondo,0,0,0 -76147159,Ghofrane Mohamed,SYR,female,1989-06-06,1.68,60.0,athletics,0,0,0 -49318551,Ghulam Mustafa Bashir,PAK,male,1987-07-04,1.8,74.0,shooting,0,0,0 -810499490,Gi Jung Kim,KOR,male,1990-08-14,1.79,83.0,badminton,0,0,0 -966812060,Gianina Beleaga,ROU,female,1995-05-21,1.78,57.0,rowing,0,0,0 -425384574,Gideoni Monteiro,BRA,male,1989-09-02,1.8,77.0,cycling,0,0,0 -442076859,Giedrius Titenis,LTU,male,1989-07-21,1.93,95.0,aquatics,0,0,0 -826096399,Gift Motupa,RSA,male,1994-09-23,1.76,76.0,football,0,0,0 -684871850,Gil Cohen,ISR,female,1992-07-07,1.7,62.0,sailing,0,0,0 -802471466,Gil Roberts,USA,male,1989-03-15,1.88,81.0,athletics,1,0,0 -515485007,Gilda Casanova,CUB,female,1995-12-19,1.65,54.0,athletics,0,0,0 -63834716,Gilda Maria de Oliveira,BRA,female,1983-08-06,1.69,69.0,wrestling,0,0,0 -48092287,Giles Scott,GBR,male,1987-06-23,1.97,95.0,sailing,1,0,0 -275098717,Gili Cohen,ISR,female,1991-06-19,1.6,52.0,judo,0,0,0 -462314692,Gilles Muller,LUX,male,1983-05-09,1.93,90.0,tennis,0,0,0 -991427076,Gilles Simon,FRA,male,1984-12-27,1.83,70.0,tennis,0,0,0 -156004551,Gillian Sanders,RSA,female,1981-10-15,1.68,53.0,triathlon,0,0,0 -266372600,Gillies Kaka,NZL,male,1990-05-28,1.85,90.0,rugby sevens,0,0,0 -802381327,Gilvan Bitencourt Ribeiro,BRA,male,1989-05-08,1.8,83.0,canoe,0,0,0 -784102100,Gina Bass,GAM,female,1995-05-03,,,athletics,0,0,0 -326826084,Gina Luckenkemper,GER,female,1996-11-21,1.7,56.0,athletics,0,0,0 -64983596,Ginga Munetomo,JPN,male,1994-04-07,1.65,55.0,gymnastics,0,0,0 -436375736,Gintare Scheidt,LTU,female,1982-11-12,1.72,63.0,sailing,0,0,0 -859779804,Giordan Harris,MHL,male,1993-04-19,1.85,99.0,aquatics,0,0,0 -663457782,Giordano Benedetti,ITA,male,1989-05-22,1.89,68.0,athletics,0,0,0 -836669361,Giorgi Chkheidze,GEO,male,1997-10-30,1.78,105.0,weightlifting,0,0,0 -976881370,Giorgia Bordignon,ITA,female,1987-05-24,1.61,63.0,weightlifting,0,0,0 -980482855,Giorgia Bronzini,ITA,female,1983-08-03,1.6,54.0,cycling,0,0,0 -298556273,Giorgio Avola,ITA,male,1989-05-08,1.78,72.0,fencing,0,0,0 -450155404,Giorgio Poggi,ITA,male,1981-08-26,1.86,100.0,sailing,0,0,0 -590407470,Giovana Prado Pass,BRA,female,1998-03-30,1.67,59.0,equestrian,0,0,0 -42773874,Giovani Lo Celso,ARG,male,1996-04-09,1.69,72.0,football,0,0,0 -979844393,Giovanna Pedroso,BRA,female,1998-10-15,1.62,57.0,aquatics,0,0,0 -276844108,Giovanni Abagnale,ITA,male,1995-01-11,1.98,90.0,rowing,0,0,1 -870019483,Giovanni Battista Bardis,FRA,male,1987-05-21,1.77,85.0,weightlifting,0,0,0 -135611970,Giovanni Cernogoraz,CRO,male,1982-12-27,1.86,90.0,shooting,0,0,0 -890980773,Giovanni Codrington,NED,male,1988-07-17,1.77,84.0,athletics,0,0,0 -117303436,Giovanni Pellielo,ITA,male,1970-01-11,1.73,96.0,shooting,0,1,0 -453426505,Giovanni Simeone,ARG,male,1995-07-05,1.68,76.0,football,0,0,0 -611012468,Giovanni Tocci,ITA,male,1994-08-31,1.75,68.0,aquatics,0,0,0 -20576077,Giovanni de Gennaro,ITA,male,1992-07-21,1.85,80.0,canoe,0,0,0 -258007826,Giselle Ansley,GBR,female,1992-03-31,1.76,73.0,hockey,1,0,0 -994296683,Githa Michiels,BEL,female,1983-03-28,1.66,60.0,cycling,0,0,0 -69404061,Giulia Conti,ITA,female,1985-11-04,1.73,62.0,sailing,0,0,0 -820456660,Giulia Emmolo,ITA,female,1991-10-16,1.71,67.0,aquatics,0,1,0 -435542404,Giulia Gorlero,ITA,female,1990-09-26,1.8,73.0,aquatics,0,1,0 -335874891,Giulia Molinaro,ITA,female,1990-07-23,1.78,72.0,golf,0,0,0 -358923417,Giulia Sergas,ITA,female,1979-12-26,1.77,64.0,golf,0,0,0 -475800155,Giulia Steingruber,SUI,female,1994-03-24,1.6,56.0,gymnastics,0,0,1 -446145894,Giulio Dressino,ITA,male,1992-11-05,1.83,75.0,canoe,0,0,0 -231324548,Giuseppe Giordano,ITA,male,1974-07-16,1.7,73.0,shooting,0,0,0 -825126068,Giuseppe Vicino,ITA,male,1993-02-26,1.95,95.0,rowing,0,0,1 -453710519,Gladys Tejeda,PER,female,1985-09-30,1.56,47.0,athletics,0,0,0 -261518671,Glencora McGhie,AUS,female,1988-08-08,1.78,68.0,aquatics,0,0,0 -820466247,Glenn Kable,FIJ,male,1963-05-04,1.86,92.0,shooting,0,0,0 -344080952,Glenn O'Shea,AUS,male,1989-06-14,1.8,76.0,cycling,0,0,0 -801742919,Glenn Ochal,USA,male,1986-03-01,1.94,95.0,rowing,0,0,0 -36388952,Glenn Schuurman,NED,male,1991-04-16,1.83,79.0,hockey,0,0,0 -679206973,Glenn Snyders,NZL,male,1987-04-07,1.79,83.0,aquatics,0,0,0 -585626028,Glenn Surgeloose,BEL,male,1989-09-04,1.82,80.0,aquatics,0,0,0 -914132578,Glenn Sutanto,INA,male,1989-11-07,1.83,74.0,aquatics,0,0,0 -355204683,Glenn Turner,AUS,male,1984-05-01,1.79,82.0,hockey,0,0,0 -482085423,Gloria Asumnu,NGR,female,1985-05-22,1.68,64.0,athletics,0,0,0 -366511914,Gloria Comerma,ESP,female,1987-04-18,1.68,64.0,hockey,0,0,0 -95564568,Gloria Hooper,ITA,female,1992-03-03,1.74,63.0,athletics,0,0,0 -848853741,Gnonsiane Niombla,FRA,female,1990-07-09,1.72,69.0,handball,0,1,0 -138570390,Godfrey Khotso Mokoena,RSA,male,1985-03-06,1.9,76.0,athletics,0,0,0 -56023329,Goitom Kifle,ERI,male,1993-12-03,,,athletics,0,0,0 -599927900,Gojko Pijetlovic,SRB,male,1983-08-07,1.94,92.0,aquatics,1,0,0 -142292684,Golan Pollack,ISR,male,1991-09-10,1.75,66.0,judo,0,0,0 -679226224,Golnoush Sebghatollahi,IRI,female,1990-12-20,1.58,61.0,shooting,0,0,0 -69505234,Gonzalo Carou,ARG,male,1979-08-15,1.9,100.0,handball,0,0,0 -854221966,Gonzalo Carreras,ARG,male,1989-10-26,1.86,88.0,canoe,0,0,0 -532947055,Gonzalo Echenique Saglietti,ESP,male,1990-04-27,1.9,94.0,aquatics,0,0,0 -124979010,Gonzalo Molina,ARG,male,1995-05-05,1.78,83.0,cycling,0,0,0 -67406936,Gonzalo Peillat,ARG,male,1992-08-12,1.77,82.0,hockey,1,0,0 -579541939,Gonzalo Raul Tellechea,ARG,male,1985-07-11,1.74,61.0,triathlon,0,0,0 -897428173,Gonzalo Ruiz de la Cruz,MEX,male,1988-04-28,1.86,87.0,volleyball,0,0,0 -607191429,Gor Minasyan,ARM,male,1994-10-25,1.8,144.0,weightlifting,0,1,0 -612779114,Goran Stojanovic,QAT,male,1977-02-24,1.91,90.0,handball,0,0,0 -667077682,Gorazd Skof,SLO,male,1977-07-11,1.88,90.0,handball,0,0,0 -908126279,Gordon Benson,GBR,male,1994-05-12,1.91,78.0,triathlon,0,0,0 -192247711,Gordon Johnston,CAN,male,1993-01-30,1.87,88.0,hockey,0,0,0 -329155440,Goretti Alejandra Zumaya Flores,MEX,female,1997-05-31,1.65,65.0,shooting,0,0,0 -607509598,Govert Viergever,NED,male,1989-07-29,1.84,85.0,rowing,0,0,0 -702595268,Grace Claxton,PUR,female,1993-08-19,,,athletics,0,0,0 -705783753,Grace Latz,USA,female,1988-02-21,1.83,79.0,rowing,0,0,0 -552672529,Grace Luczak,USA,female,1989-05-24,1.91,77.0,rowing,0,0,0 -313174514,Grace Prendergast,NZL,female,1992-06-30,1.83,73.0,rowing,0,0,0 -555289430,Grace Reid,GBR,female,1996-05-09,1.69,67.0,aquatics,0,0,0 -120616586,Grace Stewart,AUS,female,1997-04-28,1.75,68.0,hockey,0,0,0 -304397961,Grace Wanjiru Njue,KEN,female,1979-01-10,1.62,43.0,athletics,0,0,0 -537810856,Grace Zaadi Deuna,FRA,female,1993-07-07,1.71,66.0,handball,0,1,0 -893023802,Gracie Elvin,AUS,female,1988-10-31,1.75,65.0,cycling,0,0,0 -933428907,Graciele Herrmann,BRA,female,1992-01-01,1.8,64.0,aquatics,0,0,0 -639691475,Graciete Santana,BRA,female,1980-10-12,1.55,43.0,athletics,0,0,0 -792601678,Graeme Saunders,CAN,male,1990-07-19,1.78,75.0,sailing,0,0,0 -929889629,Graham DeLaet,CAN,male,1982-01-22,1.8,75.0,golf,0,0,0 -407795026,Graham Vigrass,CAN,male,1989-06-17,2.05,97.0,volleyball,0,0,0 -104640408,Grant Ferguson,GBR,male,1993-11-15,1.86,64.0,cycling,0,0,0 -636322182,Grant Irvine,AUS,male,1991-03-17,1.87,80.0,aquatics,0,0,0 -298928947,Grant Nel,AUS,male,1988-04-07,1.73,69.0,aquatics,0,0,0 -423041181,Greg Billington,USA,male,1989-05-30,1.76,65.0,triathlon,0,0,0 -267820041,Greg Patrick Broderick,IRL,male,1985-09-21,1.8,50.0,equestrian,0,0,0 -310167929,Greg Rutherford,GBR,male,1986-11-17,1.88,92.0,athletics,0,0,1 -503615176,Greg van Avermaet,BEL,male,1985-05-17,1.81,74.0,cycling,1,0,0 -615982348,Gregor Traber,GER,male,1992-12-02,1.9,80.0,athletics,0,0,0 -127715970,Gregorio Paltrinieri,ITA,male,1994-09-05,1.91,72.0,aquatics,1,0,0 -160584680,Gregory Bauge,FRA,male,1985-01-31,1.81,100.0,cycling,0,0,1 -517601975,Gregory Bourdy,FRA,male,1982-04-25,1.8,70.0,golf,0,0,0 -146969359,Gregory Echenique,VEN,male,1990-11-23,2.06,137.0,basketball,0,0,0 -266128648,Gregory Mallet,FRA,male,1984-03-21,1.96,84.0,aquatics,0,0,0 -389701340,Gregory Vargas,VEN,male,1986-02-18,1.82,90.0,basketball,0,0,0 -342622397,Gremlis Arvelo,VEN,female,1996-08-21,1.67,62.0,table tennis,0,0,0 -695131532,Greysia Polii,INA,female,1987-08-11,1.63,56.0,badminton,0,0,0 -282490475,Griedge Mbock Bathy,FRA,female,1995-02-26,1.72,64.0,football,0,0,0 -952560452,Grigor Dimitrov,BUL,male,1991-05-16,1.91,80.0,tennis,0,0,0 -457130620,Grigori Minaskin,EST,male,1991-02-01,1.78,100.0,judo,0,0,0 -383336831,Grigory Tarasevich,RUS,male,1995-08-01,1.91,75.0,aquatics,0,0,0 -96493170,Grischa Proemel,GER,male,1995-01-09,1.82,78.0,football,0,1,0 -572471810,Griselda Khng,SIN,female,1991-07-31,1.55,60.0,sailing,0,0,0 -326722499,Grit Sadeiko,EST,female,1989-07-29,1.72,59.0,athletics,0,0,0 -96345357,Grzegorz Fijalek,POL,male,1987-05-11,1.85,99.0,volleyball,0,0,0 -491637727,Grzegorz Hedwig,POL,male,1988-07-17,1.79,82.0,canoe,0,0,0 -844475495,Grzegorz Lomacz,POL,male,1987-10-01,1.87,80.0,volleyball,0,0,0 -282268874,Guadalupe Lopez,COL,female,1988-01-12,1.65,58.0,rugby sevens,0,0,0 -466700626,Guan-Lin Yu,TPE,male,1993-11-29,1.71,58.0,archery,0,0,0 -85090026,Guangyuan Li,CHN,male,1997-02-27,1.87,81.0,aquatics,0,0,0 -613886806,Guanjie Johnathan Wong,MAS,male,1992-08-23,1.72,67.0,shooting,0,0,0 -78601659,Guannan Niu,CHN,female,1992-05-10,1.77,68.0,aquatics,0,0,0 -331829339,Gudaf Tsegay,ETH,female,1997-11-23,1.63,50.0,athletics,0,0,0 -576749129,Gudni Valur Gudnason,ISL,male,1995-10-11,1.99,120.0,athletics,0,0,0 -511096047,Gudrun Stock,GER,female,1995-05-23,1.68,62.0,cycling,0,0,0 -233933004,Guendalina Sartori,ITA,female,1988-08-08,1.69,90.0,archery,0,0,0 -100452924,Guham Cho,KOR,male,1992-07-30,1.78,100.0,judo,0,0,0 -220835497,Guido Pella,ARG,male,1990-05-17,1.83,80.0,tennis,0,0,0 -905148205,Guido Vianello,ITA,male,1994-05-09,1.98,,boxing,0,0,0 -405955663,Guilherme Giovannoni,BRA,male,1980-06-02,2.04,100.0,basketball,0,0,0 -196836126,Guilherme Guido,BRA,male,1987-02-12,1.94,90.0,aquatics,0,0,0 -256859725,Guilherme Melaragno,BRA,male,1993-08-09,1.84,88.0,fencing,0,0,0 -304528456,Guilherme Toldo,BRA,male,1992-09-01,1.76,70.0,fencing,0,0,0 -428363139,Guillaume Raineau,FRA,male,1986-06-29,1.87,71.0,rowing,0,0,1 -9637437,Guillermo Duran,ARG,male,1988-06-06,1.78,82.0,tennis,0,0,0 -107880382,Guillermo Molina Rios,ESP,male,1984-03-16,1.95,105.0,aquatics,0,0,0 -70220465,Gulbadam Babamuratova,TKM,female,1991-08-24,1.56,52.0,judo,0,0,0 -699962258,Gulnabat Kadyrova,TKM,female,1994-06-14,1.65,69.0,weightlifting,0,0,0 -192705392,Gulnaz Gubaydullina,RUS,female,1992-02-14,1.64,51.0,modern pentathlon,0,0,0 -15083082,Gulnoza Matniyazova,UZB,female,1994-08-10,1.68,70.0,judo,0,0,0 -126810270,Gulzhanat Zhanatbek,KAZ,female,1991-11-30,1.7,55.0,athletics,0,0,0 -696433108,Gundegmaa Otryad,MGL,female,1978-05-23,1.68,63.0,shooting,0,0,0 -504342645,Gunn-Rita Dahle Flesjaa,NOR,female,1973-02-10,,,cycling,0,0,0 -732208032,Gunnar Bentz,USA,male,1996-01-03,1.96,83.0,aquatics,1,0,0 -151055874,Gunta Latiseva-Cudare,LAT,female,1995-03-09,1.79,68.0,athletics,0,0,0 -970528741,Guojian Dong,CHN,male,1987-03-16,1.7,55.0,athletics,0,0,0 -849428562,Guor Marial,SSD,male,1984-04-15,1.8,60.0,athletics,0,0,0 -174463586,Guowei Zhang,CHN,male,1991-06-04,2.02,77.0,athletics,0,0,0 -695858631,Gurmeet Singh,IND,male,1985-07-01,1.72,60.0,athletics,0,0,0 -773668294,Gurpreet Singh,IND,male,1987-12-19,1.76,81.0,shooting,0,0,0 -335760688,Gustavo Albuquerque,BRA,male,1991-06-28,1.72,85.0,rugby sevens,0,0,0 -905869826,Gustavo Cuesta,DOM,male,1988-11-14,1.73,66.0,athletics,0,0,0 -587811250,Gustavo Guimaraes,BRA,male,1994-01-24,1.8,90.0,aquatics,0,0,0 -17989231,Gustavo Lima,POR,male,1977-07-13,1.85,82.0,sailing,0,0,0 -743751961,Gustavo Tsuboi,BRA,male,1985-05-31,1.7,65.0,table tennis,0,0,0 -833430291,Gustavo Vernes,BRA,male,1993-03-24,,,football,0,0,0 -125360707,Guy-Elphege Anouman,FRA,male,1994-06-13,1.77,75.0,athletics,0,0,0 -369581493,Guzel Manyurova,KAZ,female,1978-01-24,1.74,75.0,wrestling,0,1,0 -904653083,Gwanghee Cho,KOR,male,1993-12-24,1.82,92.0,canoe,0,0,0 -796100413,Gwanghyeok Lee,KOR,male,1995-09-11,,,football,0,0,0 -459485655,Gwanuk Kim,KOR,male,1990-07-22,1.77,90.0,wrestling,0,0,0 -874914826,Gwen Berry,USA,female,1989-06-29,1.76,88.0,athletics,0,0,0 -292122842,Gwen Jorgensen,USA,female,1986-04-25,1.78,58.0,triathlon,1,0,0 -3322965,Gwladys Epangue,FRA,female,1983-08-15,1.78,88.0,taekwondo,0,0,0 -302633574,Gwladys Nocera Pucet,FRA,female,1975-05-22,1.68,62.0,golf,0,0,0 -42510155,Gyorgyi Zsivoczky-Farkas,HUN,female,1985-02-13,1.7,60.0,athletics,0,0,0 -301226095,Ha Na Kim,KOR,female,1989-12-27,1.72,55.0,badminton,0,0,0 -553897082,Habib de Las Salas de la Rosa,COL,male,1987-04-19,1.59,56.0,weightlifting,0,0,0 -962588204,Habiba Ghribi,TUN,female,1984-04-09,1.74,51.0,athletics,0,0,0 -585034068,Habibollah Jomeh Akhlaghi,IRI,male,1985-08-03,1.75,90.0,wrestling,0,0,0 -204254643,Habitam Alemu,ETH,female,1997-07-09,1.71,52.0,athletics,0,0,0 -753076822,Haby Niare,FRA,female,1993-06-26,1.76,66.0,taekwondo,0,1,0 -755601006,Hadir Mekhimar,EGY,female,1997-11-25,1.65,62.0,shooting,0,0,0 -472316019,Hae Mi Park,KOR,female,1990-01-31,1.68,55.0,shooting,0,0,0 -402157963,Hae Ran Kim,KOR,female,1984-03-16,1.68,57.0,volleyball,0,0,0 -188491847,Haein Sim,KOR,female,1987-10-31,1.78,66.0,handball,0,0,0 -16804644,Haeun Yang,KOR,female,1994-02-25,1.71,58.0,table tennis,0,0,0 -861637149,Hafifi Bin Mansor Mohd,MAS,male,1990-10-28,1.66,69.0,weightlifting,0,0,0 -718811090,Hafize Sahin,TUR,female,1992-01-01,1.7,63.0,wrestling,0,0,0 -177699282,Hafsatu Kamara,SLE,female,1991-12-07,,,athletics,0,0,0 -825962890,Hagen Pohle,GER,male,1992-03-05,1.78,64.0,athletics,0,0,0 -461373663,Hagos Gebrhiwet,ETH,male,1994-05-11,1.71,56.0,athletics,0,0,1 -321494163,Haido Alexouli,GRE,female,1991-03-29,1.8,60.0,athletics,0,0,0 -754025410,Haifeng Fu,CHN,male,1984-01-02,1.81,78.0,badminton,1,0,0 -148299848,Hailemariyam Amare,ETH,male,1997-02-22,1.75,64.0,athletics,0,0,0 -48011673,Haiping Liu,CHN,female,1988-06-03,1.73,69.0,canoe,0,0,0 -428133887,Hairim Song,KOR,female,1985-01-12,1.67,67.0,handball,0,0,0 -189517234,Haislan Antonio Veranes Garcia,CAN,male,1983-03-04,1.75,66.0,wrestling,0,0,0 -788703039,Haithem Fahmy Mahmoud,EGY,male,1991-09-23,1.54,59.0,wrestling,0,0,0 -997215061,Haiwei Chen,CHN,male,1994-12-30,1.88,78.0,fencing,0,0,0 -800071001,Haiyan Wu,CHN,female,1993-02-26,1.66,58.0,football,0,0,0 -354275948,Hajar Alkhaldi,BRN,female,1995-03-17,1.6,45.0,athletics,0,0,0 -934255578,Haji Aliyev,AZE,male,1991-04-21,1.68,65.0,wrestling,0,0,1 -931847337,Hakan Dahlby,SWE,male,1965-09-15,1.85,95.0,shooting,0,0,0 -9093283,Hakan Eresker,QAT,male,1994-07-05,1.71,,boxing,0,0,0 -983579784,Hakim Sadi,ALG,male,1992-11-14,1.76,56.0,athletics,0,0,0 -329066484,Haley Anderson,USA,female,1991-11-20,1.78,68.0,aquatics,0,0,0 -587562061,Haley Ruth Augello,USA,female,1994-10-17,1.61,54.0,wrestling,0,0,0 -853405739,Hali Flickinger,USA,female,1994-07-07,1.66,52.0,aquatics,0,0,0 -427032354,Halil Akkas,TUR,male,1983-07-01,1.75,58.0,athletics,0,0,0 -779373345,Halimah Nakaayi,UGA,female,1994-10-16,,,athletics,0,0,0 -556391321,Haline Scatrut,BRA,female,1992-08-09,1.69,64.0,rugby sevens,0,0,0 -911111392,Hamad Ali Mohamed A Al Attiyah,QAT,male,1995-06-23,1.93,89.0,equestrian,0,0,0 -1664652,Hamada Mohamed,EGY,male,1992-10-22,1.75,65.0,athletics,0,0,0 -513005380,Hamada Talat,EGY,male,1981-03-01,1.76,77.0,shooting,0,0,0 -323154002,Hamdan Bayusuf,KEN,male,1994-09-29,1.82,64.0,aquatics,0,0,0 -443393856,Hamdy Moustafa Elsaid Abdelwahab,EGY,male,1993-01-22,1.71,96.0,wrestling,0,0,0 -550868905,Hamed Said Alkhatri,OMA,male,1985-05-24,1.68,90.0,shooting,0,0,0 -531580523,Hamid Ezzine,MAR,male,1983-10-05,1.74,62.0,athletics,0,0,0 -541385072,Hamid Mohammad Soryan,IRI,male,1985-08-24,1.67,64.0,wrestling,0,0,0 -614655928,Hamid Sintes,ALG,male,1980-08-08,1.69,67.0,fencing,0,0,0 -392547032,Hamidreza Zooravand,IRI,male,1990-01-13,1.76,66.0,athletics,0,0,0 -296637442,Hamish Bond,NZL,male,1986-02-13,1.89,89.0,rowing,1,0,0 -661625599,Hamish Carson,NZL,male,1988-11-01,1.81,66.0,athletics,0,0,0 -738803068,Hamish Peacock,AUS,male,1990-10-15,1.85,98.0,athletics,0,0,0 -927364164,Hammadi Ahmed,IRQ,male,1989-10-18,1.77,76.0,football,0,0,0 -589649660,Hamza Alic,BIH,male,1979-01-20,1.86,130.0,athletics,0,0,0 -668979909,Hamza Bouras,ALG,male,1987-12-16,1.7,73.0,sailing,0,0,0 -672961939,Hamza Touba,GER,male,1991-11-06,1.7,,boxing,0,0,0 -857979045,Hamzeh Zarini,IRI,male,1985-10-18,1.98,98.0,volleyball,0,0,0 -360154059,Han Na Gwon,KOR,female,1989-11-22,1.73,65.0,handball,0,0,0 -474085303,Hana Matelova,CZE,female,1990-06-08,1.6,53.0,table tennis,0,0,0 -103924089,Hanami Sekine,JPN,female,1996-02-26,1.56,43.0,athletics,0,0,0 -98339799,Haneen Ibrahim,SUD,female,2000-06-29,1.54,47.0,aquatics,0,0,0 -987592128,Hang Yu Sze,HKG,female,1988-03-05,1.68,63.0,aquatics,0,0,0 -56781844,Haniel Langaro,BRA,male,1995-03-07,1.97,104.0,handball,0,0,0 -281021453,Hanna Blomstrand,SWE,female,1996-08-25,1.73,72.0,handball,0,0,0 -868683543,Hanna Dudzenkova,BLR,female,1994-05-07,1.67,51.0,gymnastics,0,0,0 -648984551,Hanna Harchonak,BLR,female,1992-02-11,1.62,51.0,gymnastics,0,0,0 -302310091,Hanna Hatsko-Fedusova,UKR,female,1990-10-03,1.75,75.0,athletics,0,0,0 -731032288,Hanna Kasyanova,UKR,female,1983-04-24,1.78,67.0,athletics,0,0,0 -865953961,Hanna Kisteleki,HUN,female,1991-03-10,1.72,63.0,aquatics,0,0,0 -846150699,Hanna Klinga,SWE,female,1989-11-13,1.65,65.0,sailing,0,0,0 -750989988,Hanna Knyazyeva-Minenko,ISR,female,1989-09-25,1.79,62.0,athletics,0,0,0 -296210121,Hanna Lyczbinska,POL,female,1990-04-20,1.78,64.0,fencing,0,0,0 -318275998,Hanna Malyshik,BLR,female,1994-02-04,1.75,90.0,athletics,0,0,0 -593771185,Hanna Plotitsyna,UKR,female,1987-01-01,1.82,73.0,athletics,0,0,0 -805368469,Hanna Skydan,AZE,female,1992-05-14,1.83,101.0,athletics,0,0,0 -550222523,Hanna-Maria Seppala,FIN,female,1984-12-13,1.74,60.0,aquatics,0,0,0 -980247583,Hannah Amuchechi Rueben,NGR,female,1994-02-14,1.65,69.0,wrestling,0,0,0 -113266724,Hannah Buckling,AUS,female,1992-06-03,1.77,75.0,aquatics,0,0,0 -679066155,Hannah Cross,AUS,female,1997-01-29,1.69,57.0,aquatics,0,0,0 -503850224,Hannah Darling,CAN,female,1996-05-30,1.74,72.0,rugby sevens,0,0,1 -650100624,Hannah Kruger,GER,female,1988-09-04,1.73,67.0,hockey,0,0,1 -512806453,Hannah Macleod,GBR,female,1984-06-09,1.72,67.0,hockey,1,0,0 -278564208,Hannah Miley,GBR,female,1989-08-08,1.65,53.0,aquatics,0,0,0 -931174391,Hannah Mills,GBR,female,1988-02-29,1.57,50.0,sailing,1,0,0 -888831505,Hannah Wilkinson,NZL,female,1992-05-28,1.77,72.0,football,0,0,0 -467874431,Hanne Grahns,SWE,female,1992-08-29,1.67,64.0,football,0,0,0 -588728248,Hannes Aigner,GER,male,1989-03-19,1.83,75.0,canoe,0,0,0 -633655102,Hannes Obreno,BEL,male,1991-03-08,1.87,83.0,rowing,0,0,0 -828126181,Hannes Ocik,GER,male,1991-06-08,1.91,93.0,rowing,0,1,0 -707380480,Hannibal Gaskin,GUY,male,1997-08-30,1.73,68.0,aquatics,0,0,0 -759916596,Hans Arne Jensen,TGA,male,1998-02-25,1.81,95.0,archery,0,0,0 -887983421,Hans Gruhne,GER,male,1988-08-05,1.93,92.0,rowing,1,0,0 -157732723,Hans Lindberg,DEN,male,1981-08-01,1.88,88.0,handball,0,0,0 -191768973,Hans Peter Minderhoud,NED,male,1973-10-07,1.9,75.0,equestrian,0,0,0 -14222550,Hans Podlipnik-Castillo,CHI,male,1988-01-09,1.84,79.0,tennis,0,0,0 -35658013,Hans Struzyna,USA,male,1989-03-31,1.88,90.0,rowing,0,0,0 -326027910,Hansol Kim,KOR,male,1995-12-29,1.65,,gymnastics,0,0,0 -710838348,Hansu Ryu,KOR,male,1988-02-01,1.68,70.0,wrestling,0,0,0 -400204739,Hanwoong Park,KOR,male,1995-01-15,1.74,92.0,weightlifting,0,0,0 -462031803,Hao Chang,TPE,male,1990-11-14,1.73,72.0,sailing,0,0,0 -955199694,Hao Liu,CHN,male,1988-11-07,1.89,79.0,cycling,0,0,0 -688410388,Hao You,CHN,male,1992-04-26,1.62,60.0,gymnastics,0,0,1 -5582422,Hao-Ching Chan,TPE,female,1993-09-19,1.8,65.0,tennis,0,0,0 -584970001,Hao-Wen Kao,TPE,male,1995-03-17,1.8,82.0,archery,0,0,0 -784558254,Haoran Yang,CHN,male,1996-02-22,1.76,64.0,shooting,0,0,0 -92737551,Haotong Li,CHN,male,1995-08-03,1.88,75.0,golf,0,0,0 -7926077,Haram Woo,KOR,male,1998-03-21,1.68,58.0,aquatics,0,0,0 -904216148,Hardeep Hardeep,IND,male,1990-12-20,1.63,98.0,wrestling,0,0,0 -498857610,Hari Kumar Rimal,NEP,male,1987-06-13,1.66,59.0,athletics,0,0,0 -119602517,Haris Bandey,PAK,male,1999-02-14,1.67,79.0,aquatics,0,0,0 -218421111,Haris Belkebla,ALG,male,1994-01-28,1.78,70.0,football,0,0,0 -599462011,Harmanpreet Singh,IND,male,1996-01-06,1.77,70.0,hockey,0,0,0 -112459852,Harold Correa,FRA,male,1988-06-26,1.9,81.0,athletics,0,0,0 -907600917,Harold Fonseca,HON,male,1993-10-08,1.86,87.0,football,0,0,0 -370604205,Harold Houston,BER,male,1990-03-23,1.82,82.0,athletics,0,0,0 -98959856,Harold Langen,NED,male,1986-10-27,1.88,90.0,rowing,0,0,0 -782948245,Harold Preciado,COL,male,1994-06-01,1.85,73.0,football,0,0,0 -331514167,Haron Koech,KEN,male,1990-01-27,1.9,76.0,athletics,0,0,0 -213592688,Harrie Smolders,NED,male,1980-05-10,1.83,75.0,equestrian,0,0,0 -98629225,Harrison Barnes,USA,male,1992-05-30,2.03,102.0,basketball,1,0,0 -883054500,Harry Aikines-Aryeetey,GBR,male,1988-08-29,1.78,87.0,athletics,0,0,0 -666553660,Harry Martin,GBR,male,1992-10-23,1.84,81.0,hockey,0,0,0 -588715133,Haruka Miyashita,JPN,female,1994-09-01,1.77,61.0,volleyball,0,0,0 -437669454,Haruka Tachimoto,JPN,female,1990-08-03,1.68,70.0,judo,1,0,0 -946440750,Harukyo Nomura,JPN,female,1992-11-25,1.65,60.0,golf,0,0,0 -772429717,Harutyun Merdinyan,ARM,male,1984-08-16,1.65,58.0,gymnastics,0,0,0 -702338556,Haruyo Shimamura,JPN,female,1992-03-04,1.82,79.0,volleyball,0,0,0 -542430668,Hasanboy Dusmatov,UZB,male,1993-06-24,1.56,,boxing,1,0,0 -741535794,Hasnaa Lachgar,MAR,female,1989-09-27,1.64,,boxing,0,0,0 -36162949,Hassan Aliazam Yazdanicharati,IRI,male,1994-12-26,1.81,74.0,wrestling,1,0,0 -101286340,Hassan Amzile,FRA,male,1988-06-05,1.83,,boxing,0,0,0 -312704439,Hassan Chani,BRN,male,1988-05-05,1.7,60.0,athletics,0,0,0 -456522076,Hassan Mabrouk,QAT,male,1982-07-29,1.9,100.0,handball,0,0,0 -175315541,Hassan Mead,USA,male,1991-06-28,1.88,65.0,athletics,0,0,0 -97563452,Hassan Mohamed Mahmoud,EGY,male,1984-02-10,,,athletics,0,0,0 -173152080,Hassan Ndam Njikam,CMR,male,1984-02-18,1.82,,boxing,0,0,0 -147777095,Hassan Saada,MAR,male,1994-01-02,1.83,,boxing,0,0,0 -644229404,Hassan Saaid,MDV,male,1992-03-04,,,athletics,0,0,0 -659504471,Hassan Sabzali Rahimi,IRI,male,1989-06-15,1.65,62.0,wrestling,0,0,1 -109499078,Hassan Taftian,IRI,male,1993-05-04,1.87,68.0,athletics,0,0,0 -240509151,Hassanine Sebei,TUN,male,1984-01-21,1.77,60.0,athletics,0,0,0 -796467757,Hassen Chaktami,TUN,male,1988-12-14,1.8,,boxing,0,0,0 -101394753,Havard Haukenes,NOR,male,1990-04-22,,,athletics,0,0,0 -554006309,Hawbir Khasro,IRQ,male,1993-09-24,1.75,70.0,football,0,0,0 -631521529,Hayat Lambarki,MAR,female,1988-05-18,1.72,57.0,athletics,0,0,0 -346469460,Hayden Phillips,NZL,male,1998-02-06,1.8,72.0,hockey,0,0,0 -927855997,Hayden Roulston,NZL,male,1981-01-10,1.86,78.0,cycling,0,0,0 -662093898,Hayder Shkara,AUS,male,1990-05-21,1.84,80.0,taekwondo,0,0,0 -400928274,Haydy Morsy,EGY,female,1999-09-20,1.68,58.0,modern pentathlon,0,0,0 -267258552,Hayle Ibrahimov,AZE,male,1990-01-18,1.71,58.0,athletics,0,0,0 -842681952,Haziq Kamaruddin,MAS,male,1993-07-04,1.78,75.0,archery,0,0,0 -935038843,Hazuki Nagai,JPN,female,1994-08-15,1.52,53.0,hockey,0,0,0 -420841500,Hazuki Yuda,JPN,female,1989-07-11,1.67,54.0,hockey,0,0,0 -490580796,Heabin Jung,KOR,female,1994-01-20,1.69,64.0,hockey,0,0,0 -348445577,Heather Bansley,CAN,female,1987-09-13,1.71,65.0,volleyball,0,0,0 -530012228,Heather Fisher,GBR,female,1984-06-13,1.68,71.0,rugby sevens,0,0,0 -865048976,Heather Miller-Koch,USA,female,1987-03-30,1.76,63.0,athletics,0,0,0 -782975512,Heather O Reilly,USA,female,1985-01-02,1.66,59.0,football,0,0,0 -136391039,Heather Olver,GBR,female,1986-03-15,1.7,,badminton,0,0,0 -605175756,Heather Stanning,GBR,female,1985-01-26,1.81,72.0,rowing,1,0,0 -388913953,Heather Steacy,CAN,female,1988-04-14,1.75,80.0,athletics,0,0,0 -378577718,Heather Watson,GBR,female,1992-05-19,1.68,63.0,tennis,0,0,0 -677773211,Heba Allejji,SYR,female,1997-01-20,,,table tennis,0,0,0 -847163019,Hebert Brol,GUA,male,1980-01-10,1.81,135.0,shooting,0,0,0 -335558674,Hector Luis Garcia Mora,DOM,male,1991-11-01,1.73,,boxing,0,0,0 -788560912,Hedaya Wahba,EGY,female,1993-04-21,1.74,57.0,taekwondo,0,0,1 -869886406,Hedda Hynne,NOR,female,1990-03-13,,,athletics,0,0,0 -923891732,Hederson Estefani,BRA,male,1991-09-11,1.83,65.0,athletics,0,0,0 -303803370,Hedi Gharbi,TUN,male,1969-08-05,1.69,70.0,sailing,0,0,0 -307748741,Hedvig Karakas,HUN,female,1990-02-21,1.67,61.0,judo,0,0,0 -941808202,Hedvig Lindahl,SWE,female,1983-04-29,1.79,74.0,football,0,1,0 -134790267,Hedvig Rasmussen,DEN,female,1993-12-22,1.87,79.0,rowing,0,0,1 -388831240,Hee Sook Jeon,KOR,female,1984-06-16,1.69,60.0,fencing,0,0,0 -456268808,Hee Young Yang,KOR,female,1989-07-28,1.73,77.0,golf,0,0,0 -393939547,Heechan Hwang,KOR,male,1996-01-26,1.77,70.0,football,0,0,0 -812515999,Heejin Kim,KOR,female,1991-04-29,1.85,78.0,volleyball,0,0,0 -505168885,Heena Sidhu,IND,female,1989-08-29,1.58,51.0,shooting,0,0,0 -769324470,Heesun Jang,KOR,female,1986-05-31,1.64,60.0,hockey,0,0,0 -76022675,Heidi Diethelm Gerber,SUI,female,1969-03-20,1.68,93.0,shooting,0,0,1 -563435764,Heidi Gan,MAS,female,1988-10-08,1.62,57.0,aquatics,0,0,0 -528568211,Heidi Loke,NOR,female,1982-12-12,1.73,,handball,0,0,1 -279097439,Heiki Nabi,EST,male,1985-06-06,1.93,116.0,wrestling,0,0,0 -942170112,Heissler Guillent,VEN,male,1986-12-17,1.83,79.0,basketball,0,0,0 -42902988,Hela Ayari,TUN,female,1994-08-26,,52.0,judo,0,0,0 -419568025,Hela Riabi,TUN,female,1987-02-18,1.65,63.0,wrestling,0,0,0 -927679644,Helah Jelagat Kiprop,KEN,female,1985-04-07,1.6,50.0,athletics,0,0,0 -823760024,Helalia Johannes,NAM,female,1980-08-13,1.65,46.0,athletics,0,0,0 -231174935,Helder Silva,POR,male,1987-08-02,1.76,73.0,canoe,0,0,0 -820626440,Helen Glover,GBR,female,1986-06-17,1.77,67.0,rowing,1,0,0 -77008314,Helen Grobert,GER,female,1992-04-11,1.75,58.0,cycling,0,0,0 -879616037,Helen Jenkins,GBR,female,1984-03-08,1.69,55.0,triathlon,0,0,0 -992478807,Helen Louise Maroulis,USA,female,1991-09-19,1.63,56.0,wrestling,1,0,0 -826011682,Helen Richardson-Walsh,GBR,female,1981-09-23,1.65,55.0,hockey,1,0,0 -707272550,Helena Casas Roige,ESP,female,1988-07-24,1.63,63.0,cycling,0,0,0 -833977823,Helena Ciak,FRA,female,1989-12-15,1.97,89.0,basketball,0,0,0 -659637294,Helena Gasson,NZL,female,1994-12-08,1.72,76.0,aquatics,0,0,0 -914156027,Helena Scutt,USA,female,1992-06-15,1.71,65.0,sailing,0,0,0 -382923791,Helene Defrance,FRA,female,1986-08-11,1.79,66.0,sailing,0,0,1 -189823124,Helene Lefebvre,FRA,female,1991-02-26,1.7,69.0,rowing,0,0,0 -76511268,Helibelton Palacios,COL,male,1993-06-11,1.8,77.0,football,0,0,0 -447903618,Hellen Onsando Obiri,KEN,female,1989-12-13,1.6,50.0,athletics,0,1,0 -177603663,Heming Hu,AUS,male,1994-03-21,1.79,76.0,table tennis,0,0,0 -634678225,Hemza Haloui,ALG,male,1994-07-10,1.8,96.0,wrestling,0,0,0 -686631480,Hendra Purnama,INA,male,1997-01-12,1.69,64.0,archery,0,0,0 -339509053,Hendra Setiawan,INA,male,1984-08-25,1.83,82.0,badminton,0,0,0 -637196931,Hendrik Pekeler,GER,male,1991-07-02,2.03,103.0,handball,0,0,1 -623376345,Henk Grol,NED,male,1985-04-14,1.9,100.0,judo,0,0,0 -431941649,Henna Katarina Johansson,SWE,female,1991-05-01,1.64,67.0,wrestling,0,0,0 -280401602,Henning Bommel,GER,male,1983-02-23,1.83,79.0,cycling,0,0,0 -200432555,Henri Hurskainen,SWE,male,1986-09-13,1.84,80.0,badminton,0,0,0 -777353303,Henri Junghaenel,GER,male,1988-02-05,1.79,80.0,shooting,1,0,0 -257515881,Henri Schoeman,RSA,male,1991-10-03,1.7,59.0,triathlon,0,0,1 -814312572,Henricho Bruintjies,RSA,male,1993-07-16,1.79,72.0,athletics,0,0,0 -761427990,Henriette Hansen,DEN,female,1982-04-15,1.7,66.0,canoe,0,0,0 -530692898,Henriette Nadege Koulla,CMR,female,1992-09-14,1.69,67.0,volleyball,0,0,0 -136465545,Henrik Christiansen,NOR,male,1996-10-09,,,aquatics,0,0,0 -706752580,Henrik Ingebrigtsen,NOR,male,1991-02-24,,,athletics,0,0,0 -808654438,Henrik Mollgaard Jensen,DEN,male,1985-01-02,1.97,102.0,handball,1,0,0 -471800036,Henrik Rummel,USA,male,1987-09-26,1.96,97.0,rowing,0,0,0 -278653669,Henrik Stenson,SWE,male,1976-04-05,1.87,90.0,golf,0,1,0 -89814850,Henrik Toft Hansen,DEN,male,1986-12-18,2.0,105.0,handball,1,0,0 -225165978,Henrik Vasbanyai,HUN,male,1991-07-16,1.87,84.0,canoe,0,0,0 -683861433,Henrik von Eckermann,SWE,male,1981-05-25,1.86,77.0,equestrian,0,0,0 -142941975,Henrikas Zustautas,LTU,male,1994-07-13,1.92,96.0,canoe,0,0,0 -683131991,Henrique Avancini,BRA,male,1989-03-30,1.76,67.0,cycling,0,0,0 -66716324,Henrique Haddad,BRA,male,1987-05-28,1.65,63.0,sailing,0,0,0 -473340391,Henrique Marques,BRA,male,1996-09-24,1.78,71.0,fencing,0,0,0 -715314680,Henrique Martins,BRA,male,1991-11-14,1.8,73.0,aquatics,0,0,0 -991563365,Henrique Rodrigues,BRA,male,1991-02-04,1.94,86.0,aquatics,0,0,0 -484566672,Henrique Teixeira,BRA,male,1989-02-27,1.92,100.0,handball,0,0,0 -378817615,Henry Frayne,AUS,male,1990-04-14,1.88,83.0,athletics,0,0,0 -771439495,Henry Hutchison,AUS,male,1997-02-12,1.76,86.0,rugby sevens,0,0,0 -656087164,Henry Weir,GBR,male,1990-02-13,1.76,75.0,hockey,0,0,0 -196907702,Henryk Szost,POL,male,1982-01-20,1.86,70.0,athletics,0,0,0 -672188106,Hensley Paulina,NED,male,1993-06-26,1.82,77.0,athletics,0,0,0 -674181723,Hermenegildo Leite,ANG,male,2000-05-17,1.71,63.0,athletics,0,0,0 -474532217,Hernan Moises Viera Espinoza,PER,male,1993-01-16,1.76,104.0,weightlifting,0,0,0 -875801164,Hersony Canelon,VEN,male,1988-12-08,1.76,73.0,cycling,0,0,0 -682402469,Heungmin Son,KOR,male,1992-07-08,1.83,67.0,football,0,0,0 -761606260,Hexin Yu,CHN,male,1996-01-01,1.94,87.0,aquatics,0,0,0 -11002844,Hicham Bouchicha,ALG,male,1989-05-19,1.82,64.0,athletics,0,0,0 -730005244,Hicham Sigueni,MAR,male,1993-01-30,1.72,61.0,athletics,0,0,0 -811166663,Hidde Turkstra,NED,male,1988-03-05,1.94,81.0,hockey,0,0,0 -412609068,Hideki Omoto,JPN,male,1984-08-12,1.79,70.0,rowing,0,0,0 -251596711,Hidilyn Diaz,PHI,female,1991-02-20,1.49,53.0,weightlifting,0,1,0 -35937798,Higor Alves,BRA,male,1994-02-23,1.83,64.0,athletics,0,0,0 -708328857,Hilal Hemed Hilal,TAN,male,1994-07-12,1.76,63.0,aquatics,0,0,0 -144358597,Hilary Caldwell,CAN,female,1991-03-13,1.73,61.0,aquatics,0,0,1 -718411923,Hilary Stellingwerff,CAN,female,1981-08-07,1.6,47.0,athletics,0,0,0 -689390939,Hilda Carlen,SWE,female,1991-08-13,1.73,75.0,football,0,1,0 -668140506,Hillary Bor,USA,male,1989-11-22,1.71,52.0,athletics,0,0,0 -256534193,Hin Chun Chiu,HKG,male,1994-08-20,1.76,73.0,rowing,0,0,0 -309810148,Hind Jamili,MAR,female,1998-12-11,1.56,50.0,canoe,0,0,0 -895035478,Hiroaki Takao,JPN,male,1992-01-02,1.56,56.0,weightlifting,0,0,0 -884615594,Hirokatsu Tayama,JPN,male,1981-11-12,1.67,62.0,triathlon,0,0,0 -378183689,Hiroki Fujiharu,JPN,male,1988-11-28,1.75,60.0,football,0,0,0 -361521049,Hiroki Ogita,JPN,male,1987-12-30,1.86,80.0,athletics,0,0,0 -198161931,Hiromasa Fujimori,JPN,male,1991-08-07,1.76,72.0,aquatics,0,0,0 -441329153,Hiromi Miyake,JPN,female,1985-11-18,1.45,48.0,weightlifting,0,0,1 -361129434,Hirooki Arai,JPN,male,1988-05-18,1.8,62.0,athletics,0,0,1 -569753343,Hiroshi Nakano,JPN,male,1987-12-01,1.76,72.0,rowing,0,0,0 -689852563,Hiroyuki Endo,JPN,male,1986-12-16,1.71,72.0,badminton,0,0,0 -182022040,Hirving Lozano,MEX,male,1995-07-30,1.74,63.0,football,0,0,0 -327247777,Hisanori Kitajima,JPN,male,1984-10-16,1.71,55.0,athletics,0,0,0 -26054325,Hisayoshi Harasawa,JPN,male,1992-07-03,1.91,125.0,judo,0,1,0 -83035378,Hiskel Tewelde,ERI,male,1986-09-15,,,athletics,0,0,0 -924061459,Hiwot Ayalew,ETH,female,1990-03-06,1.73,53.0,athletics,0,0,0 -504200446,Hnialum Ruat Feli,IND,female,1996-07-15,1.62,54.0,hockey,0,0,0 -719133572,Ho Ching Lee,HKG,female,1992-11-24,1.6,53.0,table tennis,0,0,0 -757948146,Hoi Kem Doo,HKG,female,1996-11-27,1.66,60.0,table tennis,0,0,0 -289609999,Hoi Wah Chau,HKG,female,1986-06-05,1.65,61.0,badminton,0,0,0 -508366658,Hojamuhammet Toychyyev,TKM,male,1992-01-16,1.86,145.0,weightlifting,0,0,0 -789088022,Holder da Silva,GBS,male,1988-01-12,1.82,80.0,athletics,0,0,0 -261349975,Hollie Webb,GBR,female,1990-09-19,1.65,65.0,hockey,1,0,0 -859525144,Holly Bradshaw,GBR,female,1991-11-02,1.75,67.0,athletics,0,0,0 -923396311,Holly Lincoln-Smith,AUS,female,1988-03-26,1.83,82.0,aquatics,0,0,0 -196810717,Homiyu Tesfaye,GER,male,1993-06-23,1.84,64.0,athletics,0,0,0 -958706021,Hong Liu,CHN,female,1987-05-12,1.6,50.0,athletics,1,0,0 -353716042,Hongpin Huang,CHN,female,1989-04-23,1.95,98.0,basketball,0,0,0 -90017888,Hongxia Li,CHN,female,1986-09-10,1.68,63.0,hockey,0,0,0 -653150622,Hope Solo,USA,female,1981-07-30,1.75,67.0,football,0,0,0 -172023688,Horacio Nava,MEX,male,1982-01-20,1.81,65.0,athletics,0,0,0 -619522770,Horia Tecau,ROU,male,1985-01-19,1.92,90.0,tennis,0,1,0 -707205572,Hortance Diedhiou,SEN,female,1983-08-19,1.65,57.0,judo,0,0,0 -782647147,Hortence Vanessa Mballa Atangana,CMR,female,1992-01-05,1.71,78.0,judo,0,0,0 -816766723,Hosam Hussein Bakr Abdin,EGY,male,1985-10-26,1.8,,boxing,0,0,0 -635210027,Hossam Abdalla,EGY,male,1988-02-16,2.03,97.0,volleyball,0,0,0 -452412504,Houari Ferhani,ALG,male,1993-02-11,1.68,60.0,football,0,0,0 -14950030,Houd Zourdani,ALG,male,1993-10-17,1.65,66.0,judo,0,0,0 -468309568,Houda Miled,TUN,female,1987-02-08,1.71,70.0,judo,0,0,0 -281360452,Houleye Ba,MTN,female,1992-07-17,1.7,55.0,athletics,0,0,0 -111077944,Houry Gebeshian,ARM,female,1989-07-27,1.52,48.0,gymnastics,0,0,0 -61502,Hovhannes Bachkov,ARM,male,1992-12-02,1.73,,boxing,0,0,0 -374222882,Hovhannes Davtyan,ARM,male,1983-11-25,1.73,60.0,judo,0,0,0 -426998976,Howard Grotts,USA,male,1993-01-12,1.71,58.0,cycling,0,0,0 -532364177,Howard Shu,USA,male,1990-11-28,1.86,74.0,badminton,0,0,0 -474611032,Hrachik Babayan,ARM,male,1996-08-01,1.75,75.0,shooting,0,0,0 -578319115,Hrafnhildur Luthersdottir,ISL,female,1991-08-02,1.78,65.0,aquatics,0,0,0 -893777759,Hrisoula Anagnostopoulou,GRE,female,1991-08-27,1.75,85.0,athletics,0,0,0 -751452389,Hristiana Todorova,BUL,female,1994-11-28,1.75,57.0,gymnastics,0,0,1 -834856987,Hristoforos Merousis,GRE,male,1982-03-22,1.78,72.0,athletics,0,0,0 -170401459,Hrvoje Sep,CRO,male,1986-02-26,1.75,,boxing,0,0,0 -529837353,Hsing-Chun Kuo,TPE,female,1993-11-26,1.57,58.0,weightlifting,0,0,1 -207495575,Hsuan-Yen Lee,TPE,male,1993-05-13,1.73,63.0,aquatics,0,0,0 -744184028,Hsuan-Yu Wendy Chen,AUS,female,1993-06-01,1.67,56.0,badminton,0,0,0 -312441381,Hua Wilfried Koffi,CIV,male,1987-10-12,1.89,76.0,athletics,0,0,0 -830965009,Huai-Hsuan Huang,TPE,female,1997-07-07,1.68,53.0,taekwondo,0,0,0 -567560651,Huanhuan Ma,CHN,female,1990-01-13,1.78,66.0,aquatics,0,0,0 -424934520,Hubertus Schmidt,GER,male,1959-10-08,1.83,80.0,equestrian,0,0,0 -743741137,Hugo Barrette,CAN,male,1991-07-04,1.75,90.0,cycling,0,0,0 -402716810,Hugo Boucheron,FRA,male,1993-05-30,1.95,90.0,rowing,0,0,0 -626936383,Hugo Calderano,BRA,male,1996-06-22,1.82,74.0,table tennis,0,0,0 -551778014,Hugo Gonzalez de Oliveira,ESP,male,1999-02-19,1.92,82.0,aquatics,0,0,0 -823618365,Hugo Houle,CAN,male,1990-09-27,1.83,69.0,cycling,0,0,0 -931957390,Hugo Inglis,NZL,male,1991-01-18,1.78,74.0,hockey,0,0,0 -365733362,Hugo Parisi,BRA,male,1984-08-01,1.72,69.0,aquatics,0,0,0 -362810248,Hugo de Sousa,BRA,male,1987-03-05,1.87,74.0,athletics,0,0,0 -20467543,Hugues Fournel,CAN,male,1988-08-05,1.7,73.0,canoe,0,0,0 -874312122,Hui Cao,CHN,female,1991-09-07,1.75,70.0,archery,0,0,0 -825117534,Huihui Lyu,CHN,female,1989-06-26,1.71,70.0,athletics,0,0,0 -552569008,Huijun Lin,CHN,female,1993-02-01,1.7,59.0,athletics,0,0,0 -628637344,Huilin Le,CHN,female,1989-04-01,1.72,62.0,fencing,0,0,0 -595115702,Huisol Lee,KOR,female,1989-08-27,1.74,119.0,weightlifting,0,0,0 -203027250,Huixia Liu,CHN,female,1997-11-30,1.57,48.0,aquatics,1,0,0 -396732861,Humam Tareq,IRQ,male,1996-02-10,1.7,67.0,football,0,0,0 -135011760,Humphrey Kayange,KEN,male,1982-07-20,1.93,106.0,rugby sevens,0,0,0 -733102728,Hung-Chieh Chiang,TPE,male,1989-02-22,1.8,70.0,table tennis,0,0,0 -601326293,Huriana Manuel,NZL,female,1986-08-08,1.66,65.0,rugby sevens,0,1,0 -66237669,Hurshid Tojibaev,UZB,male,1989-11-13,1.72,,boxing,0,0,0 -509019365,Huseyin Kandemir,TUR,male,1986-09-09,1.76,70.0,rowing,0,0,0 -703246010,Hussain Shah Shah,PAK,male,1993-06-08,1.9,100.0,judo,0,0,0 -730071504,Hussein Al-Aameri,IRQ,male,1990-11-24,1.77,80.0,judo,0,0,0 -2983955,Hussein Iashaish,JOR,male,1995-08-06,1.8,,boxing,0,0,0 -573701625,Huyen Ta Thanh,VIE,female,1994-05-03,1.7,58.0,rowing,0,0,0 -531592086,Hye Lyoung Han,KOR,female,1986-01-15,1.63,56.0,hockey,0,0,0 -288648218,Hye Seon Yeum,KOR,female,1991-02-03,1.76,65.0,volleyball,0,0,0 -739082177,Hye-Gyong Kim,PRK,female,1993-03-09,1.53,42.0,athletics,0,0,0 -398649308,Hye-Song Kim,PRK,female,1993-03-09,1.53,42.0,athletics,0,0,0 -887795017,Hyejin Chang,KOR,female,1987-05-13,1.58,50.0,archery,2,0,0 -152186180,Hyejin Cho,KOR,female,1995-01-16,1.59,56.0,hockey,0,0,0 -328642843,Hyejin Lee,KOR,female,1992-01-23,1.65,61.0,cycling,0,0,0 -68832418,Hyeonjun Kim,KOR,male,1992-10-18,1.75,59.0,shooting,0,0,0 -861437248,Hyeonwoo Kim,KOR,male,1988-11-06,1.73,80.0,wrestling,0,0,1 -733898810,Hyeri Oh,KOR,female,1988-04-30,1.82,69.0,taekwondo,1,0,0 -242202885,Hyo Hee Lee,KOR,female,1980-09-09,1.73,57.0,volleyball,0,0,0 -294430163,Hyo Jin Yang,KOR,female,1989-12-14,1.9,72.0,volleyball,0,0,0 -641596822,Hyo Sim Choe,PRK,female,1993-12-05,1.59,62.0,weightlifting,0,1,0 -641299632,Hyoju An,KOR,female,1987-11-25,1.68,54.0,hockey,0,0,0 -871219266,Hyon Gyong Kim,PRK,female,1995-04-17,1.53,48.0,wrestling,0,0,0 -146721591,Hyowon Suh,KOR,female,1987-05-10,1.59,54.0,table tennis,0,0,0 -163022401,Hyunhee Nam,KOR,female,1981-09-29,1.55,46.0,fencing,0,0,0 -996028848,Hyunji Kim,KOR,female,1993-11-04,1.7,52.0,hockey,0,0,0 -925362927,Hyunji Yoo,KOR,female,1984-07-16,1.75,67.0,handball,0,0,0 -99215460,Hyunjun Suk,KOR,male,1991-06-29,1.9,82.0,football,0,0,0 -526973620,Hyunsoo Jang,KOR,male,1991-09-28,1.87,77.0,football,0,0,0 -667171291,Hyunsub Kim,KOR,male,1985-05-31,1.77,61.0,athletics,0,0,0 -502834861,Hyvin Kiyeng Jepkemoi,KEN,female,1992-01-13,1.62,42.0,athletics,0,1,0 -794514120,I Ketut Ariana,INA,male,1989-09-06,1.67,69.0,weightlifting,0,0,0 -710339664,I-Ching Cheng,TPE,female,1992-02-15,1.62,52.0,table tennis,0,0,0 -113806395,Iacovos Hadjiconstantinou,CYP,male,1994-11-17,1.94,90.0,aquatics,0,0,0 -74843032,Iago Lopez Marra,ESP,male,1990-04-07,1.81,81.0,sailing,0,0,0 -45266900,Iain Jensen,AUS,male,1988-05-23,1.86,80.0,sailing,0,1,0 -471378978,Iain Lewers,GBR,male,1984-01-05,1.83,79.0,hockey,0,0,0 -364965310,Iain Smythe,CAN,male,1985-06-02,1.8,77.0,hockey,0,0,0 -909245021,Iakiv Khammo,UKR,male,1994-06-11,1.88,,judo,0,0,0 -879442986,Iakobi Kajaia,GEO,male,1993-09-28,1.87,130.0,wrestling,0,0,0 -158469592,Ian Borrows,AUS,male,1989-11-26,1.86,78.0,canoe,0,0,0 -737845937,Ian Lariba,PHI,female,1994-10-13,1.62,56.0,table tennis,0,0,0 -408716601,Ian Matos,BRA,male,1989-04-24,1.71,72.0,aquatics,0,0,0 -712859551,Ian Sloan,GBR,male,1993-11-19,1.75,76.0,hockey,0,0,0 -224119413,Ian Stannard,GBR,male,1987-05-25,1.91,80.0,cycling,0,0,0 -681120192,Iaroslav Potapov,RUS,male,1999-07-01,1.88,72.0,aquatics,0,0,0 -372106126,Iaroslava Iakushina,RUS,female,1993-06-24,1.7,,boxing,0,0,0 -884378619,Ibragim Labazanov,RUS,male,1987-09-15,1.64,64.0,wrestling,0,0,0 -971860435,Ibrahim Bolukbasi,TUR,male,1990-12-01,1.87,97.0,wrestling,0,0,0 -921428771,Ibrahim Elmasry,EGY,male,1989-03-11,1.91,82.0,handball,0,0,0 -580680998,Ibrahim Khalaf,JOR,male,1986-07-09,1.8,90.0,judo,0,0,0 -564969731,Ibrahim Nishwan,MDV,male,1997-06-12,,,aquatics,0,0,0 -350168886,Ibrahim Ramadan Ibrahim Abdelbaki,EGY,male,1988-02-06,1.73,77.0,weightlifting,0,0,0 -488321463,Ibrahim Saidau,BLR,male,1985-03-09,1.8,125.0,wrestling,0,0,1 -399888791,Ibtihaj Muhammad,USA,female,1985-12-04,1.71,68.0,fencing,0,0,1 -59350988,Ida Alstad,NOR,female,1985-06-13,1.72,,handball,0,0,1 -304264901,Ida Lindborg,SWE,female,1994-06-13,1.67,60.0,aquatics,0,0,0 -705937798,Ida Marko-Varga,SWE,female,1985-03-10,1.7,65.0,aquatics,0,0,0 -412225321,Ida Mayrin,ISR,female,1997-10-30,1.72,52.0,gymnastics,0,0,0 -130996491,Ida Villumsen,DEN,female,1994-11-30,1.76,70.0,canoe,0,0,0 -445744437,Idalys Ortiz,CUB,female,1989-09-27,1.8,82.0,judo,0,1,0 -664747134,Iera Echebarria,ESP,female,1992-10-20,1.6,63.0,rugby sevens,0,0,0 -953620490,Ieuan Lloyd,GBR,male,1993-07-09,1.94,91.0,aquatics,0,0,0 -262282271,Ieva Serapinaite,LTU,female,1995-02-04,1.75,62.0,modern pentathlon,0,0,0 -693361470,Ievegeniia Tetelbaum,ISR,female,1991-07-31,1.58,48.0,aquatics,0,0,0 -914405287,Ievgeniia Nimchenko,UKR,female,1992-09-29,1.8,68.0,rowing,0,0,0 -49708578,Iga Baumgart,POL,female,1989-04-11,1.78,58.0,athletics,0,0,0 -841939942,Ignacio Martin,ESP,male,1983-10-15,1.88,90.0,rugby sevens,0,0,0 -911577483,Ignacio Morales,CHI,male,1995-08-12,1.79,62.0,taekwondo,0,0,0 -632290179,Ignacio Ortiz,ARG,male,1987-07-26,1.8,76.0,hockey,1,0,0 -877477413,Ignacio Perrin,ARG,male,1985-01-20,1.72,,boxing,0,0,0 -212081217,Ignacio Prado,MEX,male,1993-09-21,1.78,65.0,cycling,0,0,0 -407328082,Ignas Navakauskas,LTU,male,1989-09-22,1.8,90.0,canoe,0,0,0 -317258255,Ignatas Konovalovas,LTU,male,1985-12-08,1.9,75.0,cycling,0,0,0 -746541982,Igor Genua,ESP,male,1988-06-05,1.77,78.0,rugby sevens,0,0,0 -877954587,Igor Glavan,UKR,male,1990-09-25,1.68,61.0,athletics,0,0,0 -207860786,Igor Karacic,CRO,male,1988-11-02,1.91,91.0,handball,0,0,0 -101294964,Igor Kobzar,RUS,male,1991-04-13,1.98,86.0,volleyball,0,0,0 -701378766,Igor Kovacevic,FRA,male,1988-11-03,1.9,85.0,aquatics,0,0,0 -647906398,Igor Marenic,CRO,male,1986-01-02,1.73,70.0,sailing,1,0,0 -292473740,Igor Mogne,MOZ,male,1996-08-01,1.75,66.0,aquatics,0,0,0 -882236724,Igor Olshanetskyi,ISR,male,1986-02-16,1.84,130.0,weightlifting,0,0,0 -295973567,Igor Pawel Jakubowski,POL,male,1992-08-06,1.84,,boxing,0,0,0 -343249944,Igor Polyanskiy,RUS,male,1990-01-16,1.91,72.0,triathlon,0,0,0 -42372934,Igor Radivilov,UKR,male,1992-10-19,1.67,67.0,gymnastics,0,0,0 -118939589,Igor Wandtke,GER,male,1990-11-03,1.76,76.0,judo,0,0,0 -510520768,Igor Zelenay,SVK,male,1982-10-02,1.98,88.0,tennis,0,0,0 -207987261,Ihar Pashevich,BLR,male,1991-12-08,1.95,101.0,rowing,0,0,0 -358053477,Ihor Bodrov,UKR,male,1987-07-09,1.84,77.0,athletics,0,0,0 -644497938,Ihor Olefirenko,UKR,male,1990-03-14,1.86,70.0,athletics,0,0,0 -804961388,Ihor Russ,UKR,male,1988-09-08,1.74,62.0,athletics,0,0,0 -606865842,Ihor Shymechko,UKR,male,1986-05-27,1.97,130.0,weightlifting,0,0,0 -976629954,Ika Rochmawati,INA,female,1989-07-02,1.65,52.0,archery,0,0,0 -552884668,Ike Diogu,NGR,male,1983-09-11,2.04,115.0,basketball,0,0,0 -3146070,Ikhtiyor Navruzov,UZB,male,1989-07-05,1.65,65.0,wrestling,0,0,1 -14214437,Ilana Kratysh,ISR,female,1990-07-06,1.69,69.0,wrestling,0,0,0 -259115271,Ilaria Bianchi,ITA,female,1990-01-06,1.7,65.0,aquatics,0,0,0 -976676407,Ilaria Bianco,ITA,female,1980-05-29,1.65,55.0,fencing,0,0,0 -91346833,Ildiko Toth,HUN,female,1987-04-23,1.75,72.0,aquatics,0,0,0 -853990592,Ilham Tanui Ozbilen,TUR,male,1990-03-05,1.77,60.0,athletics,0,0,0 -351709088,Ilia Druzhinin,RUS,male,1998-04-23,1.73,63.0,aquatics,0,0,0 -165852340,Ilia Shtokalov,RUS,male,1986-09-01,1.85,95.0,canoe,0,0,0 -50995857,Ilia Zakharov,RUS,male,1991-05-02,1.75,69.0,aquatics,0,0,0 -905071434,Ilias Iliadis,GRE,male,1986-11-10,1.79,90.0,judo,0,0,0 -448835262,Ilija Brozovic,CRO,male,1991-05-26,1.96,109.0,handball,0,0,0 -70065609,Ilke Ozyuksel,TUR,female,1997-02-26,1.67,53.0,modern pentathlon,0,0,0 -80054566,Illia Charheika,BLR,male,1993-04-15,1.79,81.0,shooting,0,0,0 -647841374,Illias Fifa,ESP,male,1989-05-16,1.73,68.0,athletics,0,0,0 -768750395,Illya Kvasha,UKR,male,1988-03-05,1.76,73.0,aquatics,0,0,0 -189313001,Illya Marchenko,UKR,male,1987-09-08,1.85,84.0,tennis,0,0,0 -169762981,Ilona Marhele,LAT,female,1986-04-05,1.64,50.0,athletics,0,0,0 -446700589,Ilse Paulis,NED,female,1993-07-30,1.74,57.0,rowing,1,0,0 -304002901,Ilya Golendov,KAZ,male,1994-10-02,1.85,87.0,canoe,0,0,0 -507272052,Ilya Khomenko,RUS,male,1995-10-14,1.81,75.0,aquatics,0,0,0 -755684703,Ilya Mokretcov,KAZ,male,1984-04-17,1.77,74.0,fencing,0,0,0 -971438297,Ilya Pervukhin,RUS,male,1991-07-06,1.83,82.0,canoe,0,0,0 -456650881,Ilya Tiapkin,KGZ,male,1991-08-02,1.6,65.0,athletics,0,0,0 -695779236,Ilyas Abbadi,ALG,male,1992-10-21,1.85,,boxing,0,0,0 -57892115,Imad Bassou,MAR,male,1993-07-04,1.73,66.0,judo,0,0,0 -587168078,Iman Essa Jasim,BRN,female,1997-07-09,,,athletics,0,0,0 -186497946,Imanol Erviti Ollo,ESP,male,1983-11-15,1.9,80.0,cycling,0,0,0 -303543728,Imene Ouneyssa Cherif Sahraoui,ALG,female,1995-09-14,1.65,64.0,sailing,0,0,0 -712856100,Imoh Ezekiel,NGR,male,1993-10-24,1.71,,football,0,0,1 -229646007,Imre Balazs Bacskai,HUN,male,1988-01-29,1.77,,boxing,0,0,0 -92960172,In Gee Chun,KOR,female,1994-08-10,1.76,70.0,golf,0,0,0 -158102208,Ina Nikulina,BLR,female,1995-02-23,1.72,66.0,rowing,0,0,0 -142973725,Inaki Aguilar Vicente,ESP,male,1983-09-09,1.89,82.0,aquatics,0,0,0 -952258564,Inaki Gomez,CAN,male,1988-01-16,1.72,59.0,athletics,0,0,0 -22566156,Inaki Villanueva,ESP,male,1991-02-10,1.98,103.0,rugby sevens,0,0,0 -349411929,Inbee Park,KOR,female,1988-07-12,1.68,60.0,golf,1,0,0 -471014946,Ines Boubakri,TUN,female,1988-12-28,1.67,56.0,fencing,0,0,1 -933685256,Ines Gmati,TUN,female,1997-04-05,1.65,70.0,sailing,0,0,0 -601743503,Ines Henriques,POR,female,1980-05-01,1.56,45.0,athletics,0,0,0 -26427576,Ines Melchor,PER,female,1986-08-30,1.52,45.0,athletics,0,0,0 -6720592,Ines Remersaro,URU,female,1992-12-02,1.67,55.0,aquatics,0,0,0 -71569433,Inessa Merkulova,RUS,female,1964-11-09,1.7,65.0,equestrian,0,0,0 -701169086,Inge Dekker,NED,female,1985-08-18,1.83,67.0,aquatics,0,0,0 -121871269,Inge Janssen,NED,female,1989-04-20,1.82,74.0,rowing,0,1,0 -771770373,Ingeborg Lovnes,NOR,female,1992-09-05,,,athletics,0,0,0 -569222306,Ingrid Klimke,GER,female,1968-04-01,1.72,58.0,equestrian,0,1,0 -778347841,Ingrid Oliveira,BRA,female,1996-05-07,1.6,58.0,aquatics,0,0,0 -449860583,Ingrid Puusta,EST,female,1990-11-08,1.63,57.0,sailing,0,0,0 -803332684,Ingrid Vidal,COL,female,1991-04-22,1.66,53.0,football,0,0,0 -464022421,Ingrit Lorena Valencia Victoria,COL,female,1988-09-03,1.6,,boxing,0,0,1 -454151024,Inigo Pena,ESP,male,1990-09-07,1.94,92.0,canoe,0,0,0 -22127464,Inika McPherson,USA,female,1986-09-29,,,athletics,0,0,0 -989454733,Injeong Choi,KOR,female,1990-05-21,1.74,59.0,fencing,0,0,0 -846310345,Inkululeko Suntele,LES,male,1994-04-30,,,boxing,0,0,0 -983898565,Inna Deriglazova,RUS,female,1990-03-10,1.73,61.0,fencing,1,0,0 -595647629,Inna Hryshchun,UKR,female,1994-09-29,1.72,72.0,canoe,0,0,0 -927315526,Inna Kashyna,UKR,female,1991-09-27,,,athletics,0,0,0 -204865458,Inna Klinova,KAZ,female,1986-05-13,1.73,78.0,canoe,0,0,0 -135745730,Inna Logutenkova,UKR,female,1986-10-19,1.72,63.0,equestrian,0,0,0 -980208758,Inna Osipenko-Rodomska,AZE,female,1982-09-20,1.66,63.0,canoe,0,0,1 -188617365,Inna Stepanova,RUS,female,1990-04-17,1.76,63.0,archery,0,1,0 -167916854,Inna Trazhukova,RUS,female,1990-09-11,1.7,63.0,wrestling,0,0,0 -163668674,Ioana Strungaru,ROU,female,1989-01-04,1.8,77.0,rowing,0,0,1 -562041099,Ioanna Anagnostopoulou,GRE,female,1997-06-12,1.81,58.0,gymnastics,0,0,0 -850374991,Ioannis Christou,GRE,male,1983-06-23,1.9,92.0,rowing,0,0,0 -617416112,Ioannis Fountoulis,GRE,male,1988-05-25,1.86,90.0,aquatics,0,0,0 -580426427,Ioannis Mitakis,GRE,male,1989-11-08,1.86,96.0,sailing,0,0,0 -115317084,Ioannis Petrou,GRE,male,1996-08-10,1.86,73.0,rowing,0,0,0 -557528943,Ioannis Tamouridis,GRE,male,1980-06-03,1.8,73.0,cycling,0,0,0 -576485074,Ioannis Tsilis,GRE,male,1986-07-15,1.83,86.0,rowing,0,0,0 -33869783,Ion Iulian Panait,ROU,male,1981-05-05,1.69,70.0,wrestling,0,0,0 -914048344,Ionela-Livia Lehaci,ROU,female,1995-01-03,1.78,57.0,rowing,0,0,0 -936581108,Ionica Munteanu,ROU,female,1979-01-07,1.75,68.0,handball,0,0,0 -113789263,Iosefo Verevou,FIJ,male,1996-01-05,1.9,96.0,football,0,0,0 -855720243,Ippei Watanabe,JPN,male,1997-03-18,1.93,76.0,aquatics,0,0,0 -442968220,Irakli Revishvili,GEO,male,1989-11-03,1.78,77.0,aquatics,0,0,0 -938066786,Irakli Turmanidze,GEO,male,1984-12-13,1.82,136.0,weightlifting,0,0,1 -513788347,Irem Karamete,TUR,female,1993-06-20,1.67,60.0,fencing,0,0,0 -508732843,Irene Otieno,KEN,female,1986-03-26,1.62,58.0,rugby sevens,0,0,0 -84886032,Irene Prescott,TGA,female,1994-06-21,1.74,74.0,aquatics,0,0,0 -182591826,Irene Vecchi,ITA,female,1989-06-10,1.7,56.0,fencing,0,0,0 -417651899,Irina Bliznova,RUS,female,1986-10-06,1.82,68.0,handball,1,0,0 -283851540,Irina Dolgova,RUS,female,1995-09-26,1.53,48.0,judo,0,0,0 -296869693,Irina Ektova,KAZ,female,1987-01-08,1.73,61.0,athletics,0,0,0 -804181328,Irina Embrich,EST,female,1980-07-12,1.7,54.0,fencing,0,0,0 -739855069,Irina Fetisova,RUS,female,1994-09-07,1.9,76.0,volleyball,0,0,0 -557552346,Irina Kalentyeva,RUS,female,1977-11-10,1.55,45.0,cycling,0,0,0 -917681217,Irina Podoinikova,KAZ,female,1988-06-28,1.66,65.0,canoe,0,0,0 -219669436,Irina Sazonova,ISL,female,1991-09-02,1.6,55.0,gymnastics,0,0,0 -696538108,Irina Smolnikova,KAZ,female,1980-07-21,1.63,55.0,athletics,0,0,0 -83837014,Irina Voronkova,RUS,female,1995-10-20,1.9,84.0,volleyball,0,0,0 -431895427,Irina Zabludina,RUS,female,1987-02-24,1.6,57.0,judo,0,0,0 -101444757,Irina Zaryazhko,RUS,female,1991-10-04,1.96,78.0,volleyball,0,0,0 -172876009,Irina-Camelia Begu,ROU,female,1990-08-26,1.81,70.0,tennis,0,0,0 -201635466,Irini Vasiliou,GRE,female,1990-03-18,1.69,58.0,athletics,0,0,0 -795911754,Iris Sing,BRA,female,1990-08-21,1.67,51.0,taekwondo,0,0,0 -642164387,Iris Wang,USA,female,1994-09-02,1.61,52.0,badminton,0,0,0 -587220722,Irma Testa,ITA,female,1997-12-28,1.74,,boxing,0,0,0 -784228861,Irmina Mrozek-Gliszczynska,POL,female,1992-02-09,1.76,70.0,sailing,0,0,0 -858423285,Irvette van Zyl,RSA,female,1987-07-05,1.69,53.0,athletics,0,0,0 -229536037,Irving Perez,MEX,male,1986-05-16,1.75,64.0,triathlon,0,0,0 -544870247,Iryna Dekha,UKR,female,1996-05-14,1.74,75.0,weightlifting,0,0,0 -448533729,Iryna Gerashchenko,UKR,female,1995-03-10,1.81,61.0,athletics,0,0,0 -851400427,Iryna Khokhlova,ARG,female,1990-01-29,1.67,60.0,modern pentathlon,0,0,0 -712655795,Iryna Klymets,UKR,female,1994-10-04,1.68,78.0,athletics,0,0,0 -123129605,Iryna Limanouskaya,BLR,female,1994-05-18,1.66,54.0,aquatics,0,0,0 -586982382,Iryna Novozhylova,UKR,female,1986-01-07,1.75,90.0,athletics,0,0,0 -413999687,Iryna Popova,UKR,female,1991-05-27,1.64,53.0,cycling,0,0,0 -312836609,Iryna Vaskouskaya,BLR,female,1991-04-02,1.79,65.0,athletics,0,0,0 -25232668,Iryna Yakaltsevich,BLR,female,1993-01-26,1.66,60.0,athletics,0,0,0 -862541606,Isaac Grainger,NZL,male,1992-05-26,1.96,95.0,rowing,0,0,0 -686113562,Isaac Korir,BRN,male,1990-08-26,1.88,59.0,athletics,0,0,0 -427843232,Isaac Makwala,BOT,male,1985-09-24,1.85,78.0,athletics,0,0,0 -231426919,Isaac Silafau,ASA,male,1990-10-05,1.75,68.0,athletics,0,0,0 -245903701,Isabel Brand,GUA,female,1996-06-23,1.69,53.0,modern pentathlon,0,0,0 -840841199,Isabel Cristina Romero Benitez,COL,female,1996-07-27,1.61,62.0,rugby sevens,0,0,0 -196950194,Isabel Evelize W. Guialo,ANG,female,1990-04-08,1.8,73.0,handball,0,0,0 -338940609,Isabel Kerschowski,GER,female,1988-01-22,1.67,57.0,football,1,0,0 -519820661,Isabel Swan,BRA,female,1983-11-18,1.81,69.0,sailing,0,0,0 -809661932,Isabela Macedo,BRA,female,1994-01-23,1.79,76.0,basketball,0,0,0 -210703897,Isabela Onyshko,CAN,female,1998-06-23,1.57,50.0,gymnastics,0,0,0 -630931206,Isabell Werth,GER,female,1969-07-21,1.69,66.0,equestrian,1,1,0 -526667108,Isabella Amado,PAN,female,1996-08-09,1.55,59.0,gymnastics,0,0,0 -610732042,Isabella Arcila Hurtado,COL,female,1994-03-11,1.68,64.0,aquatics,0,0,0 -435115822,Isabella Echeverri,COL,female,1994-06-16,1.72,66.0,football,0,0,0 -465840905,Isabella Isaksen,USA,female,1993-11-22,1.73,63.0,modern pentathlon,0,0,0 -978854513,Isabelle Forrer,SUI,female,1982-03-28,1.78,67.0,volleyball,0,0,0 -844071383,Isabelle Gullden,SWE,female,1989-06-29,1.77,76.0,handball,0,0,0 -729625063,Isabelle Haerle,GER,female,1988-01-10,1.75,64.0,aquatics,0,0,0 -846218313,Isabelle Pedersen,NOR,female,1992-01-27,,,athletics,0,0,0 -441669814,Isabelle Sambou,SEN,female,1980-10-20,1.55,53.0,wrestling,0,0,0 -61576654,Isabelle Yacoubou,FRA,female,1986-04-21,1.9,104.0,basketball,0,0,0 -684603129,Isadora Cerullo,BRA,female,1991-03-24,1.58,58.0,rugby sevens,0,0,0 -99018195,Isak Ohrstrom,SWE,male,1990-11-26,1.77,72.0,canoe,0,0,0 -624395952,Isamu Fujisawa,JPN,male,1987-10-12,1.65,54.0,athletics,0,0,0 -121190622,Isaquias Queiroz dos Santos,BRA,male,1994-01-03,1.75,85.0,canoe,0,2,1 -141207459,Isheau Wong,TPE,female,1989-02-12,1.79,70.0,equestrian,0,0,0 -887945521,Isiah Kiplangat Koech,KEN,male,1993-12-19,1.72,65.0,athletics,0,0,0 -181441933,Isidora Jimenez,CHI,female,1993-08-10,1.7,56.0,athletics,0,0,0 -874295706,Isidoro Ibarra,ARG,male,1992-10-02,1.75,75.0,hockey,1,0,0 -211473631,Isil Alben,TUR,female,1986-02-22,1.72,62.0,basketball,0,0,0 -218012478,Isis Gimenez,VEN,female,1990-07-30,1.7,68.0,fencing,0,0,0 -444343903,Islam El Shehaby,EGY,male,1982-08-01,1.95,105.0,judo,0,0,0 -642911122,Islam Magomedov,RUS,male,1991-02-08,1.82,98.0,wrestling,0,0,0 -318856332,Islam-Beka Albiev,RUS,male,1988-12-28,1.65,66.0,wrestling,0,0,0 -426130402,Ismael Borrero Molina,CUB,male,1992-01-06,1.6,59.0,wrestling,1,0,0 -17997174,Ismael Coulibaly,MLI,male,1992-11-20,1.91,78.0,taekwondo,0,0,0 -168084351,Ismael Marcelo Hernandez Uscanga,MEX,male,1990-01-23,1.78,64.0,modern pentathlon,0,0,1 -938396110,Ismail Kamara,SLE,male,1997-02-14,,,athletics,0,0,0 -183143122,Ismail Keles,TUR,male,1988-03-05,1.68,70.0,shooting,0,0,0 -843284700,Isobel Bishop,AUS,female,1991-09-08,1.8,72.0,aquatics,0,0,0 -997954972,Issam Tej,TUN,male,1979-07-29,1.87,110.0,handball,0,0,0 -133506247,Istvan Peni,HUN,male,1997-02-14,1.76,64.0,shooting,0,0,0 -347433647,Istvan Vereb,HUN,male,1987-10-08,1.81,90.0,wrestling,0,0,0 -521044712,Iszlam Monier Suliman,SUD,male,1990-12-17,1.82,90.0,judo,0,0,0 -177843995,Italo Duarte,BRA,male,1992-03-13,1.8,73.0,aquatics,0,0,0 -413617317,Itumeleng Khune,RSA,male,1987-06-20,1.84,75.0,football,0,0,0 -85278686,Itzel Adilene Manjarrez Bastidas,MEX,female,1990-04-10,1.71,49.0,taekwondo,0,0,0 -490237088,Iuliana Popa,ROU,female,1996-07-05,1.85,72.0,rowing,0,0,1 -886931188,Iuliia Andreeva,KGZ,female,1984-03-07,1.68,51.0,athletics,0,0,0 -734306362,Iuliia Olishevska,UKR,female,1989-02-02,1.67,60.0,athletics,0,0,0 -36280393,Iuliia Paratova,UKR,female,1986-11-07,1.55,48.0,weightlifting,0,0,0 -143130668,Iuliia Prokopchuk,UKR,female,1986-10-23,1.6,50.0,aquatics,0,0,0 -967646151,Iurii Cheban,UKR,male,1986-07-05,1.85,93.0,canoe,1,0,0 -689497328,Iurii Krakovetskii,KGZ,male,1992-08-27,1.8,130.0,judo,0,0,0 -898570375,Ivan Banzeruk,UKR,male,1990-02-09,1.8,70.0,athletics,0,0,0 -69692265,Ivan Cupic,CRO,male,1986-03-27,1.78,78.0,handball,0,0,0 -360210187,Ivan Dovhodko,UKR,male,1989-01-15,1.96,100.0,rowing,0,0,0 -666382432,Ivan Dychko,KAZ,male,1990-08-11,2.06,,boxing,0,0,1 -470445982,Ivan Efremov,UZB,male,1986-03-09,1.84,105.0,weightlifting,0,0,0 -784382414,Ivan Emilianov,MDA,male,1977-02-19,2.02,165.0,athletics,0,0,0 -77481096,Ivan Endericao,ECU,male,1991-10-28,1.78,80.0,aquatics,0,0,0 -725755440,Ivan Garcia,MEX,male,1993-10-25,1.6,55.0,aquatics,0,0,0 -435489428,Ivan Guidea,ROU,male,1988-05-12,1.63,60.0,wrestling,0,0,0 -98196983,Ivan Horvat,CRO,male,1993-08-17,1.83,73.0,athletics,0,0,0 -21185636,Ivan Ivanov,UKR,male,1989-01-08,1.83,67.0,triathlon,0,0,0 -731072777,Ivan Ivanov,KAZ,male,1992-01-03,2.02,144.0,athletics,0,0,0 -515991313,Ivan Kljakovic Gaspic,CRO,male,1984-05-24,1.89,95.0,sailing,0,0,0 -469953081,Ivan Krapic,CRO,male,1989-02-14,1.94,103.0,aquatics,0,1,0 -217049507,Ivan Pastor Lafuente,ESP,male,1980-02-18,1.77,73.0,sailing,0,0,0 -983627857,Ivan Pesic,CRO,male,1989-03-17,1.94,112.0,handball,0,0,0 -172346281,Ivan Popov,AUS,male,1986-05-25,2.03,106.0,wrestling,0,0,0 -363273561,Ivan Remarenco,UAE,male,1988-08-07,1.86,100.0,judo,0,0,0 -195192329,Ivan Sliskovic,CRO,male,1991-10-23,1.97,100.0,handball,0,0,0 -744721123,Ivan Sozonov,RUS,male,1989-07-06,1.8,78.0,badminton,0,0,0 -432949673,Ivan Stevanovic,CRO,male,1982-05-18,1.93,105.0,handball,0,0,0 -150785655,Ivan Stevic,SRB,male,1980-03-12,1.72,68.0,cycling,0,0,0 -858324078,Ivan Stretovich,RUS,male,1996-10-06,1.7,59.0,gymnastics,0,1,0 -337752893,Ivan Trotski,BLR,male,1976-05-27,1.72,62.0,athletics,0,0,0 -617539408,Ivan Tsikhan,BLR,male,1976-07-24,1.86,110.0,athletics,0,1,0 -33123082,Ivan Zaytsev,ITA,male,1988-10-02,2.04,100.0,volleyball,0,1,0 -177114501,Ivan Zaytsev,UZB,male,1988-11-07,1.92,95.0,athletics,0,0,0 -296305491,Ivana Andusic Maksimovic,SRB,female,1990-05-02,1.61,51.0,shooting,0,0,0 -313918380,Ivana Spanovic,SRB,female,1990-05-10,1.76,65.0,athletics,0,0,1 -666265585,Ivaylo Ivanov,BUL,male,1994-07-20,1.8,81.0,judo,0,0,0 -144103259,Ives Alonso,BRA,male,1980-10-12,1.91,102.0,aquatics,0,0,0 -621283199,Ivet Lalova-Collio,BUL,female,1984-05-18,1.68,56.0,athletics,0,0,0 -440079091,Iveta Putalova,SVK,female,1988-03-24,1.73,60.0,athletics,0,0,0 -45693208,Iveta Vacenovska,CZE,female,1986-03-22,1.68,58.0,table tennis,0,0,0 -237977103,Ivo Rodrigues,POR,male,1995-03-30,1.8,70.0,football,0,0,0 -447718633,Ivona Dadic,AUT,female,1993-12-29,1.79,65.0,athletics,0,0,0 -958847456,Iwona Lewandowska,POL,female,1985-02-19,1.61,43.0,athletics,0,0,0 -909695929,Iwona Nina Matkowska,POL,female,1982-05-28,1.6,48.0,wrestling,0,0,0 -951915327,Izabella Chiappini,BRA,female,1995-09-28,1.7,59.0,aquatics,0,0,0 -645206665,Iziane Castro,BRA,female,1982-03-13,1.81,69.0,basketball,0,0,0 -324317073,Izmir Smajlaj,ALB,male,1993-03-29,1.95,86.0,athletics,0,0,0 -269077637,Izzat Artykov,KGZ,male,1993-09-08,1.6,69.0,weightlifting,0,0,0 -963470653,Izzet Safer,TUR,male,1990-07-10,1.78,74.0,athletics,0,0,0 -860885364,Izzy Joachim,VIN,female,2000-05-11,,,aquatics,0,0,0 -478575859,J'den Michael Tbory Cox,USA,male,1995-03-03,1.81,85.0,wrestling,0,0,1 -411708041,Jaak-Heinrich Jagor,EST,male,1990-05-11,1.9,80.0,athletics,0,0,0 -108953366,Jabrayil Hasanov,AZE,male,1990-02-24,1.7,74.0,wrestling,0,0,1 -299391231,Jacco Arends,NED,male,1991-01-28,1.86,72.0,badminton,0,0,0 -788110686,Jack Beaumont,GBR,male,1993-11-21,1.88,88.0,rowing,0,0,0 -579353978,Jack Bobridge,AUS,male,1989-07-13,1.8,65.0,cycling,0,1,0 -760496010,Jack Burnell,GBR,male,1993-06-13,1.85,72.0,aquatics,0,0,0 -762346507,Jack Conger,USA,male,1994-09-26,1.94,79.0,aquatics,1,0,0 -603033705,Jack Green,GBR,male,1991-10-06,1.93,78.0,athletics,0,0,0 -123646851,Jack Laugher,GBR,male,1995-01-30,1.68,72.0,aquatics,1,1,0 -438635106,Jack McLoughlin,AUS,male,1995-02-01,1.83,75.0,aquatics,0,0,0 -485335601,Jack Rossiter,AUS,male,1997-06-13,1.71,70.0,shooting,0,0,0 -466400637,Jack Sock,USA,male,1992-09-24,1.91,83.0,tennis,1,0,1 -88575304,Jackeline Renteria Castillo,COL,female,1986-02-23,1.67,58.0,wrestling,0,0,0 -818815252,Jackie Baumann,GER,female,1995-08-24,1.73,58.0,athletics,0,0,0 -570931875,Jackie Galloway,USA,female,1995-12-27,1.78,79.0,taekwondo,0,0,1 -305555615,Jacko Gill,NZL,male,1994-12-20,1.89,120.0,athletics,0,0,0 -637078881,Jackson Kiprop,UGA,male,1986-10-20,,,athletics,0,0,0 -547359586,Jackson Rondinelli,BRA,male,1994-05-20,1.63,62.0,aquatics,0,0,0 -181043384,Jaclyn Briggs,USA,female,1988-05-23,1.68,61.0,hockey,0,0,0 -390353746,Jaco van Zyl,RSA,male,1979-02-23,1.84,74.0,golf,0,0,0 -144248521,Jacob Araptany,UGA,male,1994-02-11,,,athletics,0,0,0 -28259305,Jacob Barsoe,DEN,male,1988-09-21,1.88,73.0,rowing,0,1,0 -135293995,Jacob Bruun Larsen,DEN,male,1998-09-19,1.83,74.0,football,0,0,0 -521943829,Jacob Clear,AUS,male,1985-01-18,1.85,86.0,canoe,0,0,0 -775250732,Jacob Dalton,USA,male,1991-08-19,1.52,65.0,gymnastics,0,0,0 -260432807,Jacob Gibb,USA,male,1976-02-06,2.01,95.0,volleyball,0,0,0 -682128898,Jacob Hansford,AUS,male,1995-09-28,1.82,77.0,aquatics,0,0,0 -6706851,Jacob Heidtmann,GER,male,1994-11-06,1.95,83.0,aquatics,0,0,0 -194290880,Jacob Kiplimo,UGA,male,2000-11-14,,,athletics,0,0,0 -561768430,Jacob Larsen,DEN,male,1988-06-13,1.82,73.0,rowing,0,1,0 -199313141,Jacob Maliekal,RSA,male,1991-01-01,1.72,72.0,badminton,0,0,0 -888054041,Jacob Pebley,USA,male,1993-09-17,1.94,86.0,aquatics,0,0,0 -807399236,Jacob Rozani,RSA,male,1988-01-24,,,athletics,0,0,0 -934183083,Jacob Saunders,CAN,male,1992-04-15,1.76,64.0,sailing,0,0,0 -24055098,Jacob Stockmann,NED,male,1984-07-24,1.93,80.0,hockey,0,0,0 -176550067,Jacob Une Larsson,SWE,male,1994-04-08,1.78,70.0,football,0,0,0 -65437843,Jacob Whetton,AUS,male,1991-06-15,1.72,64.0,hockey,0,0,0 -412344813,Jacqueline Simoneau,CAN,female,1996-09-29,1.65,51.0,aquatics,0,0,0 -761781098,Jade Barbosa,BRA,female,1991-07-01,1.51,45.0,gymnastics,0,0,0 -401122423,Jade Howard,ZAM,female,1995-04-03,1.82,68.0,aquatics,0,0,0 -789368570,Jade Jones,GBR,female,1993-03-21,1.67,57.0,taekwondo,1,0,0 -899823544,Jade Lally,GBR,female,1987-03-30,1.82,88.0,athletics,0,0,0 -506534023,Jade Uru,NZL,male,1987-10-20,1.89,88.0,rowing,0,0,0 -142475326,Jade le Pesq,FRA,female,1992-10-12,1.63,63.0,rugby sevens,0,0,0 -609790430,Jaeyeong Lee,KOR,female,1996-10-15,1.78,66.0,volleyball,0,0,0 -732777985,Jagdish Gill,CAN,male,1984-12-05,1.75,77.0,hockey,0,0,0 -452242951,Jaheel Hyde,JAM,male,1997-02-02,1.8,74.0,athletics,0,0,0 -355490411,Jahir Ocampo,MEX,male,1990-01-12,1.78,80.0,aquatics,0,0,0 -519260746,Jahvid Best,LCA,male,1989-01-30,,,athletics,0,0,0 -300681433,Jailma de Lima,BRA,female,1986-12-31,1.74,65.0,athletics,0,0,0 -863481965,Jaime Nielsen,NZL,female,1985-09-03,1.77,70.0,cycling,0,0,0 -859145173,Jaime Quiyuch,GUA,male,1988-04-24,1.78,63.0,athletics,0,0,0 -200267269,Jaime Ryan,AUS,female,1994-05-08,1.75,68.0,sailing,0,0,0 -524949052,Jaime Yusept Espinal,PUR,male,1984-10-14,1.78,86.0,wrestling,0,0,0 -467840408,Jaimee Lovett,NZL,female,1988-05-05,1.67,83.0,canoe,0,0,0 -93687880,Jaisha Orchatteri,IND,female,1983-05-23,1.54,43.0,athletics,0,0,0 -47754234,Jak Ali Harvey,TUR,male,1989-05-04,1.83,74.0,athletics,0,0,0 -280957264,Jake Bensted,AUS,male,1994-03-04,1.73,73.0,judo,0,0,0 -507019886,Jake Kaminski,USA,male,1988-08-11,1.78,70.0,archery,0,1,0 -191443309,Jake Lilley,AUS,male,1993-07-20,2.04,98.0,sailing,0,0,0 -742672786,Jake Milton Green,RSA,male,1994-03-30,1.92,90.0,rowing,0,0,0 -919219233,Jake Packard,AUS,male,1994-06-20,1.95,68.0,aquatics,0,0,1 -833814609,Jakob Fuglsang,DEN,male,1985-03-22,1.83,69.0,cycling,0,1,0 -248905348,Jakob Makarashvili,GEO,male,1985-12-28,1.74,80.0,wrestling,0,0,0 -391045084,Jakov Gojun,CRO,male,1986-04-18,2.03,112.0,handball,0,0,0 -654667926,Jakson Vicent Monasterio,VEN,male,1991-12-31,,,rowing,0,0,0 -673012015,Jakub Dyjas,POL,male,1995-10-09,1.83,54.0,table tennis,0,0,0 -215360776,Jakub Grigar,SVK,male,1997-04-27,1.83,81.0,canoe,0,0,0 -63505242,Jakub Holusa,CZE,male,1988-02-20,1.83,72.0,athletics,0,0,0 -711962459,Jakub Jelonek,POL,male,1985-07-07,1.85,67.0,athletics,0,0,0 -566482718,Jakub Krzewina,POL,male,1989-10-10,1.82,79.0,athletics,0,0,0 -384653943,Jakub Podrazil,CZE,male,1992-01-09,1.99,100.0,rowing,0,0,0 -141908387,Jakub Vadlejch,CZE,male,1990-10-10,1.91,93.0,athletics,0,0,0 -719778285,Jale Dreloa,FIJ,male,1995-04-21,1.7,88.0,football,0,0,0 -118360942,Jamal Wilson,BAH,male,1988-09-01,,,athletics,0,0,0 -741666206,Jamaladdin Magomedov,AZE,male,1989-03-14,1.86,125.0,wrestling,0,0,0 -190769127,James Adede,KEN,male,1986-10-31,1.62,93.0,weightlifting,0,0,0 -476158406,James Connor,AUS,male,1995-05-05,1.83,70.0,aquatics,0,0,0 -384973651,James Cooke,GBR,male,1991-03-03,1.85,74.0,modern pentathlon,0,0,0 -805860316,James Coughlan,NZL,male,1990-08-28,1.83,78.0,hockey,0,0,0 -595946076,James Dasaolu,GBR,male,1987-09-05,1.87,88.0,athletics,0,0,0 -425329772,James Davies,GBR,male,1990-10-25,1.81,98.0,rugby sevens,0,1,0 -871780908,James Ellington,GBR,male,1985-09-06,1.79,81.0,athletics,0,0,0 -927707277,James Feigen,USA,male,1989-09-26,1.96,97.0,aquatics,1,0,0 -924475457,James Guy,GBR,male,1995-11-26,1.88,84.0,aquatics,0,2,0 -884248663,James Hunter,NZL,male,1992-08-24,1.85,70.0,rowing,0,0,0 -835838469,James Lassche,NZL,male,1989-08-31,1.91,71.0,rowing,0,0,0 -311569529,James Magnussen,AUS,male,1991-04-11,1.97,95.0,aquatics,0,0,1 -966013251,James McRAE,AUS,male,1987-06-27,1.93,93.0,rowing,0,1,0 -487094967,James Nyang Chiengjiek,ROT,male,1992-03-02,1.79,59.0,athletics,0,0,0 -451338958,James Paterson-Robinson,AUS,male,1978-09-29,1.7,72.0,equestrian,0,0,0 -964836144,James Reid,RSA,male,1992-08-01,1.8,70.0,cycling,0,0,0 -252455377,James Rendon,COL,male,1985-04-07,1.7,64.0,athletics,0,0,0 -950874222,James Roberts,AUS,male,1991-04-11,1.94,90.0,aquatics,0,0,1 -942584385,James Rodwell,GBR,male,1984-08-23,1.95,105.0,rugby sevens,0,1,0 -690378941,James Stannard,AUS,male,1983-02-21,1.73,82.0,rugby sevens,0,0,0 -538679620,James Stanton-French,AUS,male,1983-07-21,2.0,93.0,aquatics,0,0,0 -844650938,James Thompson,RSA,male,1986-11-18,1.82,70.0,rowing,0,0,0 -291252183,James Willett,AUS,male,1995-12-23,1.86,88.0,shooting,0,0,0 -139948765,James-Andrew Davis,GBR,male,1991-07-03,1.95,98.0,fencing,0,0,0 -745045178,Jamial Rolle,BAH,male,1980-08-16,1.74,70.0,athletics,0,0,0 -219901271,Jamie Broder,CAN,female,1985-06-08,1.72,64.0,volleyball,0,0,0 -690805480,Jamie Dwyer,AUS,male,1979-03-12,1.72,68.0,hockey,0,0,0 -116149715,Jamie Murray,GBR,male,1986-02-13,1.9,84.0,tennis,0,0,0 -742530788,Jamie Subandhi,USA,female,1989-12-15,1.63,58.0,badminton,0,0,0 -531741106,Jamila Lunkuse,UGA,female,1997-06-01,,,aquatics,0,0,0 -892658314,Jamila Sanmoogan,GUY,female,1997-03-20,1.43,58.0,aquatics,0,0,0 -153070028,Jamile Samuel,NED,female,1992-04-24,1.68,59.0,athletics,0,0,0 -48596156,Jamina Roberts,SWE,female,1990-05-28,1.76,67.0,handball,0,0,0 -621534555,Jan Barta,CZE,male,1984-12-07,1.84,75.0,cycling,0,0,0 -551500091,Jan Benzien,GER,male,1982-07-22,1.8,75.0,canoe,0,0,0 -44393712,Jan Kudlicka,CZE,male,1988-04-29,1.84,82.0,athletics,0,0,0 -143054304,Jan Kuf,CZE,male,1991-05-11,1.86,78.0,modern pentathlon,0,0,0 -192330093,Jan Lochbihler,SUI,male,1992-03-03,1.87,92.0,shooting,0,0,0 -958304454,Jan Micka,CZE,male,1995-01-15,1.83,76.0,aquatics,0,0,0 -809864436,Jan O Jorgensen,DEN,male,1987-12-31,1.84,76.0,badminton,0,0,0 -33776196,Jan Polanc,SLO,male,1992-05-06,1.72,60.0,cycling,0,0,0 -860104103,Jan Skarnitzl,CZE,male,1986-07-11,1.79,68.0,cycling,0,0,0 -973675297,Jan Sterba,CZE,male,1981-06-01,1.83,85.0,canoe,0,0,1 -798223501,Jan Switkowski,POL,male,1994-01-23,1.93,82.0,aquatics,0,0,0 -810723660,Jan Vandrey,GER,male,1991-12-11,1.88,88.0,canoe,1,0,0 -283291174,Jan Vetesnik,CZE,male,1984-03-05,1.82,70.0,rowing,0,0,0 -283817717,Jan-Lennard Struff,GER,male,1990-04-25,1.96,91.0,tennis,0,0,0 -591169958,Jan-Philip Glania,GER,male,1988-11-08,1.94,90.0,aquatics,0,0,0 -726313430,Jan-Willem van Schip,NED,male,1994-08-20,1.94,83.0,cycling,0,0,0 -918569259,Jana Beckmann,GER,female,1983-05-02,1.7,60.0,shooting,0,0,0 -87919102,Jana Berezko-Marggrander,GER,female,1995-10-17,1.69,55.0,gymnastics,0,0,0 -378489397,Jana Dukatova,SVK,female,1983-06-13,1.8,64.0,canoe,0,0,0 -625488850,Jana Labathova,SVK,female,1988-09-27,1.62,50.0,aquatics,0,0,0 -117098951,Jana Pechanova,CZE,female,1981-03-03,1.7,62.0,aquatics,0,0,0 -839957321,Jana Teschke,GER,female,1990-09-22,1.68,56.0,hockey,0,0,1 -957642024,Jana Veldakova,SVK,female,1981-06-03,1.78,60.0,athletics,0,0,0 -947261546,Janay Deloach,USA,female,1985-10-12,1.66,58.0,athletics,0,0,0 -481441857,Jandi Kim,KOR,female,1991-06-15,1.63,57.0,judo,0,0,0 -40347261,Jane Claxton,AUS,female,1992-10-26,1.69,60.0,hockey,0,0,0 -748137388,Jane Vongvorachoti,THA,female,1984-01-07,1.66,53.0,athletics,0,0,0 -896595402,Janeil Bellille,TTO,female,1989-06-18,1.63,59.0,athletics,0,0,0 -71008427,Janet Amponsah,GHA,female,1993-04-12,1.71,,athletics,0,0,0 -910286492,Janet Okelo,KEN,female,1992-05-05,1.72,60.0,rugby sevens,0,0,0 -900614005,Janet Owino,KEN,female,1985-08-08,1.65,65.0,rugby sevens,0,0,0 -229768260,Janete Viegas dos Santos,ANG,female,1991-06-10,1.75,68.0,handball,0,0,0 -817731370,Jangmi Kim,KOR,female,1992-09-25,1.6,53.0,shooting,0,0,0 -629610747,Janice Teixeira,BRA,female,1962-05-20,1.7,73.0,shooting,0,0,0 -633346217,Janieve Russell,JAM,female,1993-11-14,1.75,64.0,athletics,0,0,0 -192489040,Janika Sprunger,SUI,female,1987-05-29,1.68,54.0,equestrian,0,0,0 -497079797,Janine Beckie,CAN,female,1994-08-20,1.73,63.0,football,0,0,1 -20326038,Janine van Wyk,RSA,female,1987-04-17,1.63,61.0,football,0,0,0 -882695911,Janis Smedins,LAT,male,1987-07-31,1.91,89.0,volleyball,0,0,0 -523195798,Janja Segel,SLO,female,2001-06-17,1.77,60.0,aquatics,0,0,0 -974901356,Jannah Sonnenschein,MOZ,female,1996-04-24,1.68,60.0,aquatics,0,0,0 -141611680,Janne Muller-Wieland,GER,female,1986-10-28,1.75,69.0,hockey,0,0,1 -513737062,Jannick Green Krejberg,DEN,male,1988-09-29,1.95,95.0,handball,1,0,0 -18696294,Jannik Huth,GER,male,1994-04-15,1.85,80.0,football,0,1,0 -900686372,Jantine van der Vlist,NED,female,1985-10-30,1.82,74.0,volleyball,0,0,0 -742091391,Jaouad Achab,BEL,male,1992-08-20,1.75,64.0,taekwondo,0,0,0 -930205655,Jaqueline Antonia Ferreira,BRA,female,1987-03-05,1.65,75.0,weightlifting,0,0,0 -802446612,Jaqueline Endres,BRA,female,1983-12-31,1.86,71.0,volleyball,0,0,0 -565862528,Jared Jarvis,ANT,male,1994-08-29,,,athletics,0,0,0 -541393183,Jared Tallent,AUS,male,1984-10-17,1.78,58.0,athletics,0,1,0 -586477204,Jared Ward,USA,male,1988-09-09,1.78,65.0,athletics,0,0,0 -258997468,Jarimit Leonor Weffer Guanipa,VEN,female,1985-11-03,1.58,75.0,wrestling,0,0,0 -35220711,Jarkko Kinnunen,FIN,male,1984-01-19,1.88,69.0,athletics,0,0,0 -148216352,Jarlan Junior Barrera Escalona,COL,male,1995-09-16,1.71,68.0,football,0,0,0 -797374623,Jarlinson Pantano Gomez,COL,male,1988-11-19,,,cycling,0,0,0 -100552265,Jaromir Jezek,CZE,male,1986-11-13,1.8,78.0,judo,0,0,0 -598101856,Jaroslav Baba,CZE,male,1984-09-02,1.99,86.0,athletics,0,0,0 -167236182,Jaroslav Kulhavy,CZE,male,1985-01-08,1.88,77.0,cycling,0,1,0 -727556594,Jaroslav Radon,CZE,male,1986-09-03,1.85,85.0,canoe,0,0,0 -226767205,Jarred Crous,RSA,male,1996-06-27,1.87,84.0,aquatics,0,0,0 -573245976,Jarrin Solomon,TTO,male,1986-01-11,1.65,73.0,athletics,0,0,0 -357249315,Jarrion Lawson,USA,male,1994-05-06,1.88,78.0,athletics,0,0,0 -649163218,Jarrod Gilchrist,AUS,male,1990-06-13,1.89,90.0,aquatics,0,0,0 -935549019,Jarrod Poort,AUS,male,1994-10-31,1.85,85.0,aquatics,0,0,0 -977628785,Jarryd Dunn,GBR,male,1992-01-30,1.84,74.0,athletics,0,0,0 -891780988,Jaruwat Saensuk,THA,male,1996-05-21,1.85,76.0,rowing,0,0,0 -164713165,Jasa Veremalua,FIJ,male,1988-05-29,1.95,98.0,rugby sevens,1,0,0 -489559768,Jasmin Glaesser,CAN,female,1992-07-08,1.67,58.0,cycling,0,0,1 -63237381,Jasmin Kuelbs,GER,female,1991-11-07,1.78,127.0,judo,0,0,0 -899306265,Jasmina Jankovic,NED,female,1986-12-06,1.7,77.0,handball,0,0,0 -742296215,Jasmine Alkhaldi,PHI,female,1993-06-20,1.8,60.0,aquatics,0,0,0 -245188087,Jasmine Camacho-Quinn,PUR,female,1996-09-21,1.8,73.0,athletics,0,0,0 -851518120,Jasmine Joyce,GBR,female,1995-10-09,1.63,55.0,rugby sevens,0,0,0 -481452678,Jasmine Mian,CAN,female,1989-12-31,1.55,48.0,wrestling,0,0,0 -690689063,Jasmine Pereira,NZL,female,1996-07-20,1.68,66.0,football,0,0,0 -963694537,Jason Block,CAN,male,1989-12-28,1.82,82.0,aquatics,0,0,0 -883244567,Jason Burnett,CAN,male,1986-12-16,1.65,60.0,gymnastics,0,0,0 -42049937,Jason Eric Whateley,AUS,male,1990-11-18,1.96,,boxing,0,0,0 -441111357,Jason Kenny,GBR,male,1988-03-23,1.78,81.0,cycling,3,0,0 -316427261,Jason Osborne,GER,male,1994-03-20,1.78,72.0,rowing,0,0,0 -452497047,Jason Pryor,USA,male,1987-09-26,1.76,79.0,fencing,0,0,0 -744419570,Jason Rogers,SKN,male,1991-08-31,1.73,69.0,athletics,0,0,0 -789226204,Jason Saunders,NZL,male,1990-11-22,1.88,78.0,sailing,0,0,0 -572050627,Jason Waterhouse,AUS,male,1991-11-08,1.85,75.0,sailing,0,1,0 -687929140,Jason Wilson,BAR,male,1990-10-31,1.8,68.0,triathlon,0,0,0 -242349264,Jaspar Woon Chai Yu,BRU,male,1988-11-14,1.67,60.0,badminton,0,0,0 -430283478,Jasper Aerents,BEL,male,1992-12-18,1.91,86.0,aquatics,0,0,0 -662058490,Jasper De Buyst,BEL,male,1993-11-24,1.78,69.0,cycling,0,0,0 -340877377,Jasper Lefevere,BEL,male,1988-07-13,1.71,66.0,judo,0,0,0 -19724524,Javad Mahjoub,IRI,male,1991-05-26,1.86,100.0,judo,0,0,0 -850918376,Javere Bell,JAM,male,1992-09-20,1.83,77.0,athletics,0,0,0 -532207877,Javid Chalabiyev,AZE,male,1992-05-17,1.7,,boxing,0,0,0 -228472408,Javid Hamzatau,BLR,male,1989-12-27,1.76,91.0,wrestling,0,0,1 -896286523,Javier Acevedo,CAN,male,1998-01-28,1.82,68.0,aquatics,0,0,0 -581496209,Javier Carrion,ESP,male,1990-11-09,1.88,100.0,rugby sevens,0,0,0 -389108533,Javier Cienfuegos,ESP,male,1990-07-15,1.87,110.0,athletics,0,0,0 -439713366,Javier Cortina Lacerra,CUB,male,1987-04-12,1.85,97.0,wrestling,0,0,0 -942940677,Javier Culson,PUR,male,1984-07-25,2.0,82.0,athletics,0,0,0 -340352113,Javier Garcia Gadea,CRO,male,1984-01-05,1.98,92.0,aquatics,0,1,0 -760029563,Javier Hernanz Agueria,ESP,male,1983-02-01,1.86,83.0,canoe,0,0,0 -904572193,Javier Jimenez Scull,CUB,male,1989-11-16,1.98,89.0,volleyball,0,0,0 -728027225,Javier Octavio Concepcion Rojas,CUB,male,1997-12-27,2.0,84.0,volleyball,0,0,0 -834026891,Javier Rojas,ARG,male,1991-04-15,1.88,81.0,rugby sevens,0,0,0 -23901411,Javon Francis,JAM,male,1994-12-04,1.83,68.0,athletics,0,1,0 -553955864,Jay Blankenau,CAN,male,1989-09-27,1.94,94.0,volleyball,0,0,0 -570894983,Jay Litherland,USA,male,1995-08-24,1.83,79.0,aquatics,0,0,0 -61075691,Jay Shi,USA,male,1979-02-23,1.73,61.0,shooting,0,0,0 -395224823,Jayme Mata,ARU,male,1982-12-17,1.72,67.0,judo,0,0,0 -25728763,Jaysuma Saidy Ndure,NOR,male,1984-01-01,,,athletics,0,0,0 -350157840,Jazmin Sawyers,GBR,female,1994-05-21,1.6,61.0,athletics,0,0,0 -217440009,Jazz Carlin,GBR,female,1990-09-17,1.76,62.0,aquatics,0,2,0 -23042514,Jean Baptiste Bernaz,FRA,male,1987-07-18,1.9,80.0,sailing,0,0,0 -604309234,Jean Marie Okutu,ESP,male,1988-12-17,1.78,70.0,athletics,0,0,0 -344886355,Jean Paulo Fernandes Filho,BRA,male,1995-10-26,,,football,0,0,0 -82070854,Jean Pierre Renan Bourhis,SEN,male,1995-03-29,1.78,73.0,canoe,0,0,0 -472715886,Jean Quiquampoix,FRA,male,1995-11-03,1.89,85.0,shooting,0,1,0 -601724690,Jean-Charles Valladont,FRA,male,1989-03-20,1.8,83.0,archery,0,1,0 -250421057,Jean-Julien Rojer,NED,male,1981-08-25,1.84,81.0,tennis,0,0,0 -83700650,Jean-Luc Rasamoelina,ANG,male,1989-10-04,1.8,77.0,rowing,0,0,0 -464354011,Jean-Marc Gardette,SEY,male,1984-07-04,1.69,68.0,sailing,0,0,0 -858350003,Jean-Michel Lucenay,FRA,male,1978-04-25,1.86,78.0,fencing,1,0,0 -984646645,Jean-Paul Tony Helissey,FRA,male,1990-03-28,1.77,77.0,fencing,0,1,0 -376385376,Jeanelle Scheper,LCA,female,1994-11-21,1.78,42.0,athletics,0,0,0 -714090699,Jeanette Ottesen,DEN,female,1987-12-30,1.8,72.0,aquatics,0,0,1 -317188820,Jeanine Assani Issouf,FRA,female,1992-08-17,1.7,53.0,athletics,0,0,0 -39856517,Jeanine Cicognini,ITA,female,1986-11-14,1.7,73.0,badminton,0,0,0 -437241883,Jeannine Gmelin,SUI,female,1990-06-20,1.7,72.0,rowing,0,0,0 -953949869,Jeemin Ha,KOR,male,1989-03-21,1.86,83.0,sailing,0,0,0 -555512079,Jeff Henderson,USA,male,1989-02-19,1.83,85.0,athletics,1,0,0 -553883529,Jeff Porter,USA,male,1985-11-27,1.83,86.0,athletics,0,0,0 -861092041,Jefferson Lerma,COL,male,1994-10-25,1.82,76.0,football,0,0,0 -989207170,Jefferson Milano,VEN,male,1995-11-21,1.85,85.0,cycling,0,0,0 -488880132,Jefferson Santos Pereira,QAT,male,1989-06-08,1.8,86.0,volleyball,0,0,0 -246975227,Jeffery Gibson,BAH,male,1990-08-15,,,athletics,0,0,0 -470772822,Jeffrey Hoogland,NED,male,1993-03-16,1.85,97.0,cycling,0,0,0 -936447868,Jeffrey Julmis,HAI,male,1987-09-30,1.85,79.0,athletics,0,0,0 -984151393,Jeffrey Riseley,AUS,male,1986-11-11,1.92,74.0,athletics,0,0,0 -189507792,Jeffrey Wammes,NED,male,1987-04-24,1.68,66.0,gymnastics,0,0,0 -870523852,Jehue Gordon,TTO,male,1991-12-15,1.88,81.0,athletics,0,0,0 -71774163,Jelena Jankovic,SRB,female,1985-02-28,1.75,65.0,tennis,0,0,0 -264088273,Jelena Milovanovic,SRB,female,1989-04-28,1.9,84.0,basketball,0,0,1 -225359768,Jelena Nikolic,SRB,female,1982-04-13,1.94,79.0,volleyball,0,1,0 -893813052,Jelena Ostapenko,LAT,female,1997-06-08,1.78,68.0,tennis,0,0,0 -987915621,Jelena Prokopcuka,LAT,female,1976-09-21,1.68,52.0,athletics,0,0,0 -42938484,Jelle Geens,BEL,male,1993-03-26,1.72,61.0,triathlon,0,0,0 -763826075,Jelle van Gorkom,NED,male,1991-01-05,1.87,93.0,cycling,0,1,0 -990787231,Jemima Jelagat Sumgong,KEN,female,1984-12-21,1.6,45.0,athletics,1,0,0 -784252790,Jena Hansen,DEN,female,1988-12-10,1.66,60.0,sailing,0,0,1 -488021170,Jenia Grebennikov,FRA,male,1990-08-13,1.88,85.0,volleyball,0,0,0 -247726114,Jenly Wini,SOL,female,1983-06-09,1.65,58.0,weightlifting,0,0,0 -617821916,Jenna Laukkanen,FIN,female,1995-03-02,1.82,80.0,aquatics,0,0,0 -898091953,Jenna Prandini,USA,female,1992-11-20,1.73,59.0,athletics,0,0,0 -499225934,Jennie Johansson,SWE,female,1988-06-15,1.88,71.0,aquatics,0,0,0 -970609954,Jennifer Abel,CAN,female,1991-08-23,1.6,59.0,aquatics,0,0,0 -385452766,Jennifer Cesar,VEN,female,1989-05-26,1.59,57.0,cycling,0,0,0 -188078227,Jennifer Chieng,FSM,female,1986-04-29,1.61,,boxing,0,0,0 -622375220,Jennifer Cleary,AUS,female,1993-06-22,1.75,71.0,rowing,0,0,0 -657843784,Jennifer Dahlgren,ARG,female,1984-04-21,1.8,110.0,athletics,0,0,0 -117886185,Jennifer Frank Casanas,ESP,male,1978-10-18,1.87,117.0,athletics,0,0,0 -393564226,Jennifer Galais,FRA,female,1992-03-07,1.69,59.0,athletics,0,0,0 -335752581,Jennifer Hens,AUS,female,1986-08-01,1.74,60.0,shooting,0,0,0 -74441554,Jennifer Kish,CAN,female,1988-07-07,1.72,73.0,rugby sevens,0,0,1 -225406139,Jennifer Madu,NGR,female,1994-09-23,1.68,65.0,athletics,0,0,0 -729488450,Jennifer Martins,CAN,female,1989-01-31,1.79,76.0,rowing,0,0,0 -220670322,Jennifer McIntosh,GBR,female,1991-06-17,1.75,80.0,shooting,0,0,0 -408612404,Jennifer Oeser,GER,female,1983-11-29,1.75,68.0,athletics,0,0,0 -500359548,Jennifer Simpson,USA,female,1986-08-23,1.66,53.0,athletics,0,0,1 -626745836,Jennifer Suhr,USA,female,1982-02-05,1.83,65.0,athletics,0,0,0 -626532715,Jennifer Troncy,FRA,female,1986-01-26,1.57,58.0,rugby sevens,0,0,0 -871842382,Jennifer Valente,USA,female,1994-12-24,1.76,74.0,cycling,0,1,0 -868014010,Jennifer Wenth,AUT,female,1991-07-24,1.66,47.0,athletics,0,0,0 -331294014,Jenny Alm,SWE,female,1989-04-10,1.84,80.0,handball,0,0,0 -159950160,Jenny Blundell,AUS,female,1994-05-09,1.63,49.0,athletics,0,0,0 -597803357,Jenny Elbe,GER,female,1990-04-18,1.8,64.0,athletics,0,0,0 -596979059,Jenny Lyvette Arthur,USA,female,1993-12-11,1.66,75.0,weightlifting,0,0,0 -624049012,Jenny Mensing,GER,female,1986-02-26,1.83,70.0,aquatics,0,0,0 -87849675,Jenny Rissveds,SWE,female,1994-06-06,1.65,55.0,cycling,1,0,0 -754902821,Jens Jonsson,DEN,male,1993-01-10,1.84,78.0,football,0,0,0 -229792766,Jens Schuermans,BEL,male,1993-02-13,1.75,64.0,cycling,0,0,0 -953566525,Jeong Eun Lee,KOR,female,1994-09-13,1.53,40.0,athletics,0,0,0 -188621072,Jeongah Park,KOR,female,1993-03-26,1.86,75.0,volleyball,0,0,0 -23908007,Jeongsik Won,KOR,male,1990-12-09,1.63,69.0,weightlifting,0,0,0 -981837262,Jeppe Hojbjerg,DEN,male,1995-04-30,1.95,95.0,football,0,0,0 -974287219,Jereem Richards,TTO,male,1994-01-13,1.89,,athletics,0,0,0 -787131282,Jeremie Azou,FRA,male,1989-04-02,1.78,71.0,rowing,1,0,0 -464988921,Jeremie Mion,FRA,male,1989-07-05,1.87,74.0,sailing,0,0,0 -841582256,Jeremy Aicardi,FRA,male,1988-11-26,1.78,83.0,rugby sevens,0,0,0 -862582190,Jeremy Cadot,FRA,male,1986-11-07,1.85,78.0,fencing,0,1,0 -240137595,Jeremy Desplanches,SUI,male,1994-08-07,1.89,73.0,aquatics,0,0,0 -357577386,Jeremy Dodson,SAM,male,1987-08-30,,,athletics,0,0,0 -612661000,Jeremy Hayward,AUS,male,1993-03-03,1.81,82.0,hockey,0,0,0 -867045332,Jeremy Monnier,FRA,male,1989-05-05,1.75,65.0,shooting,0,0,0 -222564871,Jeremy Rencurel,FRA,male,1995-04-13,1.81,83.0,cycling,0,0,0 -528202320,Jeremy Stravius,FRA,male,1988-07-14,1.9,87.0,aquatics,0,1,0 -414345502,Jeremy Taiwo,USA,male,1990-01-15,1.94,90.0,athletics,0,0,0 -231926753,Jeremy Toljan,GER,male,1994-08-08,1.82,77.0,football,0,1,0 -995701112,Jermaine Seoposenwe,RSA,female,1993-10-12,1.67,62.0,football,0,0,0 -882400057,Jeroen D'Hoedt,BEL,male,1990-01-10,1.83,63.0,athletics,0,0,0 -426243229,Jeroen Dubbeldam,NED,male,1973-04-15,1.85,85.0,equestrian,0,0,0 -306559370,Jeroen Hertzberger,NED,male,1986-02-24,1.74,76.0,hockey,0,0,0 -389822683,Jeroen Mooren,NED,male,1985-07-30,1.68,60.0,judo,0,0,0 -244522313,Jerome Guery,BEL,male,1980-07-24,1.84,69.0,equestrian,0,0,0 -701894934,Jerome Truyens,BEL,male,1987-08-04,1.78,70.0,hockey,0,1,0 -244166013,Jerry Tollbring,SWE,male,1995-09-13,1.82,82.0,handball,0,0,0 -790674817,Jerry Tuwai,FIJ,male,1989-03-23,1.74,81.0,rugby sevens,1,0,0 -688754355,Jerzy Janowicz,POL,male,1990-11-13,2.04,95.0,tennis,0,0,0 -455392993,Jesper Hansen,DEN,male,1980-11-19,1.8,95.0,shooting,0,0,0 -527946979,Jesper Johansson,SWE,male,1994-05-30,1.95,85.0,football,0,0,0 -73811144,Jesper Nielsen,SWE,male,1989-09-30,2.0,110.0,handball,0,0,0 -562280742,Jesper Noddesbo,DEN,male,1980-10-23,1.99,100.0,handball,1,0,0 -109939258,Jesper Stalheim,SWE,male,1988-03-23,1.84,82.0,sailing,0,0,0 -911273581,Jess Walker,GBR,female,1990-06-24,1.77,70.0,canoe,0,0,0 -691246438,Jesse David Thielke,USA,male,1992-06-09,1.71,68.0,wrestling,0,0,0 -475795038,Jesse Parahi,AUS,male,1989-07-29,1.89,104.0,rugby sevens,0,0,0 -619652365,Jesse Smith,USA,male,1983-04-27,1.94,105.0,aquatics,0,0,0 -428646755,Jessica Andrews,GBR,female,1992-10-01,1.68,52.0,athletics,0,0,0 -245831479,Jessica Ashwood,AUS,female,1993-04-28,1.73,64.0,aquatics,0,1,0 -551853296,Jessica Augusto,POR,female,1981-11-08,1.62,44.0,athletics,0,0,0 -353712682,Jessica Blaszka,NED,female,1992-08-05,1.6,52.0,wrestling,0,0,0 -172113299,Jessica Brizeida Lopez Arocha,VEN,female,1986-01-22,1.53,47.0,gymnastics,0,0,0 -429571808,Jessica Cavalheiro,BRA,female,1991-08-01,1.64,60.0,aquatics,0,0,0 -154587352,Jessica Draskau-Petersson,DEN,female,1977-09-08,1.7,62.0,athletics,0,0,0 -651225769,Jessica Eddie,GBR,female,1984-10-07,1.78,75.0,rowing,0,1,0 -668056593,Jessica Ennis-Hill,GBR,female,1986-01-28,1.65,57.0,athletics,0,1,0 -499672936,Jessica Fox,AUS,male,1994-06-10,1.66,60.0,canoe,0,0,1 -243350396,Jessica Hall,AUS,female,1992-07-13,1.83,75.0,rowing,0,0,0 -742356145,Jessica Hancco,PER,female,1995-09-10,1.53,44.0,athletics,0,0,0 -844470576,Jessica Houara,FRA,female,1987-09-29,1.61,53.0,football,0,0,0 -481042981,Jessica Inchude,GBS,female,1996-03-25,1.75,81.0,athletics,0,0,0 -629770332,Jessica Javelet,USA,female,1985-06-25,1.68,63.0,rugby sevens,0,0,0 -116285055,Jessica Maier,BRA,female,1994-08-21,1.67,52.0,gymnastics,0,0,0 -832460083,Jessica Mendoza,GBR,female,1996-04-09,1.72,62.0,equestrian,0,0,0 -803585591,Jessica Morrison,AUS,female,1992-05-18,,,rowing,0,0,0 -147271899,Jessica O'Connell,CAN,female,1989-02-10,1.58,48.0,athletics,0,0,0 -223516819,Jessica Parratto,USA,female,1994-06-26,1.58,50.0,aquatics,0,0,0 -463897901,Jessica Phoenix,CAN,female,1983-10-16,1.57,57.0,equestrian,0,0,0 -679145498,Jessica Quintino,BRA,female,1991-04-17,1.72,67.0,handball,0,0,0 -790042843,Jessica Rossi,ITA,female,1992-01-07,1.68,59.0,shooting,0,0,0 -279018477,Jessica Samuelsson,SWE,female,1992-01-30,1.66,60.0,football,0,1,0 -273050989,Jessica Thornton,AUS,female,1998-04-12,1.7,59.0,athletics,0,0,0 -210017406,Jessica Trengove,AUS,female,1987-08-15,1.66,52.0,athletics,0,0,0 -47922395,Jessica Vall Montero,ESP,female,1988-11-22,1.64,51.0,aquatics,0,0,0 -304882581,Jessie Fleming,CAN,female,1998-03-11,1.64,55.0,football,0,0,1 -343382074,Jessie Khing Lacuna,PHI,male,1993-12-23,1.7,70.0,aquatics,0,0,0 -918777342,Jessy Kramer,NED,female,1990-02-16,1.78,77.0,handball,0,0,0 -16153353,Jessy Tremouliere,FRA,female,1992-07-29,1.8,73.0,rugby sevens,0,0,0 -55862203,Jesus Alberto Perales,MEX,male,1993-12-22,1.97,88.0,volleyball,0,0,0 -957663890,Jesus Antonio Lopez Sanchez,VEN,male,1984-12-17,1.61,62.0,weightlifting,0,0,0 -340837468,Jesus Espana,ESP,male,1978-08-21,1.68,59.0,athletics,0,0,0 -673714012,Jesus Liranzo,VEN,male,1995-11-02,1.74,60.0,aquatics,0,0,0 -589547436,Jesus Rangel,MEX,male,1980-09-20,1.9,82.0,volleyball,0,0,0 -990831505,Jesus Tortosa Cabrera,ESP,male,1997-12-21,1.85,58.0,taekwondo,0,0,0 -117602961,Jeunghun Wang,KOR,male,1995-09-07,1.8,72.0,golf,0,0,0 -407205407,Jevaughn Minzie,JAM,male,1995-07-20,1.78,85.0,athletics,1,0,0 -370569700,Jevgenijs Borodavko,LAT,male,1986-11-04,1.9,100.0,judo,0,0,0 -897539865,Jeyong Son,KOR,male,1994-01-12,1.77,70.0,cycling,0,0,0 -427460731,Jeyvier Jesus Cintron,PUR,male,1995-02-08,1.71,,boxing,0,0,0 -82066790,Jhennifer Conceicao,BRA,female,1997-06-13,1.62,52.0,aquatics,0,0,0 -87944431,Jhoan Esteban Chaves Rubio,COL,male,1990-01-17,1.64,54.0,cycling,0,0,0 -507432041,Jhoanis Portilla,CUB,male,1990-07-24,1.82,76.0,athletics,0,0,0 -677554524,Jhon Perlaza,COL,male,1994-08-26,1.8,62.0,athletics,0,0,0 -183172056,Jhonatan Esquivel,URU,male,1988-10-13,1.83,86.0,rowing,0,0,0 -78371823,Jhonathan Paz,HON,male,1995-06-18,1.83,81.0,football,0,0,0 -996212053,Jhonattan Vegas,VEN,male,1984-08-19,1.91,100.0,golf,0,0,0 -518155505,Jhonnatan Botero Villegas,COL,male,1992-04-27,1.67,58.0,cycling,0,0,0 -432678731,Jhonny Perez Urena,DOM,male,1997-09-16,1.73,50.0,aquatics,0,0,0 -687736543,Jhow Benavidez,HON,male,1995-12-26,1.79,76.0,football,0,0,0 -829490625,Ji Hyun Sung,KOR,female,1991-07-29,1.75,64.0,badminton,0,0,0 -30421879,Jia Liu,AUT,female,1982-02-16,1.6,46.0,table tennis,0,0,0 -290373539,Jiahui Lou,CHN,female,1991-05-26,1.67,55.0,football,0,0,0 -249355001,Jiajia Guo,CHN,female,1994-06-06,1.71,65.0,hockey,0,0,0 -404900556,Jialu Hao,CHN,female,1987-08-20,1.76,66.0,fencing,0,1,0 -643778716,Jialuo Shi,CHN,male,1993-01-25,1.84,71.0,fencing,0,0,0 -542874859,Jian Fang Lay,AUS,female,1973-03-06,1.63,58.0,table tennis,0,0,0 -246757519,Jianan Wang,CGO,male,1983-01-20,1.77,77.0,table tennis,0,0,0 -801219490,Jianan Wang,CHN,male,1996-08-27,1.85,72.0,athletics,0,0,0 -2310622,Jianbin He,CHN,male,1993-02-18,1.88,83.0,aquatics,0,0,0 -546426458,Jianfei Ma,CHN,male,1984-07-29,1.86,78.0,fencing,0,0,0 -715962035,Jianguan Hu,CHN,male,1993-05-11,1.68,,boxing,0,0,1 -44701367,Jianli Guo,CHN,male,1988-03-06,1.78,73.0,modern pentathlon,0,0,0 -735180620,Jianlian Yi,CHN,male,1987-10-27,2.13,113.0,basketball,0,0,0 -561632085,Jianming Shu,CHN,male,1990-01-26,1.8,75.0,canoe,0,0,0 -323548303,Jiao Li,NED,female,1973-01-15,1.67,60.0,table tennis,0,0,0 -606768935,Jiao Xue,CHN,female,1993-01-30,1.71,59.0,football,0,0,0 -446754999,Jiaojiao De,CHN,female,1990-01-05,1.67,57.0,hockey,0,0,0 -964498000,Jiaqi Li,CHN,female,1995-07-02,1.68,63.0,hockey,0,0,0 -254128139,Jiaqi Zheng,USA,female,1988-01-13,1.66,42.0,table tennis,0,0,0 -24639304,Jiawei Zhang,CHN,male,1989-01-08,1.72,,boxing,0,0,0 -497442355,Jiaxin Tan,CHN,female,1996-12-03,1.48,36.0,gymnastics,0,0,1 -534753096,Jiaxin Wu,CHN,female,1997-02-28,1.64,72.0,archery,0,0,0 -72458951,Jiayu Xu,CHN,male,1995-08-19,1.87,78.0,aquatics,0,1,0 -167827568,Jidou El Moctar,MTN,male,1985-07-08,1.71,72.0,athletics,0,0,0 -628163232,Jie Chen,CHN,female,1995-02-28,1.77,65.0,aquatics,0,0,0 -121538011,Jie Dong,CHN,female,1998-10-31,1.7,62.0,aquatics,0,0,0 -928430214,Jie Li,NED,female,1984-03-06,1.65,52.0,table tennis,0,0,0 -155657317,Jie Shi Neo,SIN,female,1985-05-20,1.68,50.0,athletics,0,0,0 -900705990,Jie Yao,CHN,male,1990-09-21,1.88,85.0,athletics,0,0,0 -113342748,Jie Youn Nam,KOR,female,1983-05-25,1.71,63.0,volleyball,0,0,0 -60881505,Jieni Shao,POR,female,1994-01-25,1.69,63.0,table tennis,0,0,0 -200297041,Jigeun Jeong,KOR,male,1990-01-10,1.77,79.0,shooting,0,0,0 -934549463,Jihee Jeon,KOR,female,1992-10-28,1.59,56.0,table tennis,0,0,0 -523127994,Jihoon Kim,KOR,male,1985-06-27,1.79,75.0,sailing,0,0,0 -669978803,Jike Zhang,CHN,male,1988-02-14,1.78,70.0,table tennis,1,1,0 -359354716,Jill Witmer,USA,female,1991-10-01,1.58,52.0,hockey,0,0,0 -167215235,Jillian Alice Gallays,CAN,female,1986-10-20,1.65,53.0,wrestling,0,0,0 -102526440,Jillion Potter,USA,female,1986-07-05,1.78,79.0,rugby sevens,0,0,0 -790998306,Jim Gottfridsson,SWE,male,1992-09-02,1.91,93.0,handball,0,0,0 -980188208,Jimmy Butler,USA,male,1989-09-14,2.01,99.0,basketball,1,0,0 -930912011,Jimmy Vicaut,FRA,male,1992-02-27,1.88,83.0,athletics,0,0,0 -48636955,Jin Hee Yoon,KOR,female,1986-08-04,1.58,53.0,weightlifting,0,0,1 -585498699,Jin Ma,CHN,female,1988-05-07,1.67,58.0,badminton,0,0,0 -526447222,Jin Wei Timothee Yap,SIN,male,1994-11-05,1.78,66.0,athletics,0,0,0 -514671382,Jing Yi Tee,MAS,female,1991-02-08,1.67,58.0,badminton,0,0,0 -617397429,Jing Zhang,CHN,female,1996-06-16,1.66,62.0,aquatics,0,0,0 -197574532,Jingbin Zhao,CHN,male,1990-04-18,1.85,72.0,rowing,0,0,0 -535068479,Jingjing Zhang,CHN,female,1988-12-03,1.64,65.0,shooting,0,0,0 -800643493,Jingli Duan,CHN,female,1989-03-08,1.8,76.0,rowing,0,0,1 -890471066,Jinglin Shi,CHN,female,1993-01-03,1.75,69.0,aquatics,0,0,1 -663047032,Jingnan Zhao,CHN,female,1995-03-07,1.68,51.0,gymnastics,0,0,0 -822617830,Jingyao Yu,CHN,female,1999-02-13,1.78,63.0,aquatics,0,0,0 -343852409,Jingyu Wu,CHN,female,1987-02-01,1.67,51.0,taekwondo,0,0,0 -618968829,Jinhwa Jung,KOR,male,1989-05-25,1.83,75.0,modern pentathlon,0,0,0 -262158592,Jinjie Gong,CHN,female,1986-11-12,1.65,65.0,cycling,1,0,0 -307605969,Jinq En Phee,MAS,female,1997-11-29,1.66,51.0,aquatics,0,0,0 -316863678,Jinrong Zhang,CHN,female,1997-03-24,1.63,58.0,hockey,0,0,0 -350253517,Jinson Johnson,IND,male,1991-03-15,,,athletics,0,0,0 -763898907,Jinsun Jung,KOR,male,1984-01-24,1.85,83.0,fencing,0,0,0 -187824275,Jinyi Kim,KOR,female,1993-06-20,1.79,82.0,handball,0,0,0 -417847231,Jinyoung Park,KOR,female,1997-04-14,1.62,58.0,aquatics,0,0,0 -303862843,Jiowana Sauto,FIJ,female,1998-03-13,1.7,69.0,rugby sevens,0,0,0 -715756785,Jip Vastenburg,NED,female,1994-03-21,1.81,57.0,athletics,0,0,0 -446797398,Jiri Beran,CZE,male,1982-01-18,1.92,80.0,fencing,0,0,0 -610244706,Jiri Kopac,CZE,male,1982-02-23,1.75,73.0,rowing,0,0,0 -678435777,Jiri Orsag,CZE,male,1989-01-05,1.81,127.0,weightlifting,0,0,0 -280267428,Jiri Prskavec,CZE,male,1993-05-18,1.73,69.0,canoe,0,0,1 -482638625,Jiri Sykora,CZE,male,1995-01-20,1.9,91.0,athletics,0,0,0 -514339084,Jirina Ptacnikova,CZE,female,1986-05-20,1.73,68.0,athletics,0,0,0 -931051373,Jisna Mathew,IND,female,1999-01-07,1.56,45.0,athletics,0,0,0 -824138324,Jisu Yoon,KOR,female,1993-01-24,1.7,60.0,fencing,0,0,0 -191271695,Jitbahadur Muktan,NEP,male,1979-08-31,1.68,64.0,archery,0,0,0 -323638966,Jitu Rai,IND,male,1987-08-26,1.6,64.0,shooting,0,0,0 -426963180,Jiwei Zhao,CHN,male,1995-08-25,1.85,80.0,basketball,0,0,0 -617915347,Jiyeon Kim,KOR,female,1988-03-12,1.65,56.0,fencing,0,0,0 -59062825,Jiyeon Seo,KOR,female,1993-03-03,1.68,56.0,fencing,0,0,0 -609901100,Jiyun Bak,KOR,female,1992-09-21,1.68,63.0,judo,0,0,0 -450037475,Jo Aleh,NZL,female,1986-05-15,1.71,58.0,sailing,0,1,0 -476728230,Jo-Wilfried Tsonga,FRA,male,1985-04-17,1.88,93.0,tennis,0,0,0 -666014603,Joachim Bottieau,BEL,male,1989-03-20,1.8,81.0,judo,0,0,0 -472743592,Joachim Eilers,GER,male,1990-04-02,1.85,90.0,cycling,0,0,0 -845052105,Joachim Fischer nielsen,DEN,male,1978-11-23,1.88,78.0,badminton,0,0,0 -818759516,Joahnys Argilagos,CUB,male,1997-01-11,1.52,,boxing,0,0,1 -629037192,Joakim Nilsson,SWE,male,1994-02-06,1.83,72.0,football,0,0,0 -99502290,Joan Herp Morell,ESP,male,1993-10-18,1.86,70.0,sailing,0,0,0 -861221421,Joan Lluis Pons Ramon,ESP,male,1996-12-09,1.7,62.0,aquatics,0,0,0 -20182796,Joan Losada,ESP,male,1992-06-20,1.82,86.0,rugby sevens,0,0,0 -727390459,Joana Costa,BRA,female,1981-05-15,1.73,60.0,athletics,0,0,0 -827210210,Joana Heidrich,SUI,female,1991-10-02,1.9,72.0,volleyball,0,0,0 -676801349,Joana Palacios,ARG,female,1996-11-08,1.6,63.0,weightlifting,0,0,0 -278025815,Joana Ramos,POR,female,1982-01-16,1.59,52.0,judo,0,0,0 -736604724,Joanna Evans,BAH,female,1997-07-25,1.8,66.0,aquatics,0,0,0 -951232651,Joanna Fiodorow,POL,female,1989-03-04,1.69,94.0,athletics,0,0,0 -215850400,Joanna Jozwik,POL,female,1991-01-30,1.69,53.0,athletics,0,0,0 -941360584,Joanna Leigh,GBR,female,1993-02-22,1.65,61.0,hockey,0,0,0 -489677377,Joanna Leszczynska,POL,female,1988-12-18,1.83,75.0,rowing,0,0,1 -189833153,Joanna Linkiewicz,POL,female,1990-05-02,1.68,55.0,athletics,0,0,0 -487036929,Joanna Maranhao,BRA,female,1987-04-29,1.75,70.0,aquatics,0,0,0 -929833418,Joanna Rowsell-Shand,GBR,female,1988-12-05,1.8,69.0,cycling,1,0,0 -943408954,Joanna Zachoszcz,POL,female,1993-04-17,1.77,66.0,aquatics,0,0,0 -984524568,Joanne Faavesi,USA,female,1992-02-05,1.68,73.0,rugby sevens,0,0,0 -279099654,Joanne Pavey,GBR,female,1973-09-20,1.63,50.0,athletics,0,0,0 -357940683,Joanne Watmore,GBR,female,1986-09-25,1.79,74.0,rugby sevens,0,0,0 -964624583,Joao Costa,POR,male,1964-10-28,1.82,100.0,shooting,0,0,0 -544379806,Joao Gomes,BRA,male,1986-01-21,1.9,88.0,aquatics,0,0,0 -612164029,Joao Monteiro,POR,male,1983-08-29,1.8,71.0,table tennis,0,0,0 -224259489,Joao Paulo de Leiria E Silva,ANG,male,1964-11-13,1.76,90.0,shooting,0,0,0 -259002081,Joao Pereira,POR,male,1987-12-28,1.86,72.0,triathlon,0,0,0 -345046292,Joao Ribeiro,POR,male,1989-08-19,1.84,87.0,canoe,0,0,0 -238238213,Joao Rodrigues,POR,male,1971-11-02,1.79,73.0,sailing,0,0,0 -52128128,Joao Silva,POR,male,1989-05-15,1.71,60.0,triathlon,0,0,0 -523514105,Joao Sousa,POR,male,1989-03-30,1.85,73.0,tennis,0,0,0 -385773930,Joao Victor Marcari Oliva,BRA,male,1996-02-02,1.82,65.0,equestrian,0,0,0 -891766253,Joao Vieira,POR,male,1976-02-20,1.75,59.0,athletics,0,0,0 -62478596,Joao Virginia,POR,male,1999-10-10,1.82,72.0,football,0,0,0 -343594006,Joao Vitor de Oliveira,BRA,male,1992-05-15,1.9,87.0,athletics,0,0,0 -924207908,Joao da Silva,BRA,male,1994-01-29,1.9,93.0,handball,0,0,0 -949399778,Joao de Lucca,BRA,male,1990-01-06,1.93,95.0,aquatics,0,0,0 -874040358,Joaquim Lobo,MOZ,male,1995-01-06,1.72,66.0,canoe,0,0,0 -548003782,Joaquim Rodriguez Oliver,ESP,male,1979-05-12,1.69,59.0,cycling,0,0,0 -779626305,Joaquin Arzura,ARG,male,1993-05-18,1.7,71.0,football,0,0,0 -572622269,Joaquin Blanco Albalat,ESP,male,1989-06-25,1.81,80.0,sailing,0,0,0 -443476953,Joaquin Lopez,BRA,male,1990-02-12,1.7,76.0,hockey,0,0,0 -202078151,Joaquin Menini,ARG,male,1991-08-18,1.82,76.0,hockey,1,0,0 -981125983,Jodie Kenny,AUS,female,1987-08-18,1.83,74.0,hockey,0,0,0 -525649911,Jodie Williams,GBR,female,1993-09-28,1.73,65.0,athletics,0,0,0 -854286799,Jody February,RSA,male,1996-05-12,1.82,72.0,football,0,0,0 -874371123,Joe Ingles,AUS,male,1987-10-02,2.04,91.0,basketball,0,0,0 -921707760,Joe Joyce,GBR,male,1985-09-19,1.97,,boxing,0,1,0 -21878416,Joe Kayes,AUS,male,1991-01-03,1.98,125.0,aquatics,0,0,0 -251331480,Joe Kovacs,USA,male,1989-06-28,1.83,133.0,athletics,0,1,0 -475553078,Joe Mahit,VAN,male,1992-07-17,1.7,66.0,judo,0,0,0 -166825342,Joe Maloy,USA,male,1985-12-20,1.76,64.0,triathlon,0,0,0 -886636468,Joe Morris,USA,male,1989-08-17,1.78,79.0,sailing,0,0,0 -650947541,Joe Webber,NZL,male,1993-08-27,1.85,95.0,rugby sevens,0,0,0 -751008649,Joedison Teixeira,BRA,male,1994-01-28,1.72,,boxing,0,0,0 -772292752,Joel Baden,AUS,male,1996-02-01,1.9,70.0,athletics,0,0,0 -64143047,Joel Dennerley,AUS,male,1987-06-25,1.95,91.0,aquatics,0,0,0 -545645224,Joel Gonzalez Bonilla,ESP,male,1989-09-30,1.85,68.0,taekwondo,0,0,1 -551792957,Joel Pereira,POR,male,1996-06-28,1.91,80.0,football,0,0,0 -666790332,Joel Swift,AUS,male,1990-06-14,1.9,103.0,aquatics,0,0,0 -603523767,Joelle Sandrine Mbumi Nkouindjin,CMR,female,1986-05-25,1.7,63.0,athletics,0,0,0 -324305623,Joelma Sousa,BRA,female,1984-07-13,1.72,49.0,athletics,0,0,0 -808443365,Joerdis Steinegger,AUT,female,1983-02-08,1.72,59.0,aquatics,0,0,0 -418059967,Joeri Verlinden,NED,male,1988-01-22,1.81,75.0,aquatics,0,0,0 -363167392,Joffrey Lauvergne,FRA,male,1991-09-30,2.09,115.0,basketball,0,0,0 -11290157,Johan Jakobsson,SWE,male,1987-02-12,1.95,98.0,handball,0,0,0 -203049800,Johan Magnus Euren,SWE,male,1985-05-18,1.92,120.0,wrestling,0,0,0 -303272993,Johanna Bundsen,SWE,female,1991-06-03,1.85,72.0,handball,0,0,0 -980564711,Johanna Goliszewski,GER,female,1986-05-09,1.72,64.0,badminton,0,0,0 -624185811,Johanna Konta,GBR,female,1991-05-17,1.8,69.0,tennis,0,0,0 -886841889,Johanna Larsson,SWE,female,1988-08-17,1.74,65.0,tennis,0,0,0 -25172268,Johanna Umurungi,RWA,female,1996-04-07,,,aquatics,0,0,0 -824255240,Johannes Hintze,GER,male,1999-07-05,1.93,82.0,aquatics,0,0,0 -735194395,Johannes Schoettler,GER,male,1984-08-27,1.92,84.0,badminton,0,0,0 -292388497,Johannes Vetter,GER,male,1993-03-26,1.88,103.0,athletics,0,0,0 -764645021,John Ampomah,GHA,male,1990-07-11,1.91,100.0,athletics,0,0,0 -327081877,John Collins,GBR,male,1989-01-24,1.92,95.0,rowing,0,0,0 -112263685,John Cotterill,AUS,male,1987-10-27,1.93,88.0,aquatics,0,0,0 -912933755,John Cox,VEN,male,1981-07-06,1.96,95.0,basketball,0,0,0 -151077400,John Edison Rodriguez,COL,male,1991-01-24,2.05,102.0,fencing,0,0,0 -165939913,John Gordon Perrin,CAN,male,1989-08-17,2.01,95.0,volleyball,0,0,0 -827895855,John Jackson,IRL,male,1986-02-21,1.7,73.0,hockey,0,0,0 -821941821,John Jermyn,IRL,male,1982-03-30,1.86,80.0,hockey,0,0,0 -190324143,John Kibet Koech,BRN,male,1995-08-23,1.68,53.0,athletics,0,0,0 -914738719,John Mann,USA,male,1985-06-27,1.99,113.0,aquatics,0,0,0 -774717983,John Millman,AUS,male,1989-06-14,1.83,79.0,tennis,0,0,0 -592750184,John Murillo,COL,male,1984-07-13,1.86,85.0,athletics,0,0,0 -444015390,John Nunn,USA,male,1978-02-03,1.88,78.0,athletics,0,0,0 -299026310,John Obi Mikel,NGR,male,1987-04-22,1.73,,football,0,0,1 -699155379,John Peers,AUS,male,1988-07-25,1.91,83.0,tennis,0,0,0 -925467068,John Porch,AUS,male,1994-03-04,1.85,82.0,rugby sevens,0,0,0 -373274768,John Ruuka,KIR,male,1995-08-13,1.64,64.0,athletics,0,0,0 -821790736,John Smith,RSA,male,1990-01-12,1.92,72.0,rowing,0,0,0 -965077119,John Storey,NZL,male,1987-07-19,1.86,85.0,rowing,0,0,0 -229427449,John Whitaker,GBR,male,1955-08-05,1.61,70.0,equestrian,0,0,0 -110489298,John-John Dohmen,BEL,male,1988-01-24,1.74,69.0,hockey,0,1,0 -737599393,Johnathan Akinyemi,NGR,male,1988-11-22,1.88,85.0,canoe,0,0,0 -881559990,Johnathan Cabral,CAN,male,1992-12-31,1.9,84.0,athletics,0,0,0 -734388097,Johnny Palacios,HON,male,1986-12-20,1.83,84.0,football,0,0,0 -556532302,Joice Rodrigues,BRA,female,1986-09-06,1.68,65.0,basketball,0,0,0 -238860234,Joice Souza da Silva,BRA,female,1983-07-20,1.67,59.0,wrestling,0,0,0 -396780204,Jolanda Annen,SUI,female,1992-09-11,1.66,56.0,triathlon,0,0,0 -22338705,Jolanda Neff,SUI,female,1993-01-05,1.68,53.0,cycling,0,0,0 -317825722,Jolanta Ogar,AUT,female,1982-04-28,1.78,68.0,sailing,0,0,0 -808313337,Jolien D'hoore,BEL,female,1990-03-14,1.76,64.0,cycling,0,0,1 -774577987,Jomana Elmaghrabi,EGY,female,1995-06-21,1.62,51.0,aquatics,0,0,0 -911513755,Jon Izaguirre Insausti,ESP,male,1989-02-04,1.72,61.0,cycling,0,0,0 -793991394,Jon Schofield,GBR,male,1985-05-10,1.8,80.0,canoe,0,1,0 -995512721,Jon Wi Choe,PRK,male,1993-06-29,1.7,77.0,weightlifting,0,0,0 -640178074,Jonas Crivella,BRA,male,1988-04-30,1.81,82.0,aquatics,0,0,0 -709330716,Jonas Hogh-Christensen,DEN,male,1981-05-21,1.86,102.0,sailing,0,0,0 -197342499,Jonas Junias Jonas,NAM,male,1993-11-24,1.81,,boxing,0,0,0 -413205965,Jonas Kaspar,CZE,male,1991-10-08,1.82,82.0,canoe,0,0,0 -510673064,Jonas Maciulis,LTU,male,1985-02-10,1.98,103.0,basketball,0,0,0 -510906933,Jonas Valanciunas,LTU,male,1992-05-06,2.11,120.0,basketball,0,0,0 -993012758,Jonas Warrer,DEN,male,1979-03-22,1.81,77.0,sailing,0,0,0 -572841797,Jonatan Hajdu,HUN,male,1996-06-28,1.88,82.0,canoe,0,0,0 -621973877,Jonathan Alan Smith,RSA,male,1992-02-19,1.95,93.0,rowing,0,0,0 -65169876,Jonathan Bell,IRL,male,1987-06-19,1.78,77.0,hockey,0,0,0 -6885634,Jonathan Borlee,BEL,male,1988-02-22,1.8,69.0,athletics,0,0,0 -339303461,Jonathan Brownlee,GBR,male,1990-04-30,1.81,70.0,triathlon,0,1,0 -254041793,Jonathan Calleri,ARG,male,1993-09-23,1.73,78.0,football,0,0,0 -809086718,Jonathan Castroviejo Nicolas,ESP,male,1987-04-27,1.72,64.0,cycling,0,0,0 -507080639,Jonathan David Gomez Noriega,COL,male,1996-04-19,1.78,78.0,aquatics,0,0,0 -474793730,Jonathan Drack,MRI,male,1988-11-06,1.84,73.0,athletics,0,0,0 -620385089,Jonathan Groth,DEN,male,1992-11-09,1.84,75.0,table tennis,0,0,0 -803640740,Jonathan Koch,GER,male,1985-10-29,1.83,73.0,rowing,0,0,0 -637083466,Jonathan Laugel,FRA,male,1993-01-30,1.94,100.0,rugby sevens,0,0,0 -609847913,Jonathan Lobert,FRA,male,1985-04-30,1.95,100.0,sailing,0,0,0 -778006833,Jonathan Moriame,FRA,male,1984-06-19,2.03,100.0,aquatics,0,0,0 -664121704,Jonathan Rieckmann,BRA,male,1987-08-20,1.71,54.0,athletics,0,0,0 -280719029,Jonathan Stenbacken,SWE,male,1988-01-07,1.95,93.0,handball,0,0,0 -510827495,Jonathan Walton,GBR,male,1990-10-06,1.91,93.0,rowing,0,0,0 -412468662,Jonathan Wright,NZL,male,1992-03-14,2.01,97.0,rowing,0,0,0 -81546527,Jonelle Price,NZL,female,1980-10-14,1.62,57.0,equestrian,0,0,0 -724095045,Jong Sim Rim,PRK,female,1993-02-05,1.62,74.0,weightlifting,1,0,0 -867882464,Jong Su Kim,PRK,male,1977-01-01,1.66,67.0,shooting,0,0,0 -438574326,Jongeun Kim,KOR,female,1986-02-18,1.67,58.0,hockey,0,0,0 -342096767,Jonghyun Kim,KOR,male,1985-07-21,1.7,73.0,shooting,0,1,0 -326377611,Jongoh Jin,KOR,male,1979-09-24,1.75,78.0,shooting,1,0,0 -318221769,Jonna Andersson,SWE,female,1993-01-02,1.67,64.0,football,0,1,0 -160814323,Jono Clegg,GBR,male,1989-07-14,1.86,72.0,rowing,0,0,0 -497079798,Jonty Evans,IRL,male,1971-10-04,1.93,82.0,equestrian,0,0,0 -193398244,Joonas Lindgren,FIN,male,1986-05-31,1.7,65.0,sailing,0,0,0 -183836938,Joonyong Seo,KOR,male,1988-03-14,1.74,65.0,cycling,0,0,0 -80963554,Joost Luiten,NED,male,1986-01-07,1.77,78.0,golf,0,0,0 -964164116,Joost van der Burg,NED,male,1993-12-11,1.81,72.0,cycling,0,0,0 -604210309,Jordan Augier,LCA,male,1994-11-14,1.71,53.0,aquatics,0,0,0 -543762791,Jordan Chipangama,ZAM,male,1988-11-12,1.73,53.0,athletics,0,0,0 -151605048,Jordan Coelho,FRA,male,1992-04-02,1.83,75.0,aquatics,0,0,0 -204399496,Jordan Ernest Burroughs,USA,male,1988-07-08,1.71,79.0,wrestling,0,0,0 -277046821,Jordan Larson-Burbach,USA,female,1986-10-16,1.88,75.0,volleyball,0,0,1 -246372668,Jordan Larsson,SWE,male,1997-06-20,1.73,68.0,football,0,0,0 -402518838,Jordan Mageo,ASA,female,1997-01-06,1.72,68.0,athletics,0,0,0 -89037230,Jordan Pothain,FRA,male,1994-10-14,1.87,74.0,aquatics,0,0,0 -780854442,Jordan Silva,MEX,male,1994-07-30,1.86,75.0,football,0,0,0 -900671149,Jordan Thompson,AUS,male,1994-04-20,1.83,73.0,tennis,0,0,0 -710046367,Jordan Wilimovsky,USA,male,1994-04-22,1.78,68.0,aquatics,0,0,0 -221513683,Jordan Wood,AUS,male,1994-08-01,1.82,78.0,canoe,0,0,0 -821771282,Jordi Xammar Hernandez,ESP,male,1993-12-02,1.76,63.0,sailing,0,0,0 -339973873,Jordin Andrade,CPV,male,1992-05-05,1.82,77.0,athletics,0,0,0 -611217392,Joren Tromp,NED,male,1988-11-01,1.9,85.0,athletics,0,0,0 -968135549,Jorge Antonio Garcia,CUB,male,1988-01-14,1.8,78.0,canoe,0,0,0 -21141252,Jorge Armando Ruiz,COL,male,1989-05-17,1.68,58.0,athletics,0,0,0 -818827545,Jorge Barajas,MEX,male,1991-05-07,1.88,80.0,volleyball,0,0,0 -476081047,Jorge Campos,CUB,male,1991-09-19,1.84,77.0,table tennis,0,0,0 -272138135,Jorge Carrera,ESP,male,1982-06-12,1.8,83.0,hockey,0,0,0 -178939509,Jorge Castelblanco,PAN,male,1987-09-23,1.69,58.0,athletics,0,0,0 -761218329,Jorge Diaz,ESP,male,1985-11-26,1.73,75.0,shooting,0,0,0 -729677214,Jorge Fonseca,POR,male,1992-10-30,1.75,100.0,judo,0,0,0 -947023674,Jorge Grau Potrille,CUB,male,1987-02-15,1.86,78.0,shooting,0,0,0 -304222396,Jorge Lima,POR,male,1981-01-29,1.77,72.0,sailing,0,0,0 -822554462,Jorge Llames,ESP,male,1978-10-17,1.7,74.0,shooting,0,0,0 -728621076,Jorge Luis Vivas,COL,male,1988-01-22,1.72,,boxing,0,0,0 -811026477,Jorge Mario Murillo Valdes,COL,male,1991-09-07,1.86,77.0,aquatics,0,0,0 -344287763,Jorge Quinones,MEX,male,1981-11-13,1.86,80.0,volleyball,0,0,0 -465083316,Jorge Torres,MEX,male,1988-01-16,1.8,76.0,football,0,0,0 -323103504,Jorge Vides,BRA,male,1992-11-24,1.92,74.0,athletics,0,0,0 -942961104,Jorge Y. Fernandez,CUB,male,1987-10-02,1.9,100.0,athletics,0,0,0 -473715878,Jorge Zarif,BRA,male,1992-09-30,1.91,97.0,sailing,0,0,0 -696332075,Jorinde Verwimp,BEL,female,1994-11-10,1.85,65.0,equestrian,0,0,0 -692369210,Joris Daudet,FRA,male,1991-02-12,1.84,78.0,cycling,0,0,0 -544349833,Joris Pijs,NED,male,1987-04-02,1.86,70.0,rowing,0,0,0 -489958654,Joris Vanspringel,BEL,male,1963-02-08,1.71,69.0,equestrian,0,0,0 -680257041,Jorrit Croon,NED,male,1998-08-09,1.83,75.0,hockey,0,0,0 -436353228,Jort van Gennep,NED,male,1994-08-06,1.85,70.0,rowing,0,0,0 -119403428,Josateki Naulu,FIJ,male,1984-06-08,1.75,81.0,judo,0,0,0 -912647589,Jose Abella,MEX,male,1994-02-10,1.76,74.0,football,0,0,0 -742377387,Jose Alessandro Bagio,BRA,male,1981-04-16,1.72,63.0,athletics,0,0,0 -934177489,Jose Amado Garcia,GUA,male,1977-09-13,1.77,59.0,athletics,0,0,0 -914289728,Jose Antonio Hermida Ramos,ESP,male,1978-08-24,1.72,67.0,cycling,0,0,0 -175796769,Jose Armenteros,CUB,male,1992-12-13,1.89,100.0,judo,0,0,0 -96353822,Jose Barralaga,HON,male,1994-12-22,1.75,74.0,football,0,0,0 -44722554,Jose Carlos Herrera,MEX,male,1986-02-05,1.87,81.0,athletics,0,0,0 -733148726,Jose Carlos Moreira,BRA,male,1983-09-28,1.72,80.0,athletics,0,0,0 -432697799,Jose Carvalho,POR,male,1988-08-18,1.75,77.0,canoe,0,0,0 -646368101,Jose Costa,POR,male,1984-01-26,1.82,82.0,sailing,0,0,0 -583146817,Jose Daniel Diaz Robertti,VEN,male,1989-02-22,1.81,96.0,wrestling,0,0,0 -265525269,Jose Daniel Martin Dockx,ESP,male,1974-01-07,1.83,74.0,equestrian,0,0,0 -20513481,Jose Guilherme de Toledo,BRA,male,1994-01-11,1.93,97.0,handball,0,0,0 -710392751,Jose Gutierrez,VEN,male,1992-10-12,1.93,83.0,sailing,0,0,0 -515906781,Jose Ignacio Diaz,ESP,male,1979-11-22,1.68,67.0,athletics,0,0,0 -112540010,Jose Joao Pimenta Costa Mendes,POR,male,1985-04-24,1.81,65.0,cycling,0,0,0 -848550329,Jose Leonardo Montana,COL,male,1992-03-21,1.74,65.0,athletics,0,0,0 -64401943,Jose Leyver Ojeda,MEX,male,1985-11-12,1.64,60.0,athletics,0,0,0 -480752909,Jose Luis Gaspar,CUB,male,1995-08-25,1.88,72.0,athletics,0,0,0 -176573380,Jose Luis Gomez,ARG,male,1993-09-13,1.67,70.0,football,0,0,0 -314838135,Jose Luis Gonzalez,ARG,male,1984-12-27,2.06,97.0,volleyball,0,0,0 -767377720,Jose Luis Rodriguez,CHI,male,1994-06-01,1.8,75.0,cycling,0,0,0 -165713774,Jose Manuel Calderon,ESP,male,1981-09-28,1.91,90.0,basketball,0,0,1 -225565069,Jose Maria Larocca,ARG,male,1969-01-01,1.87,83.0,equestrian,0,0,0 -756729947,Jose Maria Raymundo,GUA,male,1993-09-01,1.56,49.0,athletics,0,0,0 -64114851,Jose Martinez,MEX,male,1993-01-23,2.0,100.0,volleyball,0,0,0 -674292992,Jose Melendez,VEN,male,1993-05-19,1.7,70.0,athletics,0,0,0 -77380631,Jose Pena,VEN,male,1987-01-12,1.6,52.0,athletics,0,0,0 -828985174,Jose Quintanilla,BOL,male,1997-01-01,2.0,89.0,aquatics,0,0,0 -599772179,Jose Ramos,GUA,male,1994-11-09,1.56,60.0,judo,0,0,0 -145726121,Jose Ricardo Figueroa,CUB,male,1991-01-10,1.84,64.0,modern pentathlon,0,0,0 -178619727,Jose Vargas,VEN,male,1982-01-23,1.96,109.0,basketball,0,0,0 -760060377,Jose van Veen,NED,female,1986-01-09,1.95,87.0,rowing,0,0,0 -546457869,Jose-Filipe Lima,POR,male,1981-11-26,1.77,75.0,golf,0,0,0 -537078452,Josee Belanger,CAN,female,1986-05-14,1.63,63.0,football,0,0,1 -168497661,Josef Dostal,CZE,male,1993-03-03,2.02,115.0,canoe,0,1,1 -351583225,Josefa Fabiola Almeida De Sousa Alves,BRA,female,1983-02-03,1.84,70.0,volleyball,0,0,0 -938961398,Josefa Vila Betancurt,CHI,female,1997-02-06,1.69,60.0,rowing,0,0,0 -921723264,Josefin Olsson,SWE,female,1989-08-23,1.72,67.0,sailing,0,0,0 -184806323,Josefina Fernandez,ARG,female,1991-08-17,1.75,72.0,volleyball,0,0,0 -384555082,Joselito Velazquez,MEX,male,1993-09-30,1.64,,boxing,0,0,0 -945703153,Josep Romeu,ESP,male,1990-05-22,1.75,77.0,hockey,0,0,0 -128111481,Joseph Choong,GBR,male,1995-05-23,1.86,78.0,modern pentathlon,0,0,0 -943343126,Joseph Clarke,GBR,male,1992-11-03,1.82,76.0,canoe,1,0,0 -872493079,Joseph Cordina,GBR,male,1991-12-01,1.75,,boxing,0,0,0 -722417692,Joseph Emilienne Essombe Tiako,CMR,female,1988-03-22,1.59,55.0,wrestling,0,0,0 -748279929,Joseph Polossifakis,CAN,male,1990-08-21,1.78,77.0,fencing,0,0,0 -193706829,Joseph Schooling,SIN,male,1995-06-16,1.84,78.0,aquatics,1,0,0 -626123517,Joseph Turagabeci,FIJ,male,1994-11-19,1.69,67.0,football,0,0,0 -610541912,Joseph Ward,IRL,male,1993-10-30,1.86,,boxing,0,0,0 -604939981,Josephine Henning,GER,female,1989-09-08,1.75,68.0,football,1,0,0 -491045243,Josephine Jacques Andre Coquin,FRA,female,1990-09-21,1.7,51.0,fencing,0,0,0 -208075373,Josh Akognon,NGR,male,1986-02-10,1.89,83.0,basketball,0,0,0 -990888036,Josh Binstock,CAN,male,1981-01-12,1.96,99.0,volleyball,0,0,0 -429283935,Josh Junior,NZL,male,1989-12-22,1.89,96.0,sailing,0,0,0 -213543405,Josh Kelly,GBR,male,1994-03-07,1.78,,boxing,0,0,0 -234184817,Josh Konieczny,USA,male,1991-05-26,1.81,74.0,rowing,0,0,0 -820424202,Josh Prenot,USA,male,1993-07-28,1.81,75.0,aquatics,0,1,0 -7139077,Joshua Beaver,AUS,male,1993-03-01,1.75,70.0,aquatics,0,0,0 -758033306,Joshua Booth,AUS,male,1990-10-09,1.9,93.0,rowing,0,1,0 -267338735,Joshua Buatsi,GBR,male,1993-03-14,1.85,,boxing,0,0,1 -357476242,Joshua Dunkley-Smith,AUS,male,1989-06-28,1.94,98.0,rowing,0,1,0 -27887256,Joshua E Tibatemwa,UGA,male,1996-09-10,,,aquatics,0,0,0 -846703092,Joshua Ilustre,GUM,male,1994-01-23,1.78,70.0,athletics,0,0,0 -858829009,Joshua Katz,AUS,male,1997-12-29,1.67,60.0,judo,0,0,0 -894182503,Joshua Kiprui Cheptegei,UGA,male,1996-09-12,,,athletics,0,0,0 -341433396,Joshua Palmer,AUS,male,1991-08-10,1.86,87.0,aquatics,0,0,0 -485505257,Joshua Richmond,USA,male,1985-12-19,1.88,90.0,shooting,0,0,0 -761016081,Joshua Robinson,AUS,male,1985-10-04,1.87,92.0,athletics,0,0,0 -299902345,Joshua Samuels,USA,male,1991-07-08,1.94,95.0,aquatics,0,0,0 -274974371,Josip Glasnovic,CRO,male,1983-05-07,1.78,82.0,shooting,1,0,0 -751292408,Josip Pavic,CRO,male,1982-01-15,1.95,90.0,aquatics,0,1,0 -981156093,Josip Vrlic,BRA,male,1986-04-25,1.96,120.0,aquatics,0,0,0 -937359847,Jossimar Orlando Calvo Moreno,COL,male,1994-07-22,1.6,54.0,gymnastics,0,0,0 -924692154,Josua Tuisova,FIJ,male,1994-02-04,1.8,108.0,rugby sevens,1,0,0 -73761098,Josue Brachi Garcia,ESP,male,1992-09-08,1.56,56.0,weightlifting,0,0,0 -910659584,Josue Deprez,HAI,male,1981-10-30,,,judo,0,0,0 -298393136,Joud Fahmy,KSA,female,1994-02-20,1.64,52.0,judo,0,0,0 -441296734,Jovana Brakocevic,SRB,female,1988-03-05,1.96,82.0,volleyball,0,1,0 -113412907,Jovana Crnogorac,SRB,female,1992-02-29,1.7,56.0,cycling,0,0,0 -383964072,Jovana Stevanovic,SRB,female,1992-06-30,1.92,72.0,volleyball,0,1,0 -862971259,Jovana Terzic,MNE,female,1999-05-15,1.65,54.0,aquatics,0,0,0 -123069531,Jovana de la Cruz,PER,female,1992-07-12,1.61,53.0,athletics,0,0,0 -434077737,Jovanka Radicevic,MNE,female,1986-10-23,1.7,68.0,handball,0,0,0 -724258615,Jovina Choo,SIN,female,1990-02-10,1.64,57.0,sailing,0,0,0 -433904148,Joyce Sombroek,NED,female,1990-09-10,1.79,64.0,hockey,0,1,0 -85512245,Jozef Repcik,SVK,male,1986-08-03,1.9,72.0,athletics,0,0,0 -144854938,Juan Carlos Cabrera,MEX,male,1991-11-09,1.94,105.0,rowing,0,0,0 -17067669,Juan Carlos Carrillo,COL,male,1992-10-10,1.84,,boxing,0,0,0 -735807847,Juan Carlos Trujillo,GUA,male,1985-07-17,1.45,58.0,athletics,0,0,0 -766482103,Juan Diego Turcios,ESA,male,1992-09-22,1.79,81.0,judo,0,0,0 -867791853,Juan Gilardi,ARG,male,1981-11-14,1.86,91.0,hockey,1,0,0 -95348169,Juan Ignacio Caceres,ARG,male,1992-01-31,1.68,75.0,canoe,0,0,0 -435791254,Juan Ignacio Maegli,GUA,male,1988-07-21,1.83,84.0,sailing,0,0,0 -773684014,Juan Ignacio Rodriguez Liebana,ESP,male,1992-04-19,1.85,83.0,archery,0,0,0 -565597273,Juan Imhoff,ARG,male,1988-05-11,1.78,79.0,rugby sevens,0,0,0 -172353646,Juan Lopez,ARG,male,1985-05-27,1.78,74.0,hockey,1,0,0 -322112942,Juan Manuel Cano,ARG,male,1987-12-12,1.68,60.0,athletics,0,0,0 -436803627,Juan Martin Del Potro,ARG,male,1988-09-23,1.98,97.0,tennis,0,1,0 -48480691,Juan Miguel Rodriguez Martinez,CUB,male,1967-05-26,1.83,86.0,shooting,0,0,0 -919263299,Juan Monaco,ARG,male,1984-03-29,1.85,77.0,tennis,0,0,0 -517352231,Juan Nogueira,BRA,male,1988-05-01,1.86,,boxing,0,0,0 -516208233,Juan Pablo Estelles,ARG,male,1988-05-05,1.85,90.0,rugby sevens,0,0,0 -92791561,Juan Pablo Fernandez,ARG,male,1988-09-30,1.92,86.0,handball,0,0,0 -585918319,Juan Pablo Romero,MEX,male,1990-01-30,1.8,,boxing,0,0,0 -59463707,Juan Peralta Gascon,ESP,male,1990-05-17,1.94,93.0,cycling,0,0,0 -388064286,Juan Postigos,PER,male,1989-05-13,1.6,60.0,judo,0,0,0 -118278910,Juan Quintero,COL,male,1995-03-23,1.83,76.0,football,0,0,0 -935796461,Juan Ramon Virgen Pulido,MEX,male,1987-04-09,1.97,96.0,volleyball,0,0,0 -335899694,Juan Saladino,ARG,male,1987-09-28,1.74,73.0,hockey,1,0,0 -566554621,Juan Vivaldi,ARG,male,1979-07-17,1.8,80.0,hockey,1,0,0 -808940866,Juan de Jongh,RSA,male,1988-04-15,1.75,87.0,rugby sevens,0,0,1 -819205568,Juan de la Fuente,ARG,male,1976-08-15,1.81,74.0,sailing,0,0,0 -394965054,Juan-Carlos Navarro,ESP,male,1980-06-13,1.93,82.0,basketball,0,0,1 -711645513,Juan-Sebastian Cabal,COL,male,1986-04-25,1.85,82.0,tennis,0,0,0 -232537338,Juander Santos,DOM,male,1995-05-07,1.71,58.0,athletics,0,0,0 -704786896,Juciely Cristina Barreto,BRA,female,1980-12-18,1.83,72.0,volleyball,0,0,0 -42046189,Judit Ignacio Sorribes,ESP,female,1994-03-18,1.65,58.0,aquatics,0,0,0 -581440046,Judith Forca Ariza,ESP,female,1996-06-07,1.73,70.0,aquatics,0,0,0 -264395591,Judith Mbougnade,CAF,female,1998-07-11,1.5,,boxing,0,0,0 -979044606,Judith Pietersen,NED,female,1989-07-03,1.87,73.0,volleyball,0,0,0 -888955559,Judy Reynolds,IRL,female,1981-06-11,1.58,58.0,equestrian,0,0,0 -381072761,Juho Reinvall,FIN,male,1988-08-24,1.66,60.0,judo,0,0,0 -498802525,Jules Bessan,BEN,male,1979-04-14,1.83,85.0,aquatics,0,0,0 -507759754,Julia Beljajeva,EST,female,1992-07-21,1.76,70.0,fencing,0,0,0 -78792839,Julia Catherine Vincent,RSA,female,1994-08-13,1.54,57.0,aquatics,0,0,0 -409510063,Julia Edward,NZL,female,1991-02-20,1.66,57.0,rowing,0,0,0 -373007150,Julia Figueroa,ESP,female,1991-04-07,1.5,48.0,judo,0,0,0 -851367696,Julia Fischer,GER,female,1990-04-01,1.92,98.0,athletics,0,0,0 -881992373,Julia Gomes,ARG,female,1992-04-30,1.65,60.0,hockey,0,0,0 -354743290,Julia Hassler,LIE,female,1993-02-27,1.77,73.0,aquatics,0,0,0 -195497456,Julia Hauser,AUT,female,1994-02-21,1.63,48.0,triathlon,0,0,0 -919533844,Julia Krajewski,GER,female,1988-10-22,1.68,65.0,equestrian,0,1,0 -94438177,Julia Lier,GER,female,1991-11-11,1.83,78.0,rowing,1,0,0 -843674273,Julia Muller,GER,female,1985-12-10,1.7,71.0,hockey,0,0,1 -814464830,Julia Pons,ESP,female,1994-07-27,1.65,61.0,hockey,0,0,0 -594049927,Julia Reinprecht,USA,female,1991-07-12,1.61,55.0,hockey,0,0,0 -257956193,Julia Sarda,BRA,female,1982-12-01,1.74,64.0,rugby sevens,0,0,0 -292662070,Julia Sebastian,ARG,female,1993-11-23,1.78,70.0,aquatics,0,0,0 -791900499,Julia Stavickaja,GER,female,1997-12-03,1.67,48.0,gymnastics,0,0,0 -716093130,Julia Takacs,ESP,female,1989-06-29,1.71,53.0,athletics,0,0,0 -22509268,Julia Vasconcelos dos Santos,BRA,female,1992-06-15,1.7,57.0,taekwondo,0,0,0 -396350226,Julian Alaphilippe,FRA,male,1992-06-11,1.73,62.0,cycling,0,0,0 -389816250,Julian Ayala,MEX,male,1992-03-06,1.71,80.0,fencing,0,0,0 -273158116,Julian Brandt,GER,male,1996-05-02,1.85,82.0,football,0,1,0 -288174300,Julian Fletcher,BER,male,1990-10-08,1.84,79.0,aquatics,0,0,0 -915650709,Julian Flugel,GER,male,1986-04-18,1.83,66.0,athletics,0,0,0 -118960924,Julian Jrummi Walsh,JPN,male,1996-09-18,1.75,75.0,athletics,0,0,0 -24583837,Julian Justus,GER,male,1988-01-29,1.77,76.0,shooting,0,0,0 -627630800,Julian Matthews,NZL,male,1988-07-21,1.84,70.0,athletics,0,0,0 -104075617,Julian Reus,GER,male,1988-04-29,1.76,76.0,athletics,0,0,0 -479350989,Julian Weber,GER,male,1994-08-29,1.91,98.0,athletics,0,0,0 -800712406,Juliana Esteves,BRA,female,1984-01-27,1.77,73.0,rugby sevens,0,0,0 -550164775,Juliana Gaviria Rendon,COL,female,1991-03-31,1.65,58.0,cycling,0,0,0 -633577633,Juliana Jose Machado,ANG,female,1994-11-06,1.75,60.0,handball,0,0,0 -562282904,Juliana Paula dos Santos,BRA,female,1983-07-12,1.65,50.0,athletics,0,0,0 -101862484,Juliana Veloso,BRA,female,1980-12-22,1.6,62.0,aquatics,0,0,0 -728944807,Juliane Rasmussen,DEN,female,1979-02-17,1.73,61.0,rowing,0,0,0 -966424858,Julianna Miskolczi,HUN,female,1983-11-22,1.56,49.0,shooting,0,0,0 -937877240,Juliano Fiori,BRA,male,1985-06-27,1.91,107.0,rugby sevens,0,0,0 -338982011,Juliao Neto,BRA,male,1981-08-16,1.65,,boxing,0,0,0 -271404469,Julie Brougham,NZL,female,1954-05-20,1.57,48.0,equestrian,0,0,0 -923461973,Julie Johnston,USA,female,1992-04-06,1.7,61.0,football,0,0,0 -433957819,Julie Kepp Jensen,DEN,female,2000-01-03,1.77,65.0,aquatics,0,0,0 -662203262,Julie Meynen,LUX,female,1997-08-15,1.7,63.0,aquatics,0,0,0 -303814544,Julien Absalon,FRA,male,1980-08-16,1.8,68.0,cycling,0,0,0 -60020354,Julien Bahain,CAN,male,1986-04-20,1.9,93.0,rowing,0,0,0 -712150971,Julien Candelon,FRA,male,1980-07-08,1.7,80.0,rugby sevens,0,0,0 -867157773,Julien Gobaux,FRA,male,1990-12-11,1.67,62.0,gymnastics,0,0,0 -774410191,Julien Quesne,FRA,male,1980-08-16,1.85,88.0,golf,0,0,0 -757315970,Julien Watrin,BEL,male,1992-06-27,1.89,83.0,athletics,0,0,0 -962879306,Julien d'Ortoli,FRA,male,1983-10-07,1.8,75.0,sailing,0,0,0 -950043572,Juliet Chekwel,UGA,female,1990-03-25,,,athletics,0,0,0 -307667762,Juliet Itoya,ESP,female,1986-08-17,1.69,58.0,athletics,0,0,0 -149975684,Julieta Constanza Lazcano,ARG,female,1989-06-25,1.9,74.0,volleyball,0,0,0 -569399978,Julieta Granada,PAR,female,1986-11-17,1.6,58.0,golf,0,0,0 -640084413,Julieta Toledo,MEX,female,1997-05-24,1.66,66.0,fencing,0,0,0 -799627712,Juliette Ramel,SWE,female,1987-04-12,1.71,55.0,equestrian,0,0,0 -376593072,Julio Almeida,BRA,male,1969-09-23,1.8,90.0,shooting,0,0,0 -578772600,Julio Alsogaray,ARG,male,1980-04-11,1.8,81.0,sailing,0,0,0 -747521379,Julio Cesar Acosta Gonzalez,CHI,male,1987-07-22,1.6,61.0,weightlifting,0,0,0 -333152040,Julio Cesar Castillo,ECU,male,1988-05-10,1.83,,boxing,0,0,0 -549178578,Julio Cesar Iemma Hernandez,VEN,male,1984-07-31,1.68,70.0,shooting,0,0,0 -158409385,Julio Cesar Salamanca Pineda,ESA,male,1989-07-15,1.59,62.0,weightlifting,0,0,0 -356214385,Julio Cesar Salazar,MEX,male,1993-07-08,1.76,60.0,athletics,0,0,0 -429794805,Julio Cesar de Oliveira,BRA,male,1986-02-04,1.85,95.0,athletics,0,0,0 -912637672,Julio Cesar la Cruz,CUB,male,1989-08-11,1.82,,boxing,1,0,0 -293911956,Julio Peralta,CHI,male,1981-09-09,1.89,80.0,tennis,0,0,0 -856955212,Julissa Diez Canseco,PER,female,1989-06-05,1.7,49.0,taekwondo,0,0,0 -52744143,Julius Kuhn,GER,male,1993-04-01,1.98,110.0,handball,0,0,1 -352796479,Julius Yego,KEN,male,1989-01-04,1.75,94.0,athletics,0,1,0 -833182511,Jun Heo,KOR,male,1988-05-31,1.68,64.0,fencing,0,0,0 -990108444,Jun Hoong Cheong,MAS,female,1990-04-16,1.53,46.0,aquatics,0,1,0 -971499639,Jun Mizutani,JPN,male,1989-06-09,1.72,63.0,table tennis,0,1,1 -837661821,Jun Shan,CHN,male,1994-08-07,1.72,,boxing,0,0,0 -328126879,Jun Yang,CHN,female,1988-04-28,1.8,69.0,aquatics,0,0,0 -898311944,Juncheol Kwon,KOR,male,1988-11-16,1.78,76.0,shooting,0,0,0 -13885387,Jungbaik Lee,KOR,male,1986-08-27,1.61,65.0,wrestling,0,0,0 -462538119,Jungeun Seo,KOR,female,1991-12-26,1.68,61.0,hockey,0,0,0 -603653372,Junghwan Kim,KOR,male,1983-09-02,1.78,66.0,fencing,0,0,1 -851472120,Junghye Kwak,KOR,female,1986-11-19,1.64,64.0,shooting,0,0,0 -540684742,Jungsub Shim,KOR,male,1991-02-21,1.68,58.0,athletics,0,0,0 -118478984,Junhong Kim,KOR,male,1990-10-28,1.77,68.0,shooting,0,0,0 -184749002,Junhua Yin,CHN,female,1990-08-27,1.67,,boxing,0,1,0 -239850180,Junsik Yun,KOR,male,1991-08-09,1.66,65.0,wrestling,0,0,0 -645739020,Junxia Yang,CHN,female,1989-05-02,1.69,63.0,judo,0,0,0 -543006659,Junya Hasegawa,JPN,male,1993-12-13,1.8,70.0,aquatics,0,0,0 -390371883,Junya Koga,JPN,male,1987-07-19,1.82,80.0,aquatics,0,0,0 -868084272,Juozas Bernotas,LTU,male,1989-04-23,1.86,80.0,sailing,0,0,0 -954911768,Jur Vrieling,NED,male,1969-07-31,1.86,81.0,equestrian,0,0,0 -546944322,Juraj Tarr,SVK,male,1979-02-18,1.86,88.0,canoe,0,1,0 -992216184,Juraj Tuzinsky,SVK,male,1984-08-24,1.84,73.0,shooting,0,0,0 -917333595,Jure Dolenec,SLO,male,1988-12-06,1.9,93.0,handball,0,0,0 -332222731,Jure Meglic,AZE,male,1984-10-18,1.82,80.0,canoe,0,0,0 -732395162,Jurgen Spiess,GER,male,1984-03-26,1.75,105.0,weightlifting,0,0,0 -957010520,Jurgen Themen,SUR,male,1985-10-26,,,athletics,0,0,0 -730050855,Justin Duff,CAN,male,1988-05-10,2.0,102.0,volleyball,0,0,0 -574761075,Justin Gatlin,USA,male,1982-02-10,1.86,80.0,athletics,0,1,0 -880505445,Justin Geduld,RSA,male,1993-10-01,1.75,78.0,rugby sevens,0,0,1 -440014013,Justin Liu,SIN,male,1991-05-28,1.67,78.0,sailing,0,0,0 -369577883,Justin Rose,GBR,male,1980-07-30,1.93,89.0,golf,1,0,0 -148873377,Justinas Kinderis,LTU,male,1987-05-24,1.84,82.0,modern pentathlon,0,0,0 -203006331,Justine Fedronic,FRA,female,1991-05-11,1.68,54.0,athletics,0,0,0 -768117731,Justine Palframan,RSA,female,1993-11-04,1.71,64.0,athletics,0,0,0 -769813502,Justyna Kaczkowska,POL,female,1997-10-07,1.74,71.0,cycling,0,0,0 -627015913,Justyna Swiety,POL,female,1992-12-03,1.65,57.0,athletics,0,0,0 -571537130,Jutatip Maneephan,THA,female,1988-07-08,1.57,57.0,cycling,0,0,0 -672417750,Jwala Gutta,IND,female,1983-09-07,1.78,85.0,badminton,0,0,0 -947906976,KK Clark,USA,female,1990-06-28,1.88,72.0,aquatics,1,0,0 -47855369,Ka Bian,CHN,female,1993-01-05,1.82,115.0,athletics,0,0,0 -457893226,Ka Long Angus Ng,HKG,male,1994-06-24,1.81,70.0,badminton,0,0,0 -593897941,Ka Long Cheung,HKG,male,1997-06-10,1.81,68.0,fencing,0,0,0 -680900592,Ka Man Lee,HKG,female,1986-11-28,1.68,59.0,rowing,0,0,0 -305478662,Kaan Kigen Ozbilen,TUR,male,1986-01-15,1.78,57.0,athletics,0,0,0 -715141748,Kaarle Tapper,FIN,male,1995-09-19,1.9,84.0,sailing,0,0,0 -256631076,Kabange Mupopo,ZAM,female,1992-09-21,1.7,62.0,athletics,0,0,0 -148757871,Kacper Klich,POL,male,1994-11-12,1.82,78.0,aquatics,0,0,0 -205302143,Kacper Kozlowski,POL,male,1986-12-07,1.77,72.0,athletics,0,0,0 -280331524,Kacper Majchrzak,POL,male,1992-09-22,1.9,87.0,aquatics,0,0,0 -284586329,Kacper Zieminski,POL,male,1990-11-04,1.83,82.0,sailing,0,0,0 -438808296,Kadeisha Buchanan,CAN,female,1995-11-05,1.7,65.0,football,0,0,1 -219399568,Kadidiatou Diani,FRA,female,1995-04-01,1.68,57.0,football,0,0,0 -357724648,Kadra Mohamed Dembil,DJI,female,1997-04-22,1.55,55.0,athletics,0,0,0 -730889224,Kaede Kondo,JPN,female,1991-10-06,1.73,62.0,basketball,0,0,0 -721642129,Kafetien Gomis,FRA,male,1980-03-23,1.85,70.0,athletics,0,0,0 -607051462,Kahena Kunze,BRA,female,1991-03-12,1.72,68.0,sailing,1,0,0 -796984995,Kaho Minagawa,JPN,female,1997-08-20,1.68,49.0,gymnastics,0,0,0 -738530112,Kai Hafner,GER,male,1989-07-10,1.92,95.0,handball,0,0,1 -740563078,Kai Kazmirek,GER,male,1991-01-28,1.9,88.0,athletics,0,0,0 -39749857,Kai Langerfeld,CAN,male,1987-07-05,1.97,100.0,rowing,0,0,0 -619887656,Kai Qin,CHN,male,1986-01-31,1.7,65.0,aquatics,0,0,1 -968160329,Kai Selvon,TTO,female,1992-04-13,1.65,59.0,athletics,0,0,0 -239319244,Kaidi Kivioja,EST,female,1993-02-23,1.76,61.0,triathlon,0,0,0 -924327192,Kailen Sheridan,CAN,female,1995-07-16,1.78,72.0,football,0,0,0 -880036778,Kaio Marcio,BRA,male,1984-10-19,1.77,75.0,aquatics,0,0,0 -319193694,Kairat Yeraliyev,KAZ,male,1990-11-08,1.65,,boxing,0,0,0 -55703816,Kaj Hendriks,NED,male,1987-08-19,1.97,95.0,rowing,0,0,1 -951014219,Kaleigh Gilchrist,USA,female,1992-05-16,1.76,77.0,aquatics,1,0,0 -462211738,Kamal Aldin Mallash,QAT,male,1992-01-01,1.8,80.0,handball,0,0,0 -943342389,Kame Ali,MAD,male,1984-05-21,1.85,84.0,athletics,0,0,0 -324865660,Kameli Soejima,JPN,male,1983-06-01,1.89,94.0,rugby sevens,0,0,0 -608037098,Kami Craig,USA,female,1987-07-21,1.81,88.0,aquatics,1,0,0 -103254143,Kamia Yousufi,AFG,female,1996-05-20,1.65,55.0,athletics,0,0,0 -743593230,Kamil Kuczynski,POL,male,1985-03-23,,,cycling,0,0,0 -981300465,Kamil Syprzak,POL,male,1991-07-23,2.07,118.0,handball,0,0,0 -838362564,Kamila Licwinko,POL,female,1986-03-22,1.84,66.0,athletics,0,0,0 -109033637,Kamilla Rytter juhl,DEN,female,1983-11-23,1.83,71.0,badminton,0,1,0 -123021523,Kamolwan Chanyim,THA,female,1996-01-03,1.75,65.0,sailing,0,0,0 -790596898,Kamongwa Salukombo Makorobondo,COD,male,1988-08-13,,,athletics,0,0,0 -372258149,Kamran Shakhsuvarly,AZE,male,1992-12-06,1.8,,boxing,0,0,1 -388605257,Kana Mitsugi,JPN,female,1992-06-28,1.72,72.0,rugby sevens,0,0,0 -994718425,Kana Nomura,JPN,female,1990-03-23,1.67,57.0,hockey,0,0,0 -346210070,Kanae Yagi,JPN,female,1992-07-16,1.54,52.0,weightlifting,0,0,0 -104327995,Kanae Yamabe,JPN,female,1990-09-22,1.72,108.0,judo,0,0,1 -386777560,Kanak Jha,USA,male,2000-06-19,1.66,51.0,table tennis,0,0,0 -274674975,Kanako Watanabe,JPN,female,1996-11-15,1.67,60.0,aquatics,0,0,0 -160704850,Kanami Nakamaki,JPN,female,1992-06-05,1.69,56.0,aquatics,0,0,1 -772973384,Kanami Tashiro,JPN,female,1991-03-25,1.73,66.0,volleyball,0,0,0 -27065279,Kane Radford,NZL,male,1990-11-02,1.75,79.0,aquatics,0,0,0 -234490151,Kane Russell,NZL,male,1992-04-22,1.76,82.0,hockey,0,0,0 -553259590,Kanika Beckles,GRN,female,1991-10-03,,,athletics,0,0,0 -743776991,Kano Omata,JPN,female,1996-07-24,1.61,48.0,aquatics,0,0,1 -141149492,Kanstantsin Barycheuski,BLR,male,1990-05-29,1.91,87.0,athletics,0,0,0 -791361517,Kanstantsin Siutsou,BLR,male,1982-08-09,1.84,70.0,cycling,0,0,0 -447034461,Kaori Icho,JPN,female,1984-06-13,1.66,61.0,wrestling,1,0,0 -316924793,Kaori Kawanaka,JPN,female,1991-08-03,1.59,51.0,archery,0,0,0 -351978454,Kaori Matsumoto,JPN,female,1987-09-11,1.63,57.0,judo,0,0,1 -429591068,Kapririel Kitson,FSM,male,1993-12-10,1.53,70.0,athletics,0,0,0 -717634951,Kara Chad,CAN,female,1996-01-09,,,equestrian,0,0,0 -30506524,Kara Winger,USA,female,1986-04-10,1.83,83.0,athletics,0,0,0 -788211955,Karabo Sibanda,BOT,male,1998-07-02,,,athletics,0,0,0 -765541612,Karar Ibrahim,IRQ,male,1994-09-19,1.82,74.0,football,0,0,0 -865191792,Karel Lavicky,CZE,male,1985-11-08,1.92,80.0,sailing,0,0,0 -62963470,Karem Achach,MEX,female,1991-02-25,1.69,60.0,aquatics,0,0,0 -786004687,Karem Ben Hnia,TUN,male,1994-11-13,1.65,69.0,weightlifting,0,0,0 -871511200,Karen Bennett,GBR,female,1989-02-05,1.79,75.0,rowing,0,1,0 -137070973,Karen Cope Charles,CRC,female,1985-11-06,1.73,59.0,volleyball,0,0,0 -263091246,Karen Gallardo,CHI,female,1984-03-06,1.75,96.0,athletics,0,0,0 -660233137,Karen Paquin,CAN,female,1987-08-03,1.72,70.0,rugby sevens,0,0,1 -582647371,Karen Riveros,PAR,female,1994-12-04,1.7,66.0,aquatics,0,0,0 -419664403,Karen Tebar,FRA,female,1964-09-19,1.59,57.0,equestrian,0,0,0 -141875290,Karen Torrez,BOL,female,1992-07-29,1.65,59.0,aquatics,0,0,0 -339808080,Kari Aalvik Grimsbo,NOR,female,1985-01-04,1.8,,handball,0,0,1 -550682162,Kariem Hussein,SUI,male,1989-01-04,1.91,76.0,athletics,0,0,0 -306135130,Karien Robbers,NED,female,1993-08-16,1.8,68.0,rowing,0,0,0 -617638592,Karim Elsayed,EGY,male,1995-02-16,1.83,87.0,canoe,0,0,0 -402272259,Karim Elzoghby,EGY,male,1977-02-15,1.78,70.0,equestrian,0,0,0 -282551125,Karim Gharbi,SMR,male,1992-01-05,1.82,100.0,judo,0,0,0 -78441979,Karim Hendawy,EGY,male,1988-05-01,1.88,90.0,handball,0,0,0 -423872774,Karim Laghouag,FRA,male,1975-08-04,1.77,70.0,equestrian,1,0,0 -960874866,Kariman Abuljadayel,KSA,female,1994-05-11,1.8,70.0,athletics,0,0,0 -825653693,Karin Donckers,BEL,female,1971-05-28,1.68,57.0,equestrian,0,0,0 -256420475,Karin Johansson,SWE,female,1986-10-04,1.69,77.0,canoe,0,0,0 -827466551,Karin Knapp,ITA,female,1987-06-28,1.8,72.0,tennis,0,0,0 -727101608,Karin Mey Melis,TUR,female,1983-05-31,1.71,55.0,athletics,0,0,0 -728372437,Karin Schnaase,GER,female,1985-02-14,1.64,56.0,badminton,0,0,0 -255926111,Karina Goricheva,KAZ,female,1993-04-08,1.6,63.0,weightlifting,0,0,1 -84056388,Karina Lipiarska-Palka,POL,female,1987-02-16,1.74,76.0,archery,0,0,0 -402338305,Karina Lykhvar,ISR,female,1998-12-11,1.7,53.0,gymnastics,0,0,0 -326279643,Karina Ocasio,PUR,female,1985-08-01,1.92,76.0,volleyball,0,0,0 -696191181,Karine Icher,FRA,female,1979-01-26,1.72,66.0,golf,0,0,0 -794384409,Karine Thomas,CAN,female,1989-01-14,1.72,53.0,aquatics,0,0,0 -504393481,Karitaake Tewaaki,KIR,female,1997-12-01,1.55,52.0,athletics,0,0,0 -206296331,Karl Robert Saluri,EST,male,1993-08-06,1.78,75.0,athletics,0,0,0 -630306564,Karl Schulze,GER,male,1988-03-05,1.9,100.0,rowing,1,0,0 -318067161,Karl-Martin Rammo,EST,male,1989-06-06,1.9,82.0,sailing,0,0,0 -773611309,Karl-Richard Frey,GER,male,1991-07-11,1.88,103.0,judo,0,0,0 -541116732,Karla Borger,GER,female,1988-11-22,1.79,66.0,volleyball,0,0,0 -510946610,Karma Karma,BHU,female,1990-06-06,1.63,51.0,archery,0,0,0 -736369747,Karol Bielecki,POL,male,1982-01-23,2.02,106.0,handball,0,0,0 -659495321,Karol Hoffmann,POL,male,1989-06-01,1.96,84.0,athletics,0,0,0 -999580644,Karol Klos,POL,male,1989-08-08,2.01,87.0,volleyball,0,0,0 -867135736,Karol Robak,POL,male,1997-08-24,1.86,68.0,taekwondo,0,0,0 -366245862,Karol Zalewski,POL,male,1993-08-07,1.89,86.0,athletics,0,0,0 -206688360,Karol-Ann Canuel,CAN,female,1988-04-18,1.63,51.0,cycling,0,0,0 -144492522,Karolina Koleczek,POL,female,1993-01-15,1.68,53.0,athletics,0,0,0 -855211189,Karolina Naja,POL,female,1990-02-05,1.65,63.0,canoe,0,0,1 -130728600,Karoline Bjerkeli Grovdal,NOR,female,1990-06-14,,,athletics,0,0,0 -387323294,Karoline Lusitania Tatafu,TGA,female,1998-02-20,1.76,70.0,archery,0,0,0 -380236044,Karri McMahon,AUS,female,1992-02-27,1.77,70.0,hockey,0,0,0 -406293602,Karsta Lowe,USA,female,1993-02-02,1.93,75.0,volleyball,0,0,1 -123017629,Karsten Dilla,GER,male,1989-07-17,1.88,82.0,athletics,0,0,0 -338139502,Karsten Forsterling,AUS,male,1980-01-21,1.91,88.0,rowing,0,1,0 -687827213,Karsten Warholm,NOR,male,1996-02-28,,,athletics,0,0,0 -45164432,Kasey Perry-Glass,USA,female,1987-10-12,1.63,58.0,equestrian,0,0,1 -824542494,Kaspar Taimsoo,EST,male,1987-04-30,1.94,95.0,rowing,0,0,1 -807443109,Kasper Joergensen,DEN,male,1985-03-21,1.85,75.0,rowing,0,1,0 -498991577,Kasper Larsen,DEN,male,1993-01-25,1.9,88.0,football,0,0,0 -821953802,Kasper Sondergaard,DEN,male,1981-06-09,1.95,95.0,handball,1,0,0 -375680192,Kassidy Cook,USA,female,1995-05-09,1.63,56.0,aquatics,0,0,0 -745894634,Kasumi Ishikawa,JPN,female,1993-02-23,1.58,51.0,table tennis,0,0,1 -372060903,Katai Yeerlanbieke,CHN,male,1990-07-16,1.69,77.0,wrestling,0,0,0 -621329519,Katarina Beresova,SVK,female,1987-10-10,1.62,49.0,athletics,0,0,0 -963217820,Katarina Bulatovic,MNE,female,1984-11-15,1.86,71.0,handball,0,0,0 -682459058,Katarina Johnson-Thompson,GBR,female,1993-01-09,1.83,68.0,athletics,0,0,0 -776863385,Katarina Listopadova,SVK,female,1993-03-22,1.69,60.0,aquatics,0,0,0 -448575637,Katarina Simonovic,SRB,female,1994-10-25,1.64,64.0,aquatics,0,0,0 -701050744,Katarzyna Baranowska,POL,female,1987-09-13,1.84,72.0,aquatics,0,0,0 -508525256,Katarzyna Grzybowska-Franc,POL,female,1989-04-30,1.7,58.0,table tennis,0,0,0 -821941106,Katarzyna Jurkowska-Kowalska,POL,female,1992-02-18,1.61,50.0,gymnastics,0,0,0 -133150742,Katarzyna Klys,POL,female,1986-04-23,1.74,70.0,judo,0,0,0 -779199386,Katarzyna Kowalska,POL,female,1985-04-07,1.78,55.0,athletics,0,0,0 -632636643,Katarzyna Krawczyk,POL,female,1990-09-06,1.58,53.0,wrestling,0,0,0 -952109931,Katarzyna Niewiadoma,POL,female,1994-09-29,,,cycling,0,0,0 -601530122,Katarzyna Sokolska,POL,female,1993-08-23,1.72,57.0,athletics,0,0,0 -291806865,Katarzyna Trzopek,USA,female,1981-05-06,1.76,74.0,fencing,0,0,0 -609219433,Katarzyna Wilk,POL,female,1992-03-22,1.78,64.0,aquatics,0,0,0 -103732523,Kate Christowitz,RSA,female,1991-03-05,1.84,76.0,rowing,0,0,0 -750498579,Kate Foo kune,MRI,female,1993-03-29,,,badminton,0,0,0 -659941173,Kate French,GBR,female,1991-02-11,1.75,65.0,modern pentathlon,0,0,0 -42745952,Kate Grace,USA,female,1988-10-24,1.73,55.0,athletics,0,0,0 -562453603,Kate O'Brien,CAN,female,1988-07-23,1.68,70.0,cycling,0,0,0 -669429758,Kate Richardson-Walsh,GBR,female,1980-05-09,1.61,66.0,hockey,1,0,0 -845437093,Katelin Snyder,USA,female,1987-08-16,1.63,49.0,rowing,1,0,0 -211784095,Katelyn Falgowski,USA,female,1988-10-23,1.68,62.0,hockey,0,0,0 -673565659,Katerina Cachova,CZE,female,1990-02-26,1.73,63.0,athletics,0,0,0 -425721150,Katerina Kudejova,CZE,female,1990-01-17,1.73,63.0,canoe,0,0,0 -455000082,Katerina Nash,CZE,female,1977-12-09,1.64,52.0,cycling,0,0,0 -823950105,Katerina Nikoloska,MKD,female,1990-12-30,1.63,63.0,judo,0,0,0 -11497887,Katerina Safrankova,CZE,female,1989-06-08,1.93,103.0,athletics,0,0,0 -495858343,Katerine Savard,CAN,female,1993-05-26,1.67,54.0,aquatics,0,0,1 -967587376,Kateryna Derun,UKR,female,1993-09-24,1.67,75.0,athletics,0,0,0 -478782814,Kateryna Sadurska,UKR,female,1992-07-19,1.78,58.0,aquatics,0,0,0 -132167666,Katharina Haecker,AUS,female,1992-07-31,1.72,63.0,judo,0,0,0 -798170261,Katharina Otte,GER,female,1987-05-29,1.7,61.0,hockey,0,0,1 -124647381,Katharine Holmes,USA,female,1993-07-15,1.81,79.0,fencing,0,0,0 -962942201,Katherine Copeland,GBR,female,1990-12-01,1.72,58.0,rowing,0,0,0 -342063929,Katherine Driscoll,GBR,female,1986-03-13,1.57,55.0,gymnastics,0,0,0 -799137715,Katherine Grainger,GBR,female,1975-11-12,1.83,78.0,rowing,0,1,0 -495646589,Katherine Julissa Rodriguez Peguero,DOM,female,1991-12-18,1.86,75.0,taekwondo,0,0,0 -812632627,Katherine Miller,BRA,female,1994-05-09,1.8,74.0,fencing,0,0,0 -110579331,Katherine Plouffe,CAN,female,1992-09-15,1.9,89.0,basketball,0,0,0 -864345352,Katherine Reinprecht,USA,female,1989-11-01,1.63,56.0,hockey,0,0,0 -36697871,Kathleen Baker,USA,female,1997-02-28,1.73,67.0,aquatics,1,1,0 -136505337,Kathleen Bam,USA,female,1988-12-06,1.55,54.0,hockey,0,0,0 -353620509,Kathleen Bertko,USA,female,1983-11-08,1.76,58.0,rowing,0,0,0 -280337491,Kathleen Sharkey,USA,female,1990-04-30,1.63,58.0,hockey,0,0,0 -530798213,Kathrin Hendrich,GER,female,1992-04-06,1.72,60.0,football,0,0,0 -257116773,Kathrin Klaas,GER,female,1984-02-06,1.68,72.0,athletics,0,0,0 -449245678,Kathrin Marchand,GER,female,1990-11-15,1.83,69.0,rowing,0,0,0 -490337534,Kathrin Unterwurzacher,AUT,female,1992-06-05,1.73,63.0,judo,0,0,0 -911364658,Kathryn Johnson,USA,female,1991-10-25,1.78,69.0,rugby sevens,0,0,0 -297106226,Kathryn Mitchell,AUS,female,1982-07-10,1.68,72.0,athletics,0,0,0 -126231469,Kathryn Robinson,CAN,female,1985-08-04,1.79,58.0,equestrian,0,0,0 -181906965,Kathryn Slattery,AUS,female,1993-07-30,1.73,68.0,hockey,0,0,0 -624672038,Kati Tolmoff,EST,female,1983-12-03,1.64,62.0,badminton,0,0,0 -529644435,Katia Belabbas,ALG,female,1996-02-01,1.7,65.0,sailing,0,0,0 -437866225,Katie Archibald,GBR,female,1994-03-12,1.78,70.0,cycling,1,0,0 -85586674,Katie Bowen,NZL,female,1994-04-15,1.72,56.0,football,0,0,0 -56946849,Katie Clark,GBR,female,1994-03-23,1.68,56.0,aquatics,0,0,0 -447673304,Katie Duncan,NZL,female,1988-02-01,1.61,56.0,football,0,0,0 -932382598,Katie Greves,GBR,female,1982-09-02,1.79,71.0,rowing,0,1,0 -960103057,Katie Ledecky,USA,female,1997-03-17,1.83,72.0,aquatics,4,1,0 -624052007,Katie Rae Ebzery,AUS,female,1990-01-08,1.77,70.0,basketball,0,0,0 -810072016,Katie Taylor,IRL,female,1986-07-01,1.62,,boxing,0,0,0 -204377038,Katie Zaferes,USA,female,1989-06-09,1.73,58.0,triathlon,0,0,0 -643664378,Katinka Hosszu,HUN,female,1989-05-03,1.75,68.0,aquatics,3,1,0 -353743276,Katja Steen Salskov-Iversen,DEN,female,1994-08-19,1.73,70.0,sailing,0,0,1 -925144551,Katlin Tammiste,EST,female,1996-04-06,1.69,63.0,sailing,0,0,0 -762051242,Katrien Verstuyft,BEL,female,1982-07-21,1.7,61.0,triathlon,0,0,0 -668148835,Katrin Garfoot,AUS,female,1981-10-08,1.66,56.0,cycling,0,0,0 -775998118,Katrina Gorry,AUS,female,1992-08-13,1.54,54.0,football,0,0,0 -842830190,Katrina Young,USA,female,1992-01-10,1.63,59.0,aquatics,0,0,0 -152567636,Katrine Lunde,NOR,female,1980-03-30,1.81,,handball,0,0,1 -67639427,Katsiaryna Belanovich,BLR,female,1991-10-14,1.73,64.0,athletics,0,0,0 -462750147,Katsiaryna Halkina,BLR,female,1997-02-25,1.67,47.0,gymnastics,0,0,0 -67434341,Katsiaryna Snytsina,BLR,female,1985-09-02,1.88,78.0,basketball,0,0,0 -291078282,Katsumi Nakamura,JPN,male,1994-02-21,1.83,79.0,aquatics,0,0,0 -902484579,Katsuyuki Sakai,JPN,male,1988-09-07,1.72,85.0,rugby sevens,0,0,0 -482962110,Katsuyuki Tanamura,JPN,male,1989-08-03,1.84,84.0,aquatics,0,0,0 -758751258,Katy Marchant,GBR,female,1993-01-30,1.7,72.0,cycling,0,0,1 -28894925,Katy McLean,GBR,female,1985-12-19,1.67,70.0,rugby sevens,0,0,0 -257631088,Katy Sealy,BIZ,female,1990-10-15,1.72,61.0,athletics,0,0,0 -108089223,Kauiza Venancio,BRA,female,1987-06-11,1.64,58.0,athletics,0,0,0 -686041899,Kaur Kivistik,EST,male,1991-04-29,1.92,76.0,athletics,0,0,0 -25466533,Kaveh Mousavi,IRI,male,1985-05-27,1.92,89.0,athletics,0,0,0 -306669405,Kavita Tungar,IND,female,1985-05-05,1.57,45.0,athletics,0,0,0 -706766581,Kawika Shoji,USA,male,1987-11-11,1.9,79.0,volleyball,0,0,1 -55365531,Kaya Adwoa Forson,GHA,female,2002-03-19,,,aquatics,0,0,0 -707077587,Kayla Banwarth,USA,female,1989-01-21,1.78,75.0,volleyball,0,0,1 -204954873,Kayla Harrison,USA,female,1990-07-02,1.73,74.0,judo,1,0,0 -852366261,Kayla Imrie,NZL,female,1992-02-04,1.82,80.0,canoe,0,0,0 -536942057,Kayla McAlister,NZL,female,1988-08-06,1.69,70.0,rugby sevens,0,1,0 -430232213,Kayla Moleschi,CAN,female,1990-10-25,1.59,65.0,rugby sevens,0,0,1 -445589182,Kayla Pratt,NZL,female,1991-05-27,1.79,73.0,rowing,0,0,0 -892601199,Kayla Whitelock,NZL,female,1985-10-30,1.74,72.0,hockey,0,0,0 -279377870,Kaylin Swart,RSA,female,1994-09-30,,,football,0,0,0 -581084844,Kaylyn Kyle,CAN,female,1988-10-06,1.73,62.0,football,0,0,0 -187283498,Kayoko Fukushi,JPN,female,1982-03-25,1.6,47.0,athletics,0,0,0 -250885863,Kayra Sayit,TUR,female,1988-02-13,1.65,90.0,judo,0,0,0 -522045258,Kazuhiro Goya,JPN,male,1993-04-21,1.7,77.0,rugby sevens,0,0,0 -79558057,Kazuki Yazawa,JPN,male,1989-03-04,1.67,65.0,canoe,0,0,0 -717693447,Kazunari Watanabe,JPN,male,1983-08-12,1.76,80.0,cycling,0,0,0 -974913515,Kazushi Hano,JPN,male,1991-06-21,1.85,87.0,rugby sevens,0,0,0 -609247354,Kazushige Kuboki,JPN,male,1989-06-06,1.72,65.0,cycling,0,0,0 -874759036,Kazuto Doi,JPN,male,1992-03-17,1.75,65.0,sailing,0,0,0 -575081974,Kazuya Shiojiri,JPN,male,1996-11-08,1.7,53.0,athletics,0,0,0 -701275596,Kazuyasu Minobe,JPN,male,1987-07-15,1.77,75.0,fencing,0,0,0 -543655093,Kc Fraser,CAN,female,1986-03-29,1.73,68.0,canoe,0,0,0 -391464185,Keagan Dolly,RSA,male,1993-01-22,1.73,67.0,football,0,0,0 -306652910,Keegan Pereira,CAN,male,1991-09-08,1.66,59.0,hockey,0,0,0 -123790061,Keerati Bualong,THA,male,1992-12-06,1.81,82.0,sailing,0,0,0 -779357330,Keesja Gofers,AUS,female,1990-03-16,1.76,64.0,aquatics,0,0,0 -427387220,Kefasi Chitsala,MAW,male,1994-06-24,1.7,63.0,athletics,0,0,0 -953524242,Kei Marumo,JPN,female,1992-03-06,1.6,50.0,aquatics,0,0,1 -330313227,Kei Nishikori,JPN,male,1989-12-29,1.78,70.0,tennis,0,0,1 -364955058,Kei Takase,JPN,male,1988-11-25,1.8,67.0,athletics,0,0,0 -53827184,Keigo Okawa,JPN,male,1990-03-11,1.83,96.0,aquatics,0,0,0 -437883325,Keiko Miyagawa,JPN,female,1986-05-17,1.52,53.0,sailing,0,0,0 -522124135,Keila Costa,BRA,female,1983-02-06,1.7,62.0,athletics,0,0,0 -13083999,Keisuke Nozawa,JPN,male,1991-06-07,1.75,62.0,athletics,0,0,0 -151980843,Keisuke Ushiro,JPN,male,1986-07-24,1.96,95.0,athletics,0,0,0 -127445268,Keith Ferguson,AUS,male,1979-09-07,1.76,78.0,shooting,0,0,0 -450005449,Keith Sanderson,USA,male,1975-02-02,1.83,95.0,shooting,0,0,0 -357855872,Kelita Zupancic,CAN,female,1990-05-09,1.69,70.0,judo,0,0,0 -564720756,Kelley Hurley,USA,female,1988-04-04,1.76,74.0,fencing,0,0,0 -623867004,Kelley O Hara,USA,female,1988-08-04,1.65,54.0,football,0,0,0 -444880457,Kellion Knibb,JAM,female,1993-12-25,1.83,86.0,athletics,0,0,0 -500380678,Kelly Araouzou,GRE,female,1991-03-06,1.69,55.0,aquatics,0,0,0 -230772998,Kelly Brazier,NZL,female,1989-10-28,1.71,70.0,rugby sevens,0,1,0 -828916384,Kelly Catlin,USA,female,1995-11-03,1.68,63.0,cycling,0,1,0 -766396208,Kelly Dulfer,NED,female,1994-03-21,1.85,78.0,handball,0,0,0 -963773248,Kelly Griffin,USA,female,1986-11-07,1.63,68.0,rugby sevens,0,0,0 -690756146,Kelly Jonker,NED,female,1990-05-23,1.59,59.0,hockey,0,1,0 -998437044,Kelly Massey,GBR,female,1985-01-11,1.67,58.0,athletics,0,0,1 -142742348,Kelly Murphy,USA,female,1989-10-20,1.88,79.0,volleyball,0,0,1 -768193146,Kelly Russell,CAN,female,1986-12-07,1.78,83.0,rugby sevens,0,0,1 -857131931,Kelly Santos,BRA,female,1979-11-10,1.92,92.0,basketball,0,0,0 -106147663,Kelly Tan,MAS,female,1993-10-26,1.65,61.0,golf,0,0,0 -715061380,Kelly-Ann Baptiste,TTO,female,1986-10-14,1.67,54.0,athletics,0,0,0 -576431902,Kellys Arias,COL,female,1989-07-03,,150.0,athletics,0,0,0 -257977443,Kelsey Bevan,NZL,female,1990-04-10,1.74,70.0,rowing,0,0,0 -667833997,Kelsey Card,USA,female,1992-08-20,1.78,115.0,athletics,0,0,0 -549733789,Kelsey Kolojejchick,USA,female,1991-10-02,1.61,61.0,hockey,0,0,0 -132170004,Kelsey Robinson,USA,female,1992-06-25,1.88,75.0,volleyball,0,0,1 -70574268,Kelsey Smith,NZL,female,1994-08-11,1.63,58.0,hockey,0,0,0 -963525573,Kelsey Wakefield,AUS,female,1991-06-01,1.78,64.0,aquatics,0,0,0 -149754220,Kelsey-Lee Roberts,AUS,female,1991-09-20,1.75,70.0,athletics,0,0,0 -596257734,Kelsi Worrell,USA,female,1994-07-15,1.81,74.0,aquatics,1,0,0 -355934349,Kelsie Ahbe,CAN,female,1991-07-06,1.7,63.0,athletics,0,0,0 -713350600,Kelvin Cana Infante,VEN,male,1987-08-06,1.73,72.0,fencing,0,0,0 -99564974,Kemal Mesic,BIH,male,1985-08-04,1.96,120.0,athletics,0,0,0 -216203916,Kemar Bailey-Cole,JAM,male,1992-01-10,1.93,84.0,athletics,1,0,0 -762215715,Kemar Hyman,CAY,male,1989-10-11,1.78,74.0,athletics,0,0,0 -336905285,Kemarley Brown,BRN,male,1992-07-20,1.82,85.0,athletics,0,0,0 -122476731,Kemoy Campbell,JAM,male,1991-01-14,1.68,57.0,athletics,0,0,0 -891361844,Ken Sema,SWE,male,1993-09-30,1.8,69.0,football,0,0,0 -217152230,Ken Terauchi,JPN,male,1980-08-07,1.7,70.0,aquatics,0,0,0 -653195457,Ken Wallace,AUS,male,1983-07-26,1.9,90.0,canoe,0,0,1 -410580066,Kendell Williams,USA,female,1995-06-14,1.78,67.0,athletics,0,0,0 -609984968,Kendra Clarke,CAN,female,1996-11-16,1.67,56.0,athletics,0,0,0 -799965606,Kendrick James Farris,USA,male,1986-07-02,1.68,93.0,weightlifting,0,0,0 -960809660,Kenia Lechuga Alanis,MEX,female,1994-06-26,1.62,59.0,rowing,0,0,0 -254007387,Kenia Sinclair,JAM,female,1980-07-14,1.7,58.0,athletics,0,0,0 -436752047,Kenichi Hayakawa,JPN,male,1986-04-05,1.77,80.0,badminton,0,0,0 -990014757,Kenji Fujimitsu,JPN,male,1986-05-01,1.82,69.0,athletics,0,0,0 -570694823,Kenji Kobase,JPN,male,1987-07-31,1.8,80.0,aquatics,0,0,0 -206392869,Kenji Takahashi,JPN,male,1982-08-21,1.86,77.0,sailing,0,0,0 -316685534,Kenki Fukuoka,JPN,male,1992-09-07,1.76,85.0,rugby sevens,0,0,0 -41041819,Kennedy Goss,CAN,female,1996-08-19,1.73,74.0,aquatics,0,0,1 -347348758,Kennedy Katende,UGA,male,1985-03-15,1.76,,boxing,0,0,0 -392095745,Kennedy St Pierre,MRI,male,1992-10-23,1.8,,boxing,0,0,0 -907808928,Kent Farrington,USA,male,1980-12-28,1.73,65.0,equestrian,0,1,0 -936887083,Kenta Kazuno,JPN,male,1985-11-25,1.71,68.0,badminton,0,0,0 -750688405,Kenta Tokunan,JPN,male,1987-08-17,1.83,83.0,fencing,0,0,0 -919960658,Kentaro Sato,JPN,male,1994-11-16,1.74,64.0,athletics,0,0,0 -478863771,Kentin Mahe,FRA,male,1991-05-22,1.86,83.0,handball,0,1,0 -50571429,Kenya Yasuda,JPN,male,1989-03-29,1.82,77.0,aquatics,0,0,0 -948018416,Kenza Dahmani Tifahi,ALG,female,1980-11-18,1.64,54.0,athletics,0,0,0 -206971699,Kenza Dali,FRA,female,1991-07-31,1.65,60.0,football,0,0,0 -694582399,Kenzo Shirai,JPN,male,1996-08-24,1.62,51.0,gymnastics,1,0,1 -519543861,Keren Siebner,ISR,female,1990-06-09,1.8,68.0,aquatics,0,0,0 -976260835,Keri-Anne Payne,GBR,female,1987-12-09,1.77,66.0,aquatics,0,0,0 -423484325,Kerri Gowler,NZL,female,1993-12-18,1.82,76.0,rowing,0,0,0 -499209508,Kerri Walsh Jennings,USA,female,1978-08-15,1.88,72.0,volleyball,0,0,1 -891867859,Kerron Clement,USA,male,1985-10-31,1.88,86.0,athletics,1,0,0 -233041375,Kerry Hore,AUS,female,1981-07-03,1.83,74.0,rowing,0,0,0 -281101041,Kerry O'Flaherty,IRL,female,1981-07-15,1.67,53.0,athletics,0,0,0 -419463510,Kerry Simmonds,USA,female,1989-04-03,1.83,81.0,rowing,1,0,0 -897407929,Kersten Thiele,GER,male,1992-09-29,1.79,75.0,cycling,0,0,0 -199341754,Kerstin Hartmann,GER,female,1988-06-14,1.77,70.0,rowing,0,0,0 -146173723,Keryn McMaster,AUS,female,1993-09-19,1.69,63.0,aquatics,0,0,0 -770832062,Keshorn Walcott,TTO,male,1993-04-02,1.83,90.0,athletics,0,0,1 -416635977,Keston Bledman,TTO,male,1998-03-08,1.8,88.0,athletics,0,0,0 -232848294,Ketija Birzule,LAT,female,1998-10-30,1.64,60.0,sailing,0,0,0 -197595753,Keturah Orji,USA,female,1996-03-05,1.66,61.0,athletics,0,0,0 -833124628,Kevin Alvarez,HON,male,1996-08-03,1.81,76.0,football,0,0,0 -746715808,Kevin Balanta,COL,male,1997-04-28,1.83,77.0,football,0,0,0 -623836898,Kevin Borlee,BEL,male,1988-02-22,1.8,67.0,athletics,0,0,0 -966461542,Kevin Bouly,FRA,male,1981-04-26,1.77,94.0,weightlifting,0,0,0 -262171881,Kevin Campion,FRA,male,1988-05-23,1.83,63.0,athletics,0,0,0 -772532077,Kevin Chavez Banda,AUS,male,1992-07-09,1.7,70.0,aquatics,0,0,0 -905417640,Kevin Cordes,USA,male,1993-08-13,1.96,88.0,aquatics,1,0,0 -665096571,Kevin Cordon,GUA,male,1986-11-28,1.8,80.0,badminton,0,0,0 -936447136,Kevin Crovetto,MON,male,1992-06-10,1.83,81.0,gymnastics,0,0,0 -887138863,Kevin Durant,USA,male,1988-09-29,2.05,104.0,basketball,1,0,0 -947055286,Kevin Lisch,AUS,male,1986-05-16,1.88,89.0,basketball,0,0,0 -985840182,Kevin Lopez,HON,male,1996-02-03,1.7,69.0,football,0,0,0 -631415010,Kevin Lopez,ESP,male,1990-06-12,1.71,56.0,athletics,0,0,0 -385550989,Kevin Mayer,FRA,male,1992-02-10,1.86,77.0,athletics,0,1,0 -73691099,Kevin Menaldo,FRA,male,1992-07-12,1.76,66.0,athletics,0,0,0 -251643521,Kevin Seaward,IRL,male,1983-10-03,1.75,59.0,athletics,0,0,0 -78313591,Kevin Staut,FRA,male,1980-11-15,1.82,70.0,equestrian,1,0,0 -958136860,Kevin Tillie,FRA,male,1990-11-02,2.0,85.0,volleyball,0,0,0 -30949158,Kevin le Roux,FRA,male,1989-05-11,2.09,98.0,volleyball,0,0,0 -650099766,Keyuan Shang,CHN,male,1995-02-04,1.83,79.0,aquatics,0,0,0 -897015482,Khaddi Sagnia,SWE,female,1994-04-20,1.73,64.0,athletics,0,0,0 -260431373,Khader Ghetrich Baqlah,JOR,male,1998-09-15,1.84,80.0,aquatics,0,0,0 -107338569,Khadija Krimi,TUN,female,1995-08-18,1.71,57.0,rowing,0,0,0 -828792898,Khadija Mardi,MAR,female,1991-02-01,1.83,,boxing,0,0,0 -328541816,Khairul Anuar Mohamad,MAS,male,1991-09-22,1.71,75.0,archery,0,0,0 -460049325,Khairulnizam Mohd Afendy,MAS,male,1993-05-27,1.82,81.0,sailing,0,0,0 -176645733,Khaled Alkaabi,UAE,male,1985-06-25,1.68,85.0,shooting,0,0,0 -238249359,Khaled Almudhaf,IOA,male,1978-06-12,1.63,105.0,shooting,0,0,0 -936049605,Khaled Haj Youssef,TUN,male,1989-01-12,1.89,92.0,handball,0,0,0 -594297353,Khaled Houcine,TUN,male,1990-07-19,1.87,90.0,canoe,0,0,0 -659967898,Khalid Assar,EGY,male,1992-12-10,1.98,99.0,table tennis,0,0,0 -436529232,Khalid El Aabidi,MAR,male,1995-09-14,1.78,81.0,weightlifting,0,0,0 -999784977,Khalifa St Fort,TTO,female,1998-02-13,1.65,50.0,athletics,0,0,0 -9175796,Khamica Bingham,CAN,female,1994-06-15,1.6,60.0,athletics,0,0,0 -182346893,Khasan Khalmurzaev,RUS,male,1993-10-09,1.82,81.0,judo,1,0,0 -1610209,Khaterinne Medina,COL,female,1992-10-31,1.62,60.0,rugby sevens,0,0,0 -282117681,Khatuna Narimanidze,GEO,female,1974-02-02,1.75,79.0,archery,0,0,0 -118156581,Khe Wei Woon,MAS,female,1989-03-18,1.74,63.0,badminton,0,0,0 -18654335,Kheira Hamraoui,FRA,female,1990-01-13,1.79,69.0,football,0,0,0 -870781056,Kheta Ram,IND,male,1986-09-20,1.69,53.0,athletics,0,0,0 -813748404,Khetag Goziumov,AZE,male,1983-04-24,1.8,97.0,wrestling,0,1,0 -345063300,Khrystyna Stuy,UKR,female,1988-02-03,1.68,60.0,athletics,0,0,0 -824851938,Khuderbulga Dorjkhand,MGL,male,1992-05-07,1.81,102.0,wrestling,0,0,0 -953232269,Khushbir Kaur,IND,female,1993-07-09,,,athletics,0,0,0 -285632383,Kia Nurse,CAN,female,1996-02-22,1.82,68.0,basketball,0,0,0 -841438155,Kiana Eide,USA,female,1998-09-25,1.61,53.0,gymnastics,0,0,0 -966322568,Kianoush Rostami,IRI,male,1991-07-23,1.77,84.0,weightlifting,1,0,0 -224527761,Kibwe Johnson,USA,male,1981-07-17,1.88,102.0,athletics,0,0,0 -355505466,Kieran Behan,IRL,male,1989-04-19,1.63,65.0,gymnastics,0,0,0 -135963530,Kierra Smith,CAN,female,1994-02-01,1.5,57.0,aquatics,0,0,0 -414269646,Kierre Beckles,BAR,female,1990-05-21,1.76,61.0,athletics,0,0,0 -403554973,Kiichi Harada,JPN,male,1972-11-30,1.75,73.0,equestrian,0,0,0 -795413338,Kiju Park,KOR,female,1990-02-14,1.67,69.0,hockey,0,0,0 -891148925,Kiki Bertens,NED,female,1991-12-10,1.82,78.0,tennis,0,0,0 -612003301,Kiko Yokota,JPN,female,1997-05-11,1.61,44.0,gymnastics,0,0,0 -654132840,Kiley Neushul,USA,female,1993-03-05,1.73,65.0,aquatics,1,0,0 -650765839,Kilian le Blouch,FRA,male,1989-10-07,1.71,70.0,judo,0,0,0 -746145303,Kim Amb,SWE,male,1990-07-31,1.8,87.0,athletics,0,0,0 -146402828,Kim Andersson,SWE,male,1982-08-21,2.0,105.0,handball,0,0,0 -434139197,Kim Bui,GER,female,1989-01-20,1.56,49.0,gymnastics,0,0,0 -245238655,Kim Collins,SKN,male,1976-04-05,1.8,77.0,athletics,0,0,0 -189393338,Kim Conley,USA,female,1986-03-14,1.61,49.0,athletics,0,0,0 -266914710,Kim Gaucher,CAN,female,1984-05-07,1.85,72.0,basketball,0,0,0 -854142388,Kim Mickle,AUS,female,1984-12-28,1.68,70.0,athletics,0,0,0 -759190706,Kim Polling,NED,female,1991-02-08,1.75,70.0,judo,0,0,0 -579217144,Kim Tillie,FRA,male,1988-07-15,2.11,105.0,basketball,0,0,0 -533153330,Kim Tuan Thach,VIE,male,1994-01-15,1.6,56.0,weightlifting,0,0,0 -20980045,Kimberley Brennan,AUS,female,1985-08-09,1.88,74.0,rowing,1,0,0 -403279900,Kimberly Buys,BEL,female,1989-04-23,1.87,75.0,aquatics,0,0,0 -991525559,Kimberly Garcia,PER,female,1993-10-19,1.64,51.0,athletics,0,0,0 -649635243,Kimberly Hill,USA,female,1989-11-30,1.93,72.0,volleyball,0,0,1 -904668387,Kimberly Hyacinthe,CAN,female,1989-03-28,1.79,65.0,athletics,0,0,0 -447699488,Kimberly Rhode,USA,female,1979-07-16,1.63,83.0,shooting,0,0,1 -195712246,Kimberly Williams,JAM,female,1988-11-03,1.78,62.0,athletics,0,0,0 -943752318,Kimia Alizadeh Zenoorin,IRI,female,1998-07-10,1.85,62.0,taekwondo,0,0,1 -145845453,Kimihiko Imamura,JPN,male,1984-02-03,1.8,78.0,sailing,0,0,0 -897050880,Kimiko Raheem,SRI,female,1999-01-28,1.65,51.0,aquatics,0,0,0 -960777579,Kineke Alexander,VIN,female,1986-02-21,1.78,65.0,athletics,0,0,0 -637755383,King Lok Cheung,HKG,male,1991-02-08,1.72,77.0,cycling,0,0,0 -688432611,Kinga Kolosinska,POL,female,1990-06-02,1.79,67.0,volleyball,0,0,0 -240927532,Kingsley Madu,NGR,male,1995-12-12,1.71,,football,0,0,1 -247889491,Kiplangat Sang,KEN,male,1981-04-14,1.75,90.0,judo,0,0,0 -656286014,Kira Stepanova,RUS,female,1993-11-12,,,canoe,0,0,0 -528363849,Kira Toussaint,NED,female,1994-05-22,1.74,72.0,aquatics,0,0,0 -234124358,Kira Walkenhorst,GER,female,1990-11-18,1.85,75.0,volleyball,1,0,0 -889456958,Kiradech Aphibarnrat,THA,male,1989-07-23,1.72,106.0,golf,0,0,0 -106579503,Kirani James,GRN,male,1992-09-01,,66.0,athletics,0,1,0 -288650137,Kirill Denisov,RUS,male,1988-01-25,1.82,90.0,judo,0,0,0 -785590368,Kirill Gerassimenko,KAZ,male,1996-12-18,1.75,64.0,table tennis,0,0,0 -2938886,Kirill Grigoryan,RUS,male,1992-04-02,1.79,88.0,shooting,0,0,1 -228483818,Kirill Lyapunov,RUS,male,1986-03-24,1.8,81.0,canoe,0,0,0 -388644612,Kirill Prigoda,RUS,male,1995-12-29,1.92,88.0,aquatics,0,0,0 -412500956,Kirk Shimmins,IRL,male,1994-06-01,1.75,68.0,hockey,0,0,0 -580208102,Kirsten Flipkens,BEL,female,1986-01-10,1.65,59.0,tennis,0,0,0 -807032440,Kirsten McCANN,RSA,female,1988-08-25,1.7,57.0,rowing,0,0,0 -248407953,Kirsten Pearce,NZL,female,1991-04-10,1.62,58.0,hockey,0,0,0 -704187599,Kirsten Sweetland,CAN,female,1988-09-24,1.63,49.0,triathlon,0,0,0 -907795948,Kirsten Wild,NED,female,1982-10-15,1.78,78.0,cycling,0,0,0 -917790149,Kirsti Lay,CAN,female,1988-04-07,1.73,65.0,cycling,0,0,1 -857038290,Kirstie Elaine Alora,PHI,female,1989-11-25,1.73,67.0,taekwondo,0,0,0 -801571682,Kirstin Dwyer,AUS,female,1989-03-15,1.73,66.0,hockey,0,0,0 -134565248,Kirsty Gilmour,GBR,female,1993-09-21,1.68,,badminton,0,0,0 -825202898,Kirsty Leigh Coventry,ZIM,female,1983-09-16,1.76,64.0,aquatics,0,0,0 -52180375,Kirsty Yallop,NZL,female,1986-11-04,1.65,58.0,football,0,0,0 -279849374,Kit-Ching Yiu,HKG,female,1988-02-20,1.55,45.0,athletics,0,0,0 -51995447,Kitione Taliga,FIJ,male,1993-04-21,1.86,87.0,rugby sevens,1,0,0 -449603562,Kitty King,GBR,female,1982-08-10,1.58,60.0,equestrian,0,0,0 -159029734,Kitty van Male,NED,female,1988-06-05,1.96,63.0,hockey,0,1,0 -308690997,Kivilcim Kaya Salman,TUR,female,1992-03-27,1.66,85.0,athletics,0,0,0 -187677131,Kjetil Borch,NOR,male,1990-02-14,,,rowing,0,0,1 -759388006,Klara Spilkova,CZE,female,1994-12-15,1.68,53.0,golf,0,0,0 -686436937,Klaudia Bres,POL,female,1994-06-22,1.58,52.0,shooting,0,0,0 -184949058,Klaudia Jans-Ignacik,POL,female,1984-09-24,,,tennis,0,0,0 -834172250,Klaudia Konopko,POL,female,1992-02-21,1.7,57.0,athletics,0,0,0 -560449526,Klaus Lange,ARG,male,1995-01-13,1.82,81.0,sailing,0,0,0 -748463150,Klay Thompson,USA,male,1990-02-08,2.0,97.0,basketball,1,0,0 -675747408,Kleber da Silva Ramos,BRA,male,1985-08-24,1.67,65.0,cycling,0,0,0 -952862687,Kleberson Davide,BRA,male,1985-07-20,1.75,69.0,athletics,0,0,0 -189267095,Kodo Nakano,PHI,male,1993-03-08,,81.0,judo,0,0,0 -332048858,Koen Naert,BEL,male,1989-09-03,1.82,66.0,athletics,0,0,0 -608163038,Kohei Uchima,JPN,male,1988-11-08,1.7,60.0,cycling,0,0,0 -199243053,Kohei Uchimura,JPN,male,1989-01-02,1.62,52.0,gymnastics,2,0,0 -86038956,Kohei Yamamoto,JPN,male,1985-08-20,1.82,69.0,cycling,0,0,0 -51425019,Kohei Yamashita,JPN,male,1994-09-06,1.79,69.0,athletics,0,0,0 -656874306,Koichiro Morioka,JPN,male,1985-04-02,1.85,69.0,athletics,0,0,0 -337056974,Koji Takei,JPN,male,1990-07-30,1.76,78.0,aquatics,0,0,0 -497335334,Koji Yamamuro,JPN,male,1989-01-17,1.59,58.0,gymnastics,1,0,0 -196041422,Koki Niwa,JPN,male,1994-10-10,1.62,51.0,table tennis,0,1,0 -355065846,Koki Saito,JPN,male,1989-09-16,1.76,65.0,equestrian,0,0,0 -682697811,Komeil Nemat Ghasemi,IRI,male,1988-02-27,1.86,115.0,wrestling,0,1,0 -974731965,Komronshokh Ustopiriyon,TJK,male,1993-01-07,1.82,90.0,judo,0,0,0 -514884067,Konomi Kai,JPN,female,1993-07-10,1.53,50.0,athletics,0,0,0 -386847658,Konrad Bukowiecki,POL,male,1997-03-17,1.91,138.0,athletics,0,0,0 -40414487,Konrad Czerniak,POL,male,1989-07-11,1.95,87.0,aquatics,0,0,0 -682481814,Konstadinos Baniotis,GRE,male,1986-11-06,2.01,80.0,athletics,0,0,0 -180022140,Konstadinos Douvalidis,GRE,male,1987-03-10,1.81,78.0,athletics,0,0,0 -996631093,Konstadinos Filippidis,GRE,male,1986-11-26,1.87,74.0,athletics,0,0,0 -725033682,Konstantin Semenov,RUS,male,1989-06-09,2.1,101.0,volleyball,0,0,0 -532161398,Konstantinos Flegkas,GRE,male,1988-07-17,1.92,88.0,aquatics,0,0,0 -808549144,Konstantinos Genidounias,GRE,male,1993-05-03,1.83,87.0,aquatics,0,0,0 -578912084,Konstantinos Mourikis,GRE,male,1988-07-11,1.97,109.0,aquatics,0,0,0 -419860088,Konstanze Klosterhalfen,GER,female,1997-02-18,1.74,48.0,athletics,0,0,0 -288100090,Korey Jarvis,CAN,male,1986-10-04,1.88,115.0,wrestling,0,0,0 -409930037,Kosovare Asllani,SWE,female,1989-07-29,1.66,54.0,football,0,1,0 -940864440,Kostyantyn Bakun,RUS,male,1985-03-15,2.04,96.0,volleyball,0,0,0 -794043609,Kosuke Hagino,JPN,male,1994-08-15,1.77,71.0,aquatics,1,1,1 -791705872,Kosuke Nakamura,JPN,male,1995-02-27,1.84,72.0,football,0,0,0 -18488153,Kota Murayama,JPN,male,1993-02-23,1.74,54.0,athletics,0,0,0 -422424765,Kothajit Khadangbam,IND,male,1992-08-17,1.7,63.0,hockey,0,0,0 -791359360,Kotoki Zayasu,JPN,female,1990-01-11,1.59,57.0,volleyball,0,0,0 -892925415,Kotuku Ngawati,AUS,female,1994-06-16,1.65,60.0,aquatics,0,0,0 -759873461,Koutar Boulaid,MAR,female,1989-10-10,1.59,45.0,athletics,0,0,0 -348937822,Kresimir Kozina,CRO,male,1990-06-25,1.97,105.0,handball,0,0,0 -461399217,Krishan Vikas,IND,male,1992-02-10,1.77,,boxing,0,0,0 -936707475,Krista Duchene,CAN,female,1977-01-09,1.67,54.0,athletics,0,0,0 -321162229,Kristel Kobrich,CHI,female,1985-08-09,1.7,60.0,aquatics,0,0,0 -449134352,Kristel Vourna,GRE,female,1992-02-11,1.74,61.0,aquatics,0,0,0 -737728576,Kristen Shaldybin,USA,female,1997-08-08,1.76,56.0,gymnastics,0,0,0 -34438087,Kristi Castlin,USA,female,1988-07-07,1.71,60.0,athletics,0,0,1 -7457125,Kristian Blummenfelt,NOR,male,1994-02-14,,,triathlon,0,0,0 -539326422,Kristian Fris,SRB,male,1984-04-21,1.7,59.0,wrestling,0,0,0 -582900403,Kristian Gkolomeev,GRE,male,1993-07-04,2.02,95.0,aquatics,0,0,0 -512973536,Kristian Ipsen,USA,male,1992-10-20,1.71,72.0,aquatics,0,0,0 -917018494,Kristian Karlsson,SWE,male,1991-08-06,1.83,82.0,table tennis,0,0,0 -978961185,Kristian Ruth,NOR,male,1985-07-23,,,sailing,0,0,0 -407221611,Kristian Thomas,GBR,male,1989-02-14,1.8,78.0,gymnastics,0,0,0 -368830372,Kristian Vasilev,BUL,male,1991-11-18,1.97,97.0,rowing,0,0,0 -213581372,Kristiina Makela,FIN,female,1992-11-20,1.85,68.0,athletics,0,0,0 -884195361,Kristijan Durasek,CRO,male,1987-07-26,1.7,57.0,cycling,0,0,0 -791417527,Kristin Armstrong,USA,female,1973-08-11,1.73,58.0,cycling,1,0,0 -664462472,Kristin Gierisch,GER,female,1990-08-20,1.78,61.0,athletics,0,0,0 -981663977,Kristina Broring-Sprehe,GER,female,1986-10-28,1.68,54.0,equestrian,1,0,1 -254900911,Kristina Cook,GBR,female,1970-08-31,1.78,68.0,equestrian,0,0,0 -654847993,Kristina Gavnholt,CZE,female,1988-09-12,1.77,73.0,badminton,0,0,0 -66901895,Kristina Ilinykh,RUS,female,1994-11-27,1.73,56.0,aquatics,0,0,0 -656658323,Kristina Kuusk,EST,female,1985-11-16,1.8,74.0,fencing,0,0,0 -819640840,Kristina Mladenovic,FRA,female,1993-05-14,1.84,66.0,tennis,0,0,0 -312030242,Kristina Pronzhenko,TJK,female,1988-12-10,1.72,60.0,athletics,0,0,0 -928141398,Kristina Reynolds,GER,female,1984-02-18,1.81,71.0,hockey,0,0,1 -992483692,Kristina Valjas,CAN,female,1987-06-02,1.88,68.0,volleyball,0,0,0 -108583641,Kristina Vogel,GER,female,1990-11-10,1.6,66.0,cycling,1,0,1 -763893829,Kristine Esebua,GEO,female,1985-03-19,1.62,69.0,archery,0,0,0 -576779994,Kristof Rasovszky,HUN,male,1997-03-27,1.9,84.0,aquatics,0,0,0 -957396077,Kristoffer Brun,NOR,male,1988-04-07,,,rowing,0,0,1 -34412448,Kristy Oatley,AUS,female,1978-07-18,1.7,56.0,equestrian,0,0,0 -353904175,Kristyna Fleissnerova,CZE,female,1992-08-18,1.73,64.0,rowing,0,0,0 -11420262,Krisztian Manhercz,HUN,male,1997-02-06,1.91,91.0,aquatics,0,0,0 -238991769,Krisztian Pars,HUN,male,1982-02-18,1.88,116.0,athletics,0,0,0 -123300327,Krisztian Takacs,HUN,male,1985-12-30,1.86,84.0,aquatics,0,0,0 -667329045,Krisztian Toth,HUN,male,1994-05-01,1.75,93.0,judo,0,0,0 -540029142,Krisztina Fazekas-Zur,HUN,female,1980-08-01,1.72,64.0,canoe,1,0,0 -401589716,Krisztina Garda,HUN,female,1994-07-16,1.7,76.0,aquatics,0,0,0 -211842063,Krisztina Papp,HUN,female,1982-12-17,1.73,53.0,athletics,0,0,0 -267615621,Krunoslav Simon,CRO,male,1985-06-24,1.97,100.0,basketball,0,0,0 -984489399,Krystian Aranowski,POL,male,1988-04-11,1.98,102.0,rowing,0,0,0 -792210881,Krystian Zalewski,POL,male,1989-04-11,1.85,68.0,athletics,0,0,0 -890413525,Krzysztof Lijewski,POL,male,1983-07-07,1.99,99.0,handball,0,0,0 -840441745,Krzysztof Maksel,POL,male,1991-07-04,1.81,80.0,cycling,0,0,0 -985218732,Ksenia Chibisova,RUS,female,1988-07-13,1.86,78.0,judo,0,0,0 -500616803,Ksenia Perova,RUS,female,1989-02-08,1.69,77.0,archery,0,1,0 -117683465,Ksenija Balta,EST,female,1986-11-01,1.69,51.0,athletics,0,0,0 -292420209,Kseniya Moustafaeva,FRA,female,1995-06-08,1.63,50.0,gymnastics,0,0,0 -401281343,Kseniya Pantelyeyeva,UKR,female,1994-05-11,1.68,75.0,fencing,0,0,0 -664487894,Kseniya Sydorenko,UKR,female,1986-07-02,1.78,57.0,aquatics,0,0,0 -81024511,Kudakwashe Basopo,ZIM,female,1990-07-18,1.62,60.0,football,0,0,0 -370696968,Kuk Hyang Kim,PRK,female,1993-04-20,1.7,100.0,weightlifting,0,1,0 -703248016,Kuk Hyang Kim,PRK,female,1999-04-04,1.49,39.0,aquatics,0,0,0 -358078214,Kuk Hyon Hong,PRK,male,1990-07-01,1.79,73.0,judo,0,0,0 -856229639,Kukyoung Kim,KOR,male,1991-04-19,1.76,73.0,athletics,0,0,0 -576883439,Kum-Ok Kim,PRK,female,1988-12-09,1.61,48.0,athletics,0,0,0 -405931977,Kumari Babita,IND,female,1989-11-20,1.65,52.0,wrestling,0,0,0 -179174658,Kumiko Okada,JPN,female,1991-10-17,1.58,47.0,athletics,0,0,0 -434716093,Kun-Pi Yang,TPE,male,1998-08-11,1.86,107.0,shooting,0,0,0 -331317766,Kunathip Yea-On,THA,male,1995-08-18,1.93,148.0,judo,0,0,0 -340840336,Kunhu Muhammed Puthanpurakkal,IND,male,1987-03-05,1.72,70.0,athletics,0,0,0 -13588611,Kuniaki Takizaki,CAM,male,1977-08-08,1.51,55.0,athletics,0,0,0 -886640811,Kurt Couto,MOZ,male,1985-05-14,1.8,74.0,athletics,0,0,0 -574563035,Kurt Felix,GRN,male,1988-07-04,1.82,76.0,athletics,0,0,0 -146480210,Kurtis Marschall,AUS,male,1997-04-25,1.87,78.0,athletics,0,0,0 -156130854,Kurumi Yoshida,JPN,female,1991-12-01,1.67,59.0,aquatics,0,0,1 -824038451,Kwagga Smith,RSA,male,1993-06-11,1.8,90.0,rugby sevens,0,0,1 -80433240,Kwan Kit Ho,HKG,male,1997-04-20,1.78,66.0,table tennis,0,0,0 -718483245,Kwandakwensizwa Mngonyama,RSA,male,1993-09-25,1.84,79.0,football,0,0,0 -152318195,Kyah Simon,AUS,female,1991-06-25,1.64,61.0,football,0,0,0 -887479109,Kyerim Lee,KOR,female,1990-08-31,1.58,50.0,shooting,0,0,0 -676973733,Kyle Brown,RSA,male,1987-02-06,1.82,92.0,rugby sevens,0,0,1 -885688636,Kyle Chalmers,AUS,male,1998-06-25,1.93,90.0,aquatics,1,0,2 -341835179,Kyle Clemons,USA,male,1990-12-27,1.81,72.0,athletics,1,0,0 -245877079,Kyle Dodd,RSA,male,1994-02-11,1.78,80.0,cycling,0,0,0 -897784869,Kyle Edmund,GBR,male,1995-01-08,1.89,81.0,tennis,0,0,0 -599766410,Kyle Evans,GBR,male,1993-09-26,1.8,81.0,cycling,0,0,0 -334173080,Kyle Frederick Snyder,USA,male,1995-11-20,1.81,102.0,wrestling,1,0,0 -244160480,Kyle Good,IRL,male,1991-12-10,1.83,88.0,hockey,0,0,0 -141935257,Kyle Greaux,TTO,male,1988-04-26,,,athletics,0,0,0 -57806527,Kyle Lowry,USA,male,1986-03-25,1.83,93.0,basketball,1,0,0 -367373878,Kyle Reyes,CAN,male,1993-10-10,1.83,99.0,judo,0,0,0 -188768661,Kyle Sean Micallef,MLT,male,1987-01-08,1.77,85.0,weightlifting,0,0,0 -78377242,Kyle Stolk,NED,male,1996-06-28,1.86,84.0,aquatics,0,0,0 -714395928,Kylie Masse,CAN,female,1996-01-18,1.7,61.0,aquatics,0,0,1 -107792319,Kylie Rei Dickson,BLR,female,1999-02-12,1.6,50.0,gymnastics,0,0,0 -654626226,Kynan Chenai,IND,male,1991-01-29,2.0,85.0,shooting,0,0,0 -997852630,Kyong Il Yang,PRK,male,1989-08-07,1.58,57.0,wrestling,0,0,0 -263910459,Kyong Sol,PRK,female,1990-06-08,1.63,78.0,judo,0,0,0 -417582763,Kyoungdoo Park,KOR,male,1984-08-03,1.77,77.0,fencing,0,0,0 -594770931,Kyriakos Ioannou,CYP,male,1984-07-26,1.96,74.0,athletics,0,0,0 -944821832,Kyriakos Pontikeas,GRE,male,1991-05-09,1.92,84.0,aquatics,0,0,0 -212656744,Kyrie Irving,USA,male,1992-03-23,1.9,87.0,basketball,1,0,0 -797859111,Kyubaek Choi,KOR,male,1994-01-23,1.88,77.0,football,0,0,0 -136817398,Kyung Eun Jung,KOR,female,1990-03-20,1.72,59.0,badminton,0,0,1 -461106431,Kyunghee Lim,KOR,female,1982-11-16,1.63,43.0,athletics,0,0,0 -819912962,Kyuwoong Choi,KOR,male,1990-05-28,1.81,75.0,aquatics,0,0,0 -42152477,L.j. van Zyl,RSA,male,1985-07-20,1.82,79.0,athletics,0,0,0 -397906990,Lachlan Tame,AUS,male,1988-11-14,1.76,80.0,canoe,0,0,1 -202060603,Ladislav Skantar,SVK,male,1983-02-11,1.89,80.0,canoe,1,0,0 -429703737,Lady Andrade,COL,female,1992-01-10,1.64,60.0,football,0,0,0 -380147709,Lady Ruano,COL,female,1981-03-05,1.57,52.0,table tennis,0,0,0 -788128970,Laenly Phoutthavong,LAO,female,1996-06-04,1.69,55.0,athletics,0,0,0 -145889655,Laerke Buhl-Hansen,DEN,female,1992-03-30,1.67,56.0,sailing,0,0,0 -713520170,Laetisha Scanlan,AUS,female,1990-04-13,1.63,61.0,shooting,0,0,0 -796815573,Laetitia Beck,ISR,female,1992-02-05,1.75,61.0,golf,0,0,0 -1610593,Laetitia Crescence Moma Bassoko,CMR,female,1993-10-09,1.84,80.0,volleyball,0,0,0 -556419347,Laetitia Kamba,FRA,female,1987-01-10,1.87,78.0,basketball,0,0,0 -624409158,Laetitia Payet,FRA,female,1985-10-02,1.5,48.0,judo,0,0,0 -283796525,Laetitia Philippe,FRA,female,1991-04-30,1.73,61.0,football,0,0,0 -451101103,Laia Palau,ESP,female,1979-09-10,1.81,69.0,basketball,0,1,0 -463504304,Lais Nunes de Oliveira,BRA,female,1992-11-03,1.65,65.0,wrestling,0,0,0 -707312914,Lala Wane,SEN,female,1989-07-15,1.79,78.0,basketball,0,0,0 -598281407,Lalit Mathur,IND,male,1994-12-18,,,athletics,0,0,0 -340454427,Lalita Shivaji Babar,IND,female,1989-06-02,1.66,50.0,athletics,0,0,0 -54471403,Lalita Yauhleuskaya,AUS,female,1963-12-31,1.58,64.0,shooting,0,0,0 -316330881,Lalonde Gordon,TTO,male,1988-11-25,1.79,83.0,athletics,0,0,0 -486290067,Lanece Clarke,BAH,female,1987-11-04,,,athletics,0,0,0 -307336565,Lani Belcher,GBR,female,1989-06-10,1.69,65.0,canoe,0,0,0 -789007378,Lani Cabrera,BAR,female,1993-04-22,1.75,66.0,aquatics,0,0,0 -688222254,Lanni Marchant,CAN,female,1984-04-11,1.55,48.0,athletics,0,0,0 -165918129,Lara Butler,CAY,female,1994-10-02,1.72,60.0,aquatics,0,0,0 -400916964,Lara Gonzalez,ESP,female,1992-02-22,1.84,74.0,handball,0,0,0 -329123221,Lara Grangeon,FRA,female,1991-09-21,1.73,60.0,aquatics,0,0,0 -303756997,Lara Griffith,GBR,female,1988-11-14,1.76,62.0,equestrian,0,0,0 -714521221,Lara Hoffmann,GER,female,1991-03-25,1.73,68.0,athletics,0,0,0 -268747617,Lara Sanders,TUR,female,1986-09-11,1.91,77.0,basketball,0,0,0 -485160577,Lara Teixeira,BRA,female,1987-11-26,1.67,59.0,aquatics,0,0,0 -251433937,Lara Vadlau,AUT,female,1994-03-29,1.65,56.0,sailing,0,0,0 -322415539,Laraiba Seibou,BEN,female,2000-12-06,1.73,63.0,aquatics,0,0,0 -16230111,Larbi Bourrada,ALG,male,1988-05-10,1.88,88.0,athletics,0,0,0 -609688727,Larisa Ceric,BIH,female,1991-01-26,1.76,110.0,judo,0,0,0 -838253467,Larissa Araujo,BRA,female,1992-07-01,1.67,62.0,handball,0,0,0 -842092433,Larissa Crummer,AUS,female,1996-01-10,1.78,70.0,football,0,0,0 -564844336,Larissa Franca Maestrini,BRA,female,1982-04-14,1.74,70.0,volleyball,0,0,0 -923732002,Larissa Oliveira,BRA,female,1993-02-16,1.69,57.0,aquatics,0,0,0 -768025423,Larrissa Miller,AUS,female,1992-07-12,1.59,50.0,gymnastics,0,0,0 -715498421,Lars Bender,GER,male,1989-04-27,1.84,77.0,football,0,1,0 -564584099,Lars Fluggen,GER,male,1990-05-24,1.92,83.0,volleyball,0,0,0 -791318566,Lars Forster,SUI,male,1993-08-01,1.77,68.0,cycling,0,0,0 -857905937,Lars Petter Nordhaug,NOR,male,1984-05-14,,,cycling,0,0,0 -639300643,Lars Wichert,GER,male,1986-11-28,1.86,73.0,rowing,0,0,0 -850256654,Lasha Shavdatuashvili,GEO,male,1992-01-31,1.7,75.0,judo,0,0,1 -592699944,Lasha Talakhadze,GEO,male,1993-10-02,1.97,157.0,weightlifting,1,0,0 -404444174,Lasha Torgvaidze,GEO,male,1993-05-26,1.84,73.0,athletics,0,0,0 -920627938,Lashawn Merritt,USA,male,1986-06-27,1.91,86.0,athletics,1,0,1 -798007695,Lasma Liepa,TUR,female,1988-07-04,1.8,77.0,canoe,0,0,0 -345588226,Lasse Norman Hansen,DEN,male,1992-02-11,1.8,73.0,cycling,0,0,2 -384124391,Lasse Svan,DEN,male,1983-08-31,1.85,85.0,handball,1,0,0 -533708221,Lasse Vibe,DEN,male,1987-02-22,1.84,75.0,football,0,0,0 -327942821,Laszlo Cseh,HUN,male,1985-12-03,1.88,84.0,aquatics,0,1,0 -27973136,Laszlo Csoknyai,HUN,male,1987-10-25,1.72,83.0,judo,0,0,0 -932218677,Latario Collie-Minns,BAH,male,1994-03-10,1.73,64.0,athletics,0,0,0 -486930749,Laura Alleway,AUS,female,1989-11-28,1.78,74.0,football,0,0,0 -70272751,Laura Asadauskaite,LTU,female,1984-02-28,1.6,49.0,modern pentathlon,0,0,0 -151499815,Laura Auge,FRA,female,1992-01-17,1.75,60.0,aquatics,0,0,0 -247206821,Laura Benkarth,GER,female,1992-10-14,1.73,68.0,football,1,0,0 -529217667,Laura Brown,CAN,female,1986-11-27,1.67,61.0,cycling,0,0,1 -284937844,Laura Chiper,ROU,female,1989-08-21,1.73,65.0,handball,0,0,0 -456601968,Laura Dijkema,NED,female,1990-02-18,1.84,70.0,volleyball,0,0,0 -880019885,Laura Ester Ramos,ESP,female,1990-01-22,1.7,58.0,aquatics,0,0,0 -652201714,Laura Gil,ESP,female,1992-04-24,1.91,78.0,basketball,0,1,0 -996823500,Laura Giombini,ITA,female,1989-01-04,1.83,,volleyball,0,0,0 -69085100,Laura Glauser,FRA,female,1993-08-20,1.78,65.0,handball,0,1,0 -829875287,Laura Gomez,ESP,female,1984-04-19,1.6,52.0,judo,0,0,0 -768401050,Laura Gonzalez,COL,female,1993-03-08,1.61,61.0,rugby sevens,0,0,0 -225614709,Laura Graves,USA,female,1987-07-22,1.73,54.0,equestrian,0,0,1 -50773758,Laura Hodges,AUS,female,1983-12-13,1.91,78.0,basketball,0,0,0 -77033753,Laura Ikauniece-Admidina,LAT,female,1992-05-31,1.79,63.0,athletics,0,0,0 -774958820,Laura Kraut,USA,female,1965-11-14,1.71,56.0,equestrian,0,0,0 -940578018,Laura Lindemann,GER,female,1996-06-26,1.68,57.0,triathlon,0,0,0 -148490223,Laura Lopez Ventosa,ESP,female,1988-01-13,1.71,65.0,aquatics,0,0,0 -363628190,Laura Ludwig,GER,female,1986-01-13,1.81,70.0,volleyball,1,0,0 -360874773,Laura Marino,FRA,female,1993-07-02,1.68,55.0,aquatics,0,0,0 -901305776,Laura Milani,ITA,female,1984-09-30,1.68,57.0,rowing,0,0,0 -624808656,Laura Muir,GBR,female,1993-05-09,1.61,49.0,athletics,0,0,0 -468145639,Laura Muller,GER,female,1995-12-11,1.72,58.0,athletics,0,0,0 -612731499,Laura Nicholls,ESP,female,1989-02-26,1.9,90.0,basketball,0,1,0 -837136545,Laura Nunnink,NED,female,1995-01-26,1.72,64.0,hockey,0,0,0 -979004062,Laura Nurmsalu,EST,female,1994-06-01,1.7,70.0,archery,0,0,0 -381041222,Laura Oprea,ROU,female,1994-02-19,1.86,75.0,rowing,0,0,1 -278075426,Laura Quevedo,ESP,female,1996-04-15,1.85,74.0,basketball,0,1,0 -885866639,Laura Salles Lopez,AND,female,1986-02-15,1.69,63.0,judo,0,0,0 -151961559,Laura Sarosi,HUN,female,1992-11-11,1.67,57.0,badminton,0,0,0 -949858669,Laura Siegemund,GER,female,1988-03-04,1.68,62.0,tennis,0,0,0 -250953403,Laura Smulders,NED,female,1993-12-09,1.7,73.0,cycling,0,0,0 -603343722,Laura Teani,ITA,female,1991-03-13,1.75,75.0,aquatics,0,1,0 -371337915,Laura Trott,GBR,female,1992-04-24,1.64,56.0,cycling,2,0,0 -192919301,Laura Unsworth,GBR,female,1988-03-08,1.52,55.0,hockey,1,0,0 -528746135,Laura Vargas Koch,GER,female,1990-06-29,1.73,70.0,judo,0,0,1 -776443567,Laura Waem,BEL,female,1997-08-05,1.6,54.0,gymnastics,0,0,0 -70958554,Laura Weightman,GBR,female,1991-07-01,1.72,56.0,athletics,0,0,0 -825506439,Laura Whittle,GBR,female,1985-06-27,1.75,59.0,athletics,0,0,0 -259253637,Laura Zeng,USA,female,1999-10-14,1.61,43.0,gymnastics,0,0,0 -435786461,Laura de Witte,NED,female,1995-08-07,1.73,61.0,athletics,0,0,0 -264136986,Laura van der Heijden,NED,female,1990-06-27,1.72,68.0,handball,0,0,0 -612897839,Lauren Billys,PUR,female,1988-05-14,1.61,59.0,equestrian,0,0,0 -829889004,Lauren Boyle,NZL,female,1987-12-14,1.83,67.0,aquatics,0,0,0 -792471719,Lauren Crandall,USA,female,1985-03-17,1.61,57.0,hockey,0,0,0 -695489909,Lauren Doyle,USA,female,1991-02-23,1.68,68.0,rugby sevens,0,0,0 -208831332,Lauren Ellis,NZL,female,1989-04-19,1.66,64.0,cycling,0,0,0 -933903933,Lauren Fendrick,USA,female,1982-03-20,1.88,75.0,volleyball,0,0,0 -766991740,Lauren Hernandez,USA,female,2000-06-09,1.53,48.0,gymnastics,1,1,0 -583498432,Lauren Kieffer,USA,female,1987-06-06,1.78,68.0,equestrian,0,0,0 -88108486,Lauren Rembi,FRA,female,1992-03-09,1.78,60.0,fencing,0,0,0 -766647760,Lauren Reynolds,AUS,female,1991-06-25,1.76,69.0,cycling,0,0,0 -169582119,Lauren Schmetterling,USA,female,1988-08-03,1.81,77.0,rowing,1,0,0 -272927492,Lauren Smith,GBR,female,1991-09-26,1.7,,badminton,0,0,0 -981950752,Lauren Wells,AUS,female,1988-08-03,1.78,66.0,athletics,0,0,0 -805070729,Lauren Wilkinson,CAN,female,1989-10-17,1.8,75.0,rowing,0,0,0 -638896430,Laurence Baldauff,AUT,female,1974-11-19,1.64,57.0,archery,0,0,0 -542188232,Laurence Brize,FRA,female,1976-07-12,1.58,55.0,shooting,0,0,0 -874551869,Laurence Halsted,GBR,male,1984-05-22,1.85,85.0,fencing,0,0,0 -935729210,Laurens de Plus,BEL,male,1995-09-04,1.88,67.0,cycling,0,0,0 -940301095,Laurent Carnol,LUX,male,1989-10-17,1.87,82.0,aquatics,0,0,0 -286702221,Laurent Couhet,BRA,male,1994-07-12,1.71,75.0,rugby sevens,0,0,0 -821849198,Laurent Jr. Clayton,ISV,male,1990-07-18,,,boxing,0,0,0 -410803236,Laurie Berthon,FRA,female,1991-08-26,1.69,69.0,cycling,0,0,0 -937639115,Laurien Leurink,NED,female,1994-11-13,1.73,67.0,hockey,0,1,0 -256435241,Laurine van Riessen,NED,female,1987-08-10,1.67,63.0,cycling,0,0,0 -822053837,Laurisa Landre,FRA,female,1985-10-27,1.74,67.0,handball,0,1,0 -110156979,Lauritz Schoof,GER,male,1990-10-07,1.95,98.0,rowing,1,0,0 -438898270,Lautaro Giannetti,ARG,male,1993-11-13,1.75,80.0,football,0,0,0 -408272109,Lavenia Tinai,FIJ,female,1990-09-07,1.65,57.0,rugby sevens,0,0,0 -6350689,Laverne Jones-Ferrette,ISV,female,1981-09-16,1.73,68.0,athletics,0,0,0 -735071398,Lawrence Brittain,RSA,male,1990-11-09,1.87,94.0,rowing,0,1,0 -680095587,Lawrence Clarke,GBR,male,1990-03-12,1.87,77.0,athletics,0,0,0 -295372543,Lawrence Fanous,JOR,male,1985-08-27,1.7,61.0,triathlon,0,0,0 -523134751,Lawrence Okolie,GBR,male,1992-12-16,1.95,,boxing,0,0,0 -29990303,Laxmirani Majhi,IND,female,1989-01-26,1.61,56.0,archery,0,0,0 -205316686,Lazaro Jorge Alvarez,CUB,male,1991-01-28,1.73,,boxing,0,0,1 -801084506,Lazaro Martinez,CUB,male,1997-11-03,1.91,83.0,athletics,0,0,0 -129629128,Lea Davison,USA,female,1983-05-19,1.68,55.0,cycling,0,0,0 -806871935,Lea Jamelot,FRA,female,1992-11-28,1.68,65.0,canoe,0,0,0 -909792265,Lea Sprunger,SUI,female,1990-03-05,1.83,69.0,athletics,0,0,0 -990477427,Lea Yanitsas,AUS,female,1989-03-15,1.73,77.0,aquatics,0,0,0 -49883014,Leah Kirchmann,CAN,female,1990-06-30,1.67,59.0,cycling,0,0,0 -275124572,Leah Neale,AUS,female,1995-08-01,1.73,77.0,aquatics,0,1,0 -436743075,Leah Nugent,JAM,female,1992-11-23,1.73,66.0,athletics,0,0,0 -735079950,Leah Smith,USA,female,1995-04-19,1.76,67.0,aquatics,1,0,1 -24067332,Leander Paes,IND,male,1973-06-17,1.75,82.0,tennis,0,0,0 -516387036,Leandra Smeda,RSA,female,1989-07-22,1.65,55.0,football,0,0,0 -793474789,Leandre Bouchard,CAN,male,1992-10-20,1.93,81.0,cycling,0,0,0 -925700994,Leandro Barbosa,BRA,male,1982-11-28,1.94,97.0,basketball,0,0,0 -495484356,Leandro Blanc,ARG,male,1993-05-02,1.65,,boxing,0,0,0 -737168697,Leandro Silva,POR,male,1994-05-04,1.8,70.0,football,0,0,0 -964889059,Leandro Vega,ARG,male,1996-05-27,1.66,70.0,football,0,0,0 -121652442,Leandro Zamora,CUB,male,1996-03-11,1.86,72.0,athletics,0,0,0 -969409512,Leanne Choo,AUS,female,1991-06-05,1.67,58.0,badminton,0,0,0 -327424646,Lebenya Nkoka,LES,male,1982-10-19,,,athletics,0,0,0 -262078438,Lebo Mothiba,RSA,male,1996-01-28,1.81,69.0,football,0,0,0 -895052770,Lebogang Shange,RSA,male,1990-08-01,1.6,56.0,athletics,0,0,0 -797864785,Lebohang Ramalepe,RSA,female,1991-12-03,1.55,48.0,football,0,0,0 -785631417,Lee Kiefer,USA,female,1994-06-15,1.63,48.0,fencing,0,0,0 -277445751,Lee Parkhill,CAN,male,1988-11-22,1.81,82.0,sailing,0,0,0 -101248331,Lee-Ann Persse,RSA,female,1988-11-20,1.81,74.0,rowing,0,0,0 -603020546,Leevan Sands,BAH,male,1981-08-16,1.91,82.0,athletics,0,0,0 -804034599,Lei Gao,CHN,male,1992-01-02,1.7,64.0,gymnastics,0,0,1 -101911346,Lei Gong,CHN,male,1983-03-26,1.85,88.0,sailing,0,0,0 -512537797,Lei Kou,UKR,male,1987-11-20,1.76,74.0,table tennis,0,0,0 -988926338,Leia da Silva Nicolosi,BRA,female,1985-03-01,1.69,58.0,volleyball,0,0,0 -749520759,Leicy Santos,COL,female,1996-05-16,1.54,56.0,football,0,0,0 -886789693,Leidy Asprilla,COL,female,1997-04-18,1.58,57.0,football,0,0,0 -772518490,Leidy Yessenia Solis Arboleda,COL,female,1990-02-17,1.68,69.0,weightlifting,0,0,0 -381090123,Leidys Brito,VEN,female,1984-07-05,1.69,52.0,archery,0,0,0 -30611869,Leila Abdelmoez,EGY,female,1996-09-30,1.6,46.0,aquatics,0,0,0 -122417468,Leila Luik,EST,female,1985-10-14,1.65,48.0,athletics,0,0,0 -715708025,Leilani Mitchell,AUS,female,1985-06-15,1.65,60.0,basketball,0,0,0 -308992513,Leinier Eunice Pero,CUB,male,1992-11-24,1.92,,boxing,0,0,0 -746238944,Lely Berlitt Burgos Ortiz,PUR,female,1985-06-06,1.53,50.0,weightlifting,0,0,0 -910081538,Lemi Berhanu,ETH,male,1994-09-13,1.68,53.0,athletics,0,0,0 -204521805,Lena Goessling,GER,female,1986-03-08,1.71,56.0,football,1,0,0 -505827943,Lena Kreundl,AUT,female,1997-09-19,1.74,69.0,aquatics,0,0,0 -320412912,Lena Schoneborn,GER,female,1986-04-11,1.79,60.0,modern pentathlon,0,0,0 -50309903,Lena Urbaniak,GER,female,1992-10-31,1.74,92.0,athletics,0,0,0 -949030090,Lenchu Kunzang,BHU,female,1992-02-10,1.65,59.0,shooting,0,0,0 -82742065,Lenin Preciado,ECU,male,1993-08-23,1.7,60.0,judo,0,0,0 -138499307,Lenka Antosova,CZE,female,1991-09-27,1.77,70.0,rowing,0,0,0 -903947461,Lennie Waite,GBR,female,1986-02-04,1.71,59.0,athletics,0,0,0 -483445056,Leo Mainoldi,ARG,male,1985-03-04,2.04,106.0,basketball,0,0,0 -442415993,Leon Goretzka,GER,male,1995-02-06,1.86,76.0,football,0,0,0 -200012588,Leona Maguire,IRL,female,1994-11-30,1.68,56.0,golf,0,0,0 -736914222,Leonard Essau Korir,USA,male,1986-12-10,1.71,59.0,athletics,0,0,0 -55261773,Leonard Ong,SIN,male,1992-12-09,1.7,68.0,sailing,0,0,0 -617980909,Leonardo Bittencourt,GER,male,1993-12-19,1.71,62.0,football,0,0,0 -168519114,Leonardo Chacon,CRC,male,1984-06-29,1.79,65.0,triathlon,0,0,0 -403277949,Leonardo Querin,ARG,male,1982-04-17,1.97,105.0,handball,0,0,0 -40283995,Leonardo Santos,BRA,male,1994-05-30,1.94,104.0,handball,0,0,0 -159048041,Leonardo de Deus,BRA,male,1991-01-18,1.75,70.0,aquatics,0,0,0 -639962139,Leone Nakarawa,FIJ,male,1988-04-02,1.98,109.0,rugby sevens,1,0,0 -649229119,Leonel Suarez,CUB,male,1987-09-01,1.81,68.0,athletics,0,0,0 -888575799,Leonel de los Santos Nunez,DOM,male,1994-12-14,1.7,,boxing,0,0,0 -655588517,Leonid Andreev,UZB,male,1983-10-06,1.98,93.0,athletics,0,0,0 -295139504,Leonie Adam,GER,female,1993-01-02,1.62,54.0,gymnastics,0,0,0 -953257950,Leonie Antonia Beck,GER,female,1997-05-27,1.84,62.0,aquatics,0,0,0 -812063503,Leonie Kullmann,GER,female,1999-08-26,1.75,62.0,aquatics,0,0,0 -503186709,Leonie Maier,GER,female,1992-09-29,1.63,62.0,football,1,0,0 -862102425,Leonor Rodriguez,ESP,female,1991-10-21,1.8,72.0,basketball,0,1,0 -399491333,Leonora Mackinnon,CAN,female,1994-05-30,1.8,77.0,fencing,0,0,0 -246122300,Leontia Kallenou,CYP,female,1994-10-05,1.8,64.0,athletics,0,0,0 -74708917,Leopold Konig,CZE,male,1987-11-15,1.78,62.0,cycling,0,0,0 -825004701,Lerissa Henry,FSM,female,1997-08-18,1.53,57.0,athletics,0,0,0 -669805740,Lesley Thompson-Willie,CAN,female,1959-09-20,1.6,50.0,rowing,0,0,0 -660063171,Leslie Copeland,FIJ,male,1988-04-23,1.83,102.0,athletics,0,0,0 -244021964,Lestrod Roland,SKN,male,1992-09-05,1.65,54.0,athletics,0,0,0 -792302802,Leticia Boscacci,ARG,female,1985-11-08,1.86,70.0,volleyball,0,0,0 -927194850,Leticia Romero,ESP,female,1995-05-28,1.77,62.0,basketball,0,1,0 -504480132,Leticia de Souza,BRA,female,1996-05-06,1.65,49.0,athletics,0,0,0 -53358242,Leuris Pupo,CUB,male,1977-04-09,1.68,78.0,shooting,0,0,0 -340107234,Levan Berianidze,ARM,male,1990-10-10,1.86,125.0,wrestling,0,0,0 -204502697,Levent Tuncat,GER,male,1988-07-29,1.73,61.0,taekwondo,0,0,0 -159745591,Levern Spencer,LCA,female,1984-06-23,1.79,48.0,athletics,0,0,0 -576128253,Levi Cadogan,BAR,male,1995-11-08,1.81,78.0,athletics,0,0,0 -228788380,Levon Aghasyan,ARM,male,1995-01-19,1.93,75.0,athletics,0,0,0 -600455405,Lewis Holland,AUS,male,1993-01-14,1.83,89.0,rugby sevens,0,0,0 -362932074,Lewis Ormond,NZL,male,1994-02-05,1.92,100.0,rugby sevens,0,0,0 -109119825,Lexi Thompson,USA,female,1995-02-10,1.81,72.0,golf,0,0,0 -237908464,Lexi Weeks,USA,female,1996-11-20,,,athletics,0,0,0 -544849480,Leydi Laura Moya,CUB,female,1992-04-16,1.7,68.0,modern pentathlon,0,0,0 -654502394,Leyla Rajabi,IRI,female,1983-04-18,1.87,100.0,athletics,0,0,0 -176154136,Li Du,CHN,female,1982-03-05,1.7,55.0,shooting,0,1,1 -447494695,Li Guo,CHN,female,1993-05-11,1.67,54.0,aquatics,0,1,0 -157385962,Li Lin,CHN,female,1992-07-05,1.71,70.0,volleyball,1,0,0 -394183572,Li Yang,CHN,female,1991-01-31,1.67,58.0,football,0,0,0 -462590793,Lia Neal,USA,female,1995-02-13,1.78,74.0,aquatics,0,1,0 -472892087,Liadagmis Povea,CUB,female,1996-02-06,1.66,52.0,athletics,0,0,0 -329731832,Liam Adams,AUS,male,1986-09-04,1.78,64.0,athletics,0,0,0 -865915642,Liam Bertazzo,ITA,male,1992-02-17,1.85,70.0,cycling,0,0,0 -131214220,Liam Heath,GBR,male,1984-08-17,1.82,81.0,canoe,1,1,0 -945401924,Liam Phillips,GBR,male,1989-03-11,1.81,85.0,cycling,0,0,0 -182272008,Liam Pitchford,GBR,male,1993-07-12,1.86,67.0,table tennis,0,0,0 -837742830,Lian Yuan Wang,CHN,female,1994-08-26,1.67,60.0,triathlon,0,0,0 -518240529,Liana Salazar,COL,female,1992-09-16,1.69,52.0,football,0,0,0 -169395175,Lianna Swan,PAK,female,1997-03-25,1.65,67.0,aquatics,0,0,0 -82404018,Lianne Tan,BEL,female,1990-11-20,1.6,54.0,badminton,0,0,0 -598538220,Libania Grenot,ITA,female,1983-07-12,1.75,60.0,athletics,0,0,0 -9859586,Libuse Jahodova,CZE,female,1992-05-31,1.63,52.0,shooting,0,0,0 -427854268,Licet Hernandez,CUB,female,1993-04-14,1.68,57.0,rowing,0,0,0 -641616840,Lidewij Welten,NED,female,1990-07-16,1.7,64.0,hockey,0,1,0 -860964222,Lidia Valentin Perez,ESP,female,1985-02-10,1.69,74.0,weightlifting,0,0,1 -790043158,Lidiane Lopes,CPV,female,1994-09-01,1.78,70.0,athletics,0,0,0 -513701226,Lidiia Sichenikova,UKR,female,1993-02-03,1.83,69.0,archery,0,0,0 -352163486,Lieke Wevers,NED,female,1991-09-17,1.67,54.0,gymnastics,0,0,0 -12425606,Liemarvin Bonevacia,NED,male,1989-04-05,1.85,74.0,athletics,0,0,0 -763919056,Lies Rustenburg,NED,female,1990-04-08,1.83,74.0,rowing,0,0,0 -390159530,Liina Laasma,EST,female,1992-01-13,1.74,67.0,athletics,0,0,0 -795063034,Liina Luik,EST,female,1985-10-14,1.64,48.0,athletics,0,0,0 -547595943,Lijia Xu,CHN,female,1987-08-30,1.76,60.0,sailing,0,0,0 -16471586,Lijiao Gong,CHN,female,1989-01-24,1.75,105.0,athletics,0,0,0 -202813499,Lijun Chen,CHN,male,1993-02-08,1.62,62.0,weightlifting,0,0,0 -653834235,Lijun Zu,CHN,male,1989-11-26,1.88,87.0,aquatics,0,0,0 -410869644,Likourgos-Stefanos Tsakonas,GRE,male,1990-03-08,1.85,80.0,athletics,0,0,0 -268704027,Lilia Fisikowici,MDA,female,1989-03-29,1.7,52.0,athletics,0,0,0 -150040443,Lilian Castro,ESA,female,1986-12-19,1.64,63.0,shooting,0,0,0 -736338812,Lilian de Geus,NED,female,1991-10-13,1.64,57.0,sailing,0,0,0 -832461074,Liliana Fernandez Steiner,ESP,female,1987-01-04,1.78,75.0,volleyball,0,0,0 -720106859,Liliana Ibanez Lopez,MEX,female,1991-01-30,1.8,70.0,aquatics,0,0,0 -42265124,Liliana Neto,ANG,female,1997-01-29,1.66,57.0,athletics,0,0,0 -93076735,Liliana Szilagyi,HUN,female,1996-11-19,1.75,62.0,aquatics,0,0,0 -92875421,Liliana da Silva Venancio,ANG,female,1995-09-19,1.8,93.0,handball,0,0,0 -277003612,Lilima Minz,IND,female,1994-04-10,1.58,52.0,hockey,0,0,0 -986623608,Lilit Harutyunyan,ARM,female,1993-04-04,1.64,55.0,athletics,0,0,0 -39304647,Liliyana Natsir,INA,female,1985-09-09,1.69,62.0,badminton,1,0,0 -461914120,Lilly King,USA,female,1997-02-10,1.76,70.0,aquatics,2,0,0 -80507918,Lily Luik,EST,female,1985-10-14,1.63,48.0,athletics,0,0,0 -877281953,Lily Owsley,GBR,female,1994-12-10,1.7,70.0,hockey,1,0,0 -919045571,Lily Zhang,USA,female,1996-06-16,1.66,58.0,table tennis,0,0,0 -474911998,Lin Gui,BRA,female,1993-10-01,1.68,52.0,table tennis,0,0,0 -64295130,Lin Peng,CHN,female,1995-04-04,1.83,73.0,aquatics,0,0,0 -821207198,Lina Guerin,FRA,female,1991-04-16,1.75,64.0,rugby sevens,0,0,0 -530731946,Lina Magull,GER,female,1994-08-15,1.64,59.0,football,0,0,0 -472444817,Lina Marcela Rivas Ordonez,COL,female,1990-04-24,1.59,58.0,weightlifting,0,0,0 -525260451,Lina Saltyte,LTU,female,1987-02-09,1.84,70.0,rowing,0,0,0 -449688745,Lina Zhao,CHN,female,1991-09-18,1.88,67.0,football,0,0,0 -932267400,Linda Algotsson,SWE,female,1972-03-22,1.62,50.0,equestrian,0,0,0 -489441345,Linda Bolder,ISR,female,1988-07-03,1.73,70.0,judo,0,0,0 -413014010,Linda Cerruti,ITA,female,1993-10-07,1.73,56.0,aquatics,0,0,0 -401236995,Linda Fahrni,SUI,female,1993-05-25,1.57,56.0,sailing,0,0,0 -849736805,Linda Indergand,SUI,female,1993-07-13,1.69,65.0,cycling,0,0,0 -236104816,Linda Motlhalo,RSA,female,1998-07-01,1.62,47.0,football,0,0,0 -351574736,Linda Sandblom,FIN,female,1989-10-18,1.76,62.0,athletics,0,0,0 -401452346,Linda Sembrant,SWE,female,1987-05-15,1.75,66.0,football,0,1,0 -843070999,Linda Stahl,GER,female,1985-10-02,1.76,79.0,athletics,0,0,0 -446754440,Linda Villumsen,NZL,female,1985-04-09,1.65,58.0,cycling,0,0,0 -793960001,Linda Zetchiri,BUL,female,1987-07-27,1.69,58.0,badminton,0,0,0 -351805810,Lindaweni Fanetri,INA,female,1990-01-18,1.67,60.0,badminton,0,0,0 -510445480,Linden Hall,AUS,female,1991-06-20,1.67,51.0,athletics,0,0,0 -470256290,Lindiwe Magwede,ZIM,female,1991-12-01,1.76,59.0,football,0,0,0 -644836357,Lindolfo Delgado,MEX,male,1994-12-31,1.74,,boxing,0,0,0 -934402077,Lindon Victor,GRN,male,1993-02-28,,,athletics,0,0,0 -408373995,Lindsay Hanekom,RSA,male,1993-05-15,1.78,66.0,athletics,0,0,0 -181124319,Lindsay Jennerich,CAN,female,1982-07-30,1.65,59.0,rowing,0,1,0 -116706289,Lindsay Whalen,USA,female,1982-05-09,1.75,72.0,basketball,1,0,0 -365794539,Lindsey Harding,BLR,female,1984-06-12,1.73,65.0,basketball,0,0,0 -830406160,Lindsey Horan,USA,female,1994-05-26,1.75,76.0,football,0,0,0 -727787093,Line Kjaersfeldt,DEN,female,1994-04-20,1.75,65.0,badminton,0,0,0 -925589540,Linet Arasa,KEN,female,1996-01-01,2.03,61.0,rugby sevens,0,0,0 -24311177,Ling Li,CHN,female,1989-07-06,1.85,63.0,athletics,0,0,0 -41586210,Ling Zhang,CHN,female,1997-02-27,1.82,75.0,rowing,0,0,0 -348322475,Ling Zhang,CHN,female,1992-09-18,1.72,51.0,gymnastics,0,0,0 -439546048,Lingwei Kong,CHN,female,1995-07-28,1.63,51.0,athletics,0,0,0 -399160547,Lingwei Li,CHN,female,1989-01-26,1.74,72.0,athletics,0,0,0 -745922840,Linn Blohm,SWE,female,1992-05-20,1.8,79.0,handball,0,0,0 -485795887,Linn-Kristin Riegelhuth Koren,NOR,female,1984-08-01,1.75,,handball,0,0,1 -502728742,Linnea Stensils,SWE,female,1994-03-08,1.78,69.0,canoe,0,0,0 -517447697,Linnea Torstensson,SWE,female,1983-03-30,1.86,82.0,handball,0,0,0 -514401054,Lino Munoz,MEX,male,1991-02-08,1.74,65.0,badminton,0,0,0 -542689552,Linus Butt,GER,male,1987-03-12,1.86,83.0,hockey,0,0,1 -983582126,Liosbel Hernandez,CUB,male,1983-12-17,1.9,70.0,rowing,0,0,0 -557752206,Lisa Altenburg,GER,female,1989-09-23,1.55,58.0,hockey,0,0,1 -416331629,Lisa Brennauer,GER,female,1988-06-08,1.68,63.0,cycling,0,0,0 -135005136,Lisa Carrington,NZL,female,1989-06-23,1.68,63.0,canoe,1,0,1 -922248644,Lisa Dahlkvist,SWE,female,1987-02-06,1.73,68.0,football,0,1,0 -191131636,Lisa Darmanin,AUS,female,1991-08-27,1.68,65.0,sailing,0,1,0 -954723577,Lisa Ecker,AUT,female,1992-09-19,1.57,55.0,gymnastics,0,0,0 -25397368,Lisa Ericson,SWE,female,1988-05-09,1.65,60.0,sailing,0,0,0 -128351056,Lisa Graf,GER,female,1992-11-13,1.83,69.0,aquatics,0,0,0 -221215315,Lisa Hahner,GER,female,1989-11-20,1.67,50.0,athletics,0,0,0 -211903668,Lisa Jane Weightman,AUS,female,1979-01-16,1.57,44.0,athletics,0,0,0 -18063764,Lisa Klein,GER,female,1996-07-15,1.7,62.0,cycling,0,0,0 -526656164,Lisa Mayer,GER,female,1996-05-02,1.71,57.0,athletics,0,0,0 -765502625,Lisa Norden,SWE,female,1984-11-24,1.76,60.0,triathlon,0,0,0 -620856226,Lisa Roman,CAN,female,1989-09-17,1.78,76.0,rowing,0,0,0 -298441762,Lisa Ryzih,GER,female,1988-09-27,1.79,62.0,athletics,0,0,0 -130489647,Lisa Schmidla,GER,female,1991-06-05,1.73,76.0,rowing,1,0,0 -467310646,Lisa Schutze,GER,female,1996-10-05,1.72,61.0,hockey,0,0,1 -241114049,Lisa Unruh,GER,female,1988-04-12,1.8,68.0,archery,0,1,0 -437677562,Lisa Weiss,GER,female,1987-10-29,1.71,67.0,football,0,0,0 -700918622,Lisa Zaiser,AUT,female,1994-08-23,1.74,63.0,aquatics,0,0,0 -713570964,Lisa de Vanna,AUS,female,1984-11-14,1.56,56.0,football,0,0,0 -531666136,Lisandra Guerra Rodriguez,CUB,female,1987-10-31,1.67,69.0,cycling,0,0,0 -268135411,Lisandro Magallan,ARG,male,1993-09-27,1.81,79.0,football,0,0,0 -398635028,Lisanne de Witte,NED,female,1992-09-10,1.75,65.0,athletics,0,0,0 -21753483,Lisbet Jakobsen,DEN,female,1987-01-21,1.79,71.0,rowing,0,0,0 -155823041,Lisneidy Veitia,CUB,female,1994-04-29,1.68,58.0,athletics,0,0,0 -13117642,Lissa Labiche,SEY,female,1993-02-18,1.72,55.0,athletics,0,0,0 -461845874,Lissette Alexandra Antes Castillo,ECU,female,1991-05-02,1.77,58.0,wrestling,0,0,0 -728085851,Litia Naiqato,FIJ,female,1987-03-25,1.8,76.0,rugby sevens,0,0,0 -380475095,Liu Ying Goh,MAS,female,1989-05-30,1.66,56.0,badminton,0,1,0 -82572587,Liubomyr Lemeshko,UKR,male,1992-07-19,1.86,86.0,aquatics,0,0,0 -307207034,Liubov Basova,UKR,female,1988-07-16,1.62,65.0,cycling,0,0,0 -737904041,Liudmila Dmitrieva,RUS,female,1989-05-02,1.8,70.0,sailing,0,0,0 -35679352,Liudmyla Kichenok,UKR,female,1992-07-20,1.76,65.0,tennis,0,0,0 -672950250,Livan Lopez Azcuy,CUB,male,1982-01-24,1.7,74.0,wrestling,0,0,0 -537422944,Livan Osoria Rodriguez,CUB,male,1994-02-05,2.01,96.0,volleyball,0,0,0 -345846661,Livio LA PADULA,ITA,male,1985-11-20,1.83,72.0,rowing,0,0,0 -836548598,Lizanne Murphy,CAN,female,1984-03-15,1.85,75.0,basketball,0,0,0 -463712046,Lizhu Huang,CHN,female,1987-10-09,1.74,71.0,sailing,0,0,0 -887425403,Lizzie Lee,IRL,female,1980-05-22,1.65,51.0,athletics,0,0,0 -225973698,Lkhamdegd Purevjargal,MGL,female,1986-09-18,1.8,80.0,judo,0,0,0 -546982242,Loan His,FRA,female,1999-04-10,1.6,51.0,gymnastics,0,0,0 -612340234,Logan Cunningham,USA,male,1991-05-30,1.76,72.0,athletics,0,0,0 -338570667,Logan Dooley,USA,male,1987-09-26,1.75,58.0,gymnastics,0,0,0 -92285054,Lohaynny Vicente,BRA,female,1996-05-02,1.68,70.0,badminton,0,0,0 -586219392,Loic Pietri,FRA,male,1990-08-27,1.77,81.0,judo,0,0,0 -999987786,Loick Luypaert,BEL,male,1991-08-19,1.81,78.0,hockey,0,1,0 -774306010,Lois Abbingh,NED,female,1992-08-13,1.78,72.0,handball,0,0,0 -508980654,Lois Maikel Martinez,ESP,male,1981-06-03,1.87,120.0,athletics,0,0,0 -667499984,Lois Toulson,GBR,female,1999-09-26,1.66,51.0,aquatics,0,0,0 -974884842,Lok Yan Poon,HKG,female,1991-08-22,1.69,56.0,badminton,0,0,0 -270039898,Lola Riera,ESP,female,1991-06-25,1.72,74.0,hockey,0,0,0 -993580824,Lolita Ananasova,UKR,female,1992-07-09,1.69,52.0,aquatics,0,0,0 -962247263,Lomano Lemeki,JPN,male,1989-01-20,1.77,92.0,rugby sevens,0,0,0 -366057032,Long Chen,CHN,male,1989-01-18,1.88,81.0,badminton,1,0,0 -865328770,Long Gutierrez Feng,MEX,male,1995-02-23,1.9,85.0,aquatics,0,0,0 -736738706,Long Ma,CHN,male,1988-10-20,1.75,72.0,table tennis,2,0,0 -399910648,Lonneke Sloetjes,NED,female,1990-11-15,1.92,77.0,volleyball,0,0,0 -779658344,Loredana Dinu,ROU,female,1984-04-02,1.68,60.0,fencing,1,0,0 -398479062,Lorena Molinos,BRA,female,1991-03-02,1.6,51.0,aquatics,0,0,0 -180299954,Lorene Dorcas Bazolo,POR,female,1983-05-04,1.7,60.0,athletics,0,0,0 -554615022,Lorenzo Sotomayor Collazo,AZE,male,1985-02-16,1.85,,boxing,0,1,0 -636450373,Loreta Gulotta,ITA,female,1987-05-08,1.74,63.0,fencing,0,0,0 -634263213,Lornah Chemtai Korlima,ISR,female,1988-12-12,1.65,52.0,athletics,0,0,0 -904651975,Lorraine Ugen,GBR,female,1991-08-22,1.79,65.0,athletics,0,0,0 -753835891,Lorrane Oliveira,BRA,female,1998-04-13,1.53,50.0,gymnastics,0,0,0 -399652811,Lorys Bourelly,FRA,male,1992-05-27,1.86,76.0,aquatics,0,0,0 -345563722,Lote Tuqiri,JPN,male,1987-11-12,1.89,99.0,rugby sevens,0,0,0 -809986793,Lotta Lepisto,FIN,female,1989-06-28,1.64,58.0,cycling,0,0,0 -97180847,Lotta Schelin,SWE,female,1984-02-27,1.79,66.0,football,0,1,0 -421793519,Lotte Friis,DEN,female,1988-02-09,1.83,75.0,aquatics,0,0,0 -290076256,Lotte Kopecky,BEL,female,1995-11-10,1.71,66.0,cycling,0,0,0 -224274265,Louis Croenen,BEL,male,1994-01-04,1.86,79.0,aquatics,0,0,0 -99752368,Louis Meintjes,RSA,male,1992-02-21,1.73,58.0,cycling,0,0,0 -84986974,Louis Smith,GBR,male,1989-04-22,1.8,78.0,gymnastics,0,1,0 -677916006,Louisa Cadamuro,FRA,female,1987-01-23,1.68,56.0,football,0,0,0 -23974217,Louisa Chafee,USA,female,1991-09-24,1.66,56.0,sailing,0,0,0 -22758388,Louisa Gurski,GBR,female,1988-05-26,1.72,70.0,canoe,0,0,0 -544547565,Louise Bawden,AUS,female,1981-08-07,1.83,72.0,volleyball,0,0,0 -347394915,Louise Bloor,GBR,female,1985-09-21,1.68,63.0,athletics,0,0,0 -59039148,Louise Carton,BEL,female,1994-04-16,1.81,60.0,athletics,0,0,0 -602283676,Louise Hansson,SWE,female,1996-11-24,1.87,72.0,aquatics,0,0,0 -809952135,Louise Sand,SWE,female,1992-12-27,1.64,62.0,handball,0,0,0 -394180888,Louise Vanhille,FRA,female,1998-11-06,1.67,55.0,gymnastics,0,0,0 -850074642,Lourdes Mohedano,ESP,female,1995-06-17,1.73,53.0,gymnastics,0,1,0 -911242137,Lovisa Lindh,SWE,female,1991-07-09,1.69,57.0,athletics,0,0,0 -105066319,Lu Li,CHN,female,1992-02-18,1.6,55.0,canoe,0,0,0 -276246857,Luan,BRA,male,1993-03-27,1.8,71.0,football,1,0,0 -642783989,Luan Garcia,BRA,male,1993-05-10,1.83,79.0,football,1,0,0 -156350082,Luba Golovina,GEO,female,1990-04-20,1.72,55.0,gymnastics,0,0,0 -682800627,Lubna Alomair,KSA,female,1987-04-14,1.52,45.0,fencing,0,0,0 -475847268,Lubomir Jancarik,CZE,male,1987-08-17,1.93,82.0,table tennis,0,0,0 -878070410,Luc Abalo,FRA,male,1984-09-06,1.82,86.0,handball,0,1,0 -642947259,Luca Agamennoni,ITA,male,1980-08-08,1.87,93.0,rowing,0,0,0 -338874266,Luca Braidot,ITA,male,1991-05-29,1.79,69.0,cycling,0,0,0 -554715039,Luca Cupido,USA,male,1995-11-09,1.88,95.0,aquatics,0,0,0 -741078160,Luca Dotto,ITA,male,1990-04-18,1.92,80.0,aquatics,0,0,0 -659816292,Luca Leonardi,ITA,male,1991-01-01,1.91,78.0,aquatics,0,0,0 -313498882,Luca Marin,ITA,male,1986-04-09,1.87,82.0,aquatics,0,0,0 -543136851,Luca Masso,ARG,male,1994-07-17,1.86,82.0,hockey,1,0,0 -129051185,Luca Pizzini,ITA,male,1989-04-08,1.85,76.0,aquatics,0,0,0 -537669111,Luca Roman,ITA,male,1985-12-10,1.74,69.0,equestrian,0,0,0 -141527506,Luca Vettori,ITA,male,1991-04-26,2.0,95.0,volleyball,0,1,0 -564323858,Lucas Bruchet,CAN,male,1991-02-23,1.83,69.0,athletics,0,0,0 -129309426,Lucas Calabrese,ARG,male,1986-12-12,1.68,60.0,sailing,0,0,0 -604160230,Lucas Candido,BRA,male,1989-03-19,1.85,88.0,handball,0,0,0 -711817435,Lucas Carvalho,BRA,male,1993-07-16,1.75,51.0,athletics,0,0,0 -243504044,Lucas Daniel,FRA,male,1995-01-01,1.8,80.0,archery,0,0,0 -599092094,Lucas Duque,BRA,male,1984-03-15,1.7,84.0,rugby sevens,0,0,0 -489045674,Lucas Jakubczyk,GER,male,1985-04-28,1.83,77.0,athletics,0,0,0 -79045868,Lucas Kozeniesky,USA,male,1995-05-31,1.81,79.0,shooting,0,0,0 -78252233,Lucas Paixao,BRA,male,1994-09-02,1.72,62.0,hockey,0,0,0 -199482882,Lucas Rey,ARG,male,1982-10-11,1.77,74.0,hockey,1,0,0 -517848380,Lucas Romero,ARG,male,1994-04-18,1.69,73.0,football,0,0,0 -988887897,Lucas Rossi,ARG,male,1985-06-02,1.88,88.0,hockey,1,0,0 -81328247,Lucas Saatkamp,BRA,male,1986-03-06,2.09,101.0,volleyball,1,0,0 -548331359,Lucas Schaefer,GER,male,1994-08-15,1.87,70.0,rowing,0,0,0 -505340328,Lucas Tramer,SUI,male,1989-09-01,1.83,75.0,rowing,1,0,0 -612347154,Lucas Vila,ARG,male,1986-08-23,1.72,75.0,hockey,1,0,0 -249567858,Lucia Falasca,ARG,female,1993-07-08,1.71,64.0,sailing,0,0,0 -668932185,Lucia Fresco,ARG,female,1991-05-14,1.95,92.0,volleyball,0,0,0 -546619067,Lucia Haro,ARG,female,1986-08-21,1.78,71.0,handball,0,0,0 -542002382,Lucia Hrivnak Klocova,SVK,female,1983-11-20,1.72,58.0,athletics,0,0,0 -752386508,Lucia Jimenez,ESP,female,1997-01-08,1.63,51.0,hockey,0,0,0 -480093608,Lucia Mwihaki Kimani,BIH,female,1981-06-21,1.66,48.0,athletics,0,0,0 -901297411,Lucia Palermo,ARG,female,1985-09-30,1.73,59.0,rowing,0,0,0 -299729150,Luciana,BRA,female,1987-07-24,1.71,74.0,football,0,0,0 -371222775,Luciana Diniz,POR,female,1970-10-11,1.75,55.0,equestrian,0,0,0 -45981048,Luciana Mendoza,ARG,female,1990-03-14,1.7,68.0,handball,0,0,0 -422969496,Luciana Salvado,ARG,female,1990-04-13,1.69,65.0,handball,0,0,0 -345497174,Lucianne Barroncas,BRA,female,1988-04-01,1.69,64.0,aquatics,0,0,0 -95393469,Luciano Taccone,ARG,male,1989-05-29,1.76,63.0,triathlon,0,0,0 -55174907,Luciano de Cecco,ARG,male,1988-06-02,1.91,98.0,volleyball,0,0,0 -678484438,Lucie Hradecka,CZE,female,1985-05-21,1.78,70.0,tennis,0,0,1 -801203222,Lucie Safarova,CZE,female,1987-02-04,1.77,67.0,tennis,0,0,1 -822537753,Lucie Svecena,CZE,female,1997-08-21,1.75,60.0,aquatics,0,0,0 -395171804,Lucien Cujean,SUI,male,1989-08-16,1.84,82.0,sailing,0,0,0 -894482944,Lucien Delfour,AUS,male,1988-12-22,1.77,70.0,canoe,0,0,0 -300937912,Lucija Zaninovic,CRO,female,1987-06-26,1.7,52.0,taekwondo,0,0,0 -829679838,Lucila Pascua,ESP,female,1983-03-21,1.96,93.0,basketball,0,1,0 -508413234,Lucilla Boari,ITA,female,1997-03-24,1.62,82.0,archery,0,0,0 -949015694,Lucina von der Heyde,ARG,female,1997-01-24,1.6,57.0,hockey,0,0,0 -61310516,Lucy Davis,USA,female,1992-10-22,1.66,55.0,equestrian,0,1,0 -158550880,Lucy Oliver,NZL,female,1988-11-18,1.68,53.0,athletics,0,0,0 -645035836,Lucy Stephan,AUS,female,1991-12-10,1.74,67.0,rowing,0,0,0 -803793188,Ludger Beerbaum,GER,male,1963-08-26,1.9,85.0,equestrian,0,0,1 -721221132,Ludovic Chammartin,SUI,male,1985-01-31,1.68,60.0,judo,0,0,0 -549211335,Ludovic Fabregas,FRA,male,1996-07-01,1.98,100.0,handball,0,1,0 -5384302,Ludovic Henry,FRA,male,1968-10-04,1.95,88.0,equestrian,0,0,0 -906835303,Ludovico Edalli,ITA,male,1993-12-18,1.65,57.0,gymnastics,0,0,0 -870285108,Ludvy Vaillant,FRA,male,1995-03-15,1.8,67.0,athletics,0,0,0 -551941271,Ludwig Paischer,AUT,male,1981-11-28,1.7,60.0,judo,0,0,0 -736364717,Ludwig Svennerstal,SWE,male,1990-08-24,1.84,72.0,equestrian,0,0,0 -708479059,Luguelin Santos,DOM,male,1993-11-12,1.63,55.0,athletics,0,0,0 -729320329,Luigi Lodde,ITA,male,1980-04-19,1.7,79.0,shooting,0,0,0 -498891159,Luigi Teilemb,VAN,male,1992-02-25,1.87,80.0,rowing,0,0,0 -354961149,Luillys Jose Perez Mora,VEN,male,1990-12-23,1.81,97.0,wrestling,0,0,0 -237111206,Luis Alberto Garcia Brito,DOM,male,1995-04-19,1.48,56.0,weightlifting,0,0,0 -725379575,Luis Alberto Orta,VEN,male,1989-01-15,1.6,50.0,athletics,0,0,0 -116880876,Luis Ariel Molina,ARG,male,1988-03-07,1.72,57.0,athletics,0,0,0 -23362328,Luis Brethauer,GER,male,1992-09-14,1.76,82.0,cycling,0,0,0 -138487719,Luis Cabrera,VEN,male,1995-05-20,1.76,,boxing,0,0,0 -975670610,Luis Charles,DOM,male,1998-12-03,1.83,65.0,athletics,0,0,0 -660174272,Luis Emigdio Vega,CUB,male,1998-11-02,1.79,69.0,aquatics,0,0,0 -133697198,Luis Enrique Lemus Davila,MEX,male,1992-04-21,1.73,61.0,cycling,0,0,0 -600419040,Luis Fernando Lopez,COL,male,1979-06-03,1.66,60.0,athletics,0,0,0 -873077126,Luis Henry Campos,PER,male,1995-10-11,1.66,61.0,athletics,0,0,0 -219480043,Luis Hurtado,COL,male,1994-01-24,1.86,81.0,football,0,0,0 -854498717,Luis Javier Mosquera Lozano,COL,male,1995-03-27,1.65,69.0,weightlifting,0,0,1 -339149887,Luis Joel Castro,PUR,male,1991-01-29,1.98,73.0,athletics,0,0,0 -717592446,Luis Lopez,HON,male,1993-09-13,1.83,84.0,football,0,0,0 -920379695,Luis Lopez,ESA,male,1994-01-18,,,athletics,0,0,0 -851144245,Luis Martin Arcon,VEN,male,1992-06-01,1.79,,boxing,0,0,0 -536738422,Luis Martinez,GUA,male,1995-12-11,1.82,74.0,aquatics,0,0,0 -141098484,Luis Ostos,PER,male,1992-08-09,1.6,50.0,athletics,0,0,0 -772996626,Luis Scola,ARG,male,1980-04-30,2.04,108.0,basketball,0,0,0 -854151517,Luis Vasquez,COL,male,1996-03-01,1.87,76.0,football,0,0,0 -530819042,Luisa Borges,BRA,female,1996-04-20,1.66,54.0,aquatics,0,0,0 -868186831,Luisa Fatiaki Taitapu Peters,COK,female,1993-06-27,1.65,100.0,weightlifting,0,0,0 -153439760,Luisa Helga Gerda Niemesch,GER,female,1995-09-07,1.65,63.0,wrestling,0,0,0 -22717911,Luisa Kiala,ANG,female,1982-01-25,1.8,62.0,handball,0,0,0 -505599669,Luisa Tisolo,FIJ,female,1991-09-20,1.75,63.0,rugby sevens,0,0,0 -52637843,Luisa Trombetti,ITA,female,1993-09-05,1.73,67.0,aquatics,0,0,0 -788404828,Luise Malzahn,GER,female,1990-06-09,1.77,78.0,judo,0,0,0 -706961920,Luisito Pie,DOM,male,1994-03-04,1.83,57.0,taekwondo,0,0,1 -976439894,Luiz Alberto de Araujo,BRA,male,1987-06-27,1.9,86.0,athletics,0,0,0 -705013077,Luiz Altamir,BRA,male,1996-05-09,1.74,69.0,aquatics,0,0,0 -524518675,Luiz Felipe Marques Fonteles,BRA,male,1984-06-19,1.96,89.0,volleyball,1,0,0 -713630406,Luiz Felipe Outerelo,BRA,male,1991-12-11,1.81,82.0,aquatics,0,0,0 -826163083,Luiza Campos,BRA,female,1990-07-30,1.65,64.0,rugby sevens,0,0,0 -396150268,Luiza Carvalho,BRA,female,1983-07-02,1.82,79.0,aquatics,0,0,0 -837407916,Luiza Ganieva,UZB,female,1995-11-11,1.66,53.0,gymnastics,0,0,0 -915002256,Luiza Gega,ALB,female,1988-11-05,1.59,45.0,athletics,0,0,0 -117132954,Luiza Saidiyeva,KAZ,female,1994-03-17,1.62,42.0,archery,0,0,0 -280621392,Luiza Tavares de Almeida,BRA,female,1991-09-07,1.68,58.0,equestrian,0,0,0 -623136878,Luka Babic,CRO,male,1991-09-29,2.02,94.0,basketball,0,0,0 -302739733,Luka Bozic,SLO,male,1991-01-09,1.73,72.0,canoe,0,0,0 -366132937,Luka Bukic,CRO,male,1994-04-30,1.95,90.0,aquatics,0,1,0 -17097553,Luka Cindric,CRO,male,1993-07-05,1.82,90.0,handball,0,0,0 -402639302,Luka Janezic,SLO,male,1995-11-14,1.92,83.0,athletics,0,0,0 -398703913,Luka Karabatic,FRA,male,1988-04-19,2.02,108.0,handball,0,1,0 -504359994,Luka Loncar,CRO,male,1987-06-26,1.95,106.0,aquatics,0,1,0 -504597254,Luka Mratovic,CRO,male,1987-04-07,1.9,78.0,sailing,0,0,0 -189960390,Luka Stepancic,CRO,male,1990-11-20,2.03,98.0,handball,0,0,0 -644882024,Lukas Dauser,GER,male,1993-06-15,1.72,64.0,gymnastics,0,0,0 -924122874,Lukas Fernandes,DEN,male,1993-03-01,1.87,81.0,football,0,0,0 -330673646,Lukas Gdula,CZE,male,1991-12-06,1.78,65.0,athletics,0,0,0 -608004854,Lukas Helesic,CZE,male,1996-01-29,1.89,89.0,rowing,0,0,0 -51396878,Lukas Klostermann,GER,male,1996-06-03,1.89,84.0,football,0,1,0 -921390591,Lukas Krpalek,CZE,male,1990-11-15,1.97,105.0,judo,1,0,0 -877299705,Lukas Melich,CZE,male,1980-09-16,1.86,110.0,athletics,0,0,0 -856483693,Lukas Nilsson,SWE,male,1996-11-16,1.94,97.0,handball,0,0,0 -13539214,Lukas Rosol,CZE,male,1985-07-24,1.95,82.0,tennis,0,0,0 -934798797,Lukas Trefil,CZE,male,1988-09-21,1.87,90.0,canoe,0,0,1 -347976859,Lukas Weisshaidinger,AUT,male,1992-02-20,1.96,136.0,athletics,0,0,0 -997459236,Lukas Werro,SUI,male,1991-06-30,1.75,72.0,canoe,0,0,0 -587024656,Lukasz Gierak,POL,male,1988-06-22,1.94,104.0,handball,0,0,0 -704816796,Lukasz Grzeszczuk,POL,male,1990-03-03,1.9,95.0,athletics,0,0,0 -578022674,Lukasz Krawczuk,POL,male,1989-06-15,1.84,75.0,athletics,0,0,0 -915253145,Lukasz Kubot,POL,male,1982-05-16,1.91,90.0,tennis,0,0,0 -91523255,Lukasz Nowak,POL,male,1988-12-18,1.94,76.0,athletics,0,0,0 -374551802,Lukasz Przybytek,POL,male,1989-05-20,1.78,73.0,sailing,0,0,0 -966057256,Luke Bezzina,MLT,male,1995-06-07,,,athletics,0,0,0 -347048162,Luke Cutts,GBR,male,1988-02-13,1.87,81.0,athletics,0,0,0 -978732566,Luke Mathews,AUS,male,1995-06-21,1.83,70.0,athletics,0,0,0 -251226899,Luke Patience,GBR,male,1986-08-04,1.67,59.0,sailing,0,0,0 -893167750,Luke Ramsay,CAN,male,1988-01-31,1.73,68.0,sailing,0,0,0 -42399683,Lum Zhaveli,KOS,male,1990-03-05,1.94,88.0,aquatics,0,0,0 -78006686,Lumin Wang,CHN,male,1990-12-07,1.7,67.0,wrestling,0,0,0 -537472547,Lungile Gongqa,RSA,male,1979-02-22,1.62,53.0,athletics,0,0,0 -278272710,Lurdes Marcelina Monteiro,ANG,female,1984-07-11,1.7,63.0,handball,0,0,0 -387196979,Lusapho April,RSA,male,1982-05-24,1.72,50.0,athletics,0,0,0 -488085087,Lutalo Muhammad,GBR,male,1991-06-03,1.91,80.0,taekwondo,0,1,0 -845474841,Lutimar Paes,BRA,male,1988-12-14,1.84,77.0,athletics,0,0,0 -14088740,Luuka Jones,NZL,female,1988-10-18,1.72,68.0,canoe,0,1,0 -905348917,Luvo Manyonga,RSA,male,1991-01-08,,,athletics,0,1,0 -859949812,Ly Ho Thi,VIE,female,1991-02-22,1.72,60.0,rowing,0,0,0 -817621896,Lydia Chebet Rotich,KEN,female,1988-08-08,1.57,45.0,athletics,0,0,0 -409442789,Lydia Jele,BOT,female,1990-06-22,,,athletics,0,0,0 -212107375,Lydia Ko,NZL,male,1997-04-23,1.67,65.0,golf,0,1,0 -365605339,Lydia Paterson,USA,female,1996-10-17,1.63,69.0,shooting,0,0,0 -928938439,Lydia Williams,AUS,female,1988-05-13,1.75,65.0,football,0,0,0 -893383982,Lyes Bouyacoub,ALG,male,1983-04-03,1.85,100.0,judo,0,0,0 -529259909,Lynda Kiejko,CAN,female,1980-09-13,1.57,51.0,shooting,0,0,0 -146583581,Lynda Morales,PUR,female,1988-05-20,1.88,74.0,volleyball,0,0,0 -356436591,Lyndal Oatley,AUS,female,1980-06-27,1.76,61.0,equestrian,0,0,0 -127921496,Lynett Mutokuto,ZIM,female,1988-09-01,1.61,56.0,football,0,0,0 -437144743,Lynique Prinsloo,RSA,female,1991-03-30,1.69,60.0,athletics,0,0,0 -69224048,Lynn Symansky,USA,female,1983-03-19,1.71,56.0,equestrian,0,0,0 -451602579,Lynsey Sharp,GBR,female,1990-07-11,1.74,58.0,athletics,0,0,0 -616838605,Lyubomira Kazanova,BUL,female,1996-05-23,1.77,56.0,gymnastics,0,0,1 -698011015,Lyubov Shutova,RUS,female,1983-06-25,1.77,64.0,fencing,0,0,1 -486040260,M Bar N Diaye,FRA,male,1983-06-15,1.93,84.0,taekwondo,0,0,0 -365973319,Maaike Head,NED,female,1983-09-11,1.73,59.0,rowing,1,0,0 -98846520,Maarten Brzoskowski,NED,male,1995-09-19,1.84,79.0,aquatics,0,0,0 -29701462,Maartje Paumen,NED,female,1985-09-19,1.76,66.0,hockey,0,1,0 -908862793,Maayan Davidovich,ISR,female,1988-05-21,1.67,57.0,sailing,0,0,0 -872823363,Macarena Aguilar,ESP,female,1985-03-12,1.7,67.0,handball,0,0,0 -308383815,Macarena Gandulfo,ARG,female,1993-11-03,1.74,73.0,handball,0,0,0 -985732268,Macarena Sans,ARG,female,1996-11-20,1.65,64.0,handball,0,0,0 -38791158,Machel Cedenio,TTO,male,1995-09-06,1.83,70.0,athletics,0,0,0 -280514109,Maciej Bodnar,POL,male,1985-03-07,1.86,78.0,cycling,0,0,0 -703301693,Maciej Okreglak,POL,male,1993-07-05,1.76,73.0,canoe,0,0,0 -595036490,Maciej Sarnacki,POL,male,1987-02-10,2.0,130.0,judo,0,0,0 -499556541,Mack Darragh,CAN,male,1993-12-08,1.85,79.0,aquatics,0,0,0 -164336979,Mack Horton,AUS,male,1996-04-25,1.9,88.0,aquatics,1,0,0 -831329094,Mackenzie Arnold,AUS,female,1994-02-25,1.79,70.0,football,0,0,0 -359535568,Mackenzie Brown,USA,female,1995-03-14,1.77,74.0,archery,0,0,0 -243963793,Madai Perez,MEX,female,1980-02-02,1.58,46.0,athletics,0,0,0 -161590850,Madalina Beres,ROU,female,1993-07-03,1.86,75.0,rowing,0,0,1 -36358065,Madara Palameika,LAT,female,1987-06-18,1.84,76.0,athletics,0,0,0 -287974720,Maddie Hinch,GBR,female,1988-10-08,1.68,62.0,hockey,1,0,0 -713460320,Maddison Keeney,AUS,female,1996-05-23,1.67,62.0,aquatics,0,0,1 -72981815,Madelein Meppelink,NED,female,1989-11-29,1.83,75.0,volleyball,0,0,0 -946141261,Madeleine Edmunds,AUS,female,1992-01-03,1.87,81.0,rowing,0,0,0 -37020908,Madeline Dirado,USA,female,1993-04-05,1.76,64.0,aquatics,2,1,1 -341947091,Madeline Groves,AUS,female,1995-05-25,1.79,66.0,aquatics,0,2,0 -891088066,Madeline Heiner Hills,AUS,female,1987-05-15,1.74,53.0,athletics,0,0,0 -473250088,Madeline Musselman,USA,female,1998-06-16,1.81,65.0,aquatics,1,0,0 -81058004,Madiea Ghafoor,NED,female,1992-09-09,1.69,55.0,athletics,0,0,0 -996222405,Madison Hughes,USA,male,1992-10-26,1.73,79.0,rugby sevens,0,0,0 -527332545,Madison Keys,USA,female,1995-02-17,1.78,68.0,tennis,0,0,0 -376474889,Madison Kocian,USA,female,1997-06-15,1.58,46.0,gymnastics,1,1,0 -540102843,Madison Wilson,AUS,female,1994-05-31,1.79,61.0,aquatics,1,1,0 -448658832,Madonna Blyth,AUS,female,1985-11-30,1.65,60.0,hockey,0,0,0 -877108411,Mads Christiansen,DEN,male,1986-05-03,1.97,93.0,handball,1,0,0 -397456210,Mads Glaesner,DEN,male,1988-10-18,1.91,81.0,aquatics,0,0,0 -13506887,Mads Hendeliowitz,SWE,male,1982-01-14,1.78,76.0,equestrian,0,0,0 -653799383,Mads Mensah Larsen,DEN,male,1991-08-12,1.88,106.0,handball,1,0,0 -74306261,Mads Rasmussen,DEN,male,1981-11-24,1.83,75.0,rowing,0,0,0 -585960313,Mael Ambonguilat,GAB,male,1997-11-09,1.7,65.0,aquatics,0,0,0 -431358529,Magaly Bonilla,ECU,female,1992-02-08,1.52,54.0,athletics,0,0,0 -76314566,Magda Alfredo Cazanga,ANG,female,1991-05-28,1.72,54.0,handball,0,0,0 -732221079,Magda Linette,POL,female,1992-02-12,1.71,59.0,tennis,0,0,0 -302906626,Magdalena Eriksson,SWE,female,1993-09-08,1.72,67.0,football,0,1,0 -635407788,Magdalena Fularczyk-Kozlowska,POL,female,1986-09-16,1.73,70.0,rowing,1,0,0 -587247307,Magdalena Garro,ARG,female,1989-02-18,1.6,60.0,canoe,0,0,0 -537197914,Magdalena Lobnig,AUT,female,1990-07-19,1.8,71.0,rowing,0,0,0 -91993578,Magdalena Ruth Alex Moshi,TAN,female,1990-11-30,1.69,65.0,aquatics,0,0,0 -296260449,Magdiel Estrada,CUB,male,1994-08-26,1.76,73.0,judo,0,0,0 -28493203,Maggie Hogan,USA,female,1979-01-01,1.71,61.0,canoe,0,0,0 -610096820,Maggie Malone,USA,female,1993-12-30,1.73,77.0,athletics,0,0,0 -151348032,Maggie Steffens,USA,female,1993-06-03,1.73,74.0,aquatics,1,0,0 -558981183,Magnus Kirt,EST,male,1990-04-10,1.92,90.0,athletics,0,0,0 -344707477,Magnus Westermann,DEN,male,1995-03-13,1.94,94.0,aquatics,0,0,0 -829849936,Magomed Idrisovitch Ibragimov,UZB,male,1985-06-02,1.83,97.0,wrestling,0,0,1 -122833062,Magomed Musaev,KGZ,male,1989-03-11,1.81,95.0,wrestling,0,0,0 -628176825,Magomedmurad Gadzhiev,POL,male,1988-02-15,1.65,65.0,wrestling,0,0,0 -218620523,Maha Abdel Salam,EGY,female,1998-06-08,1.72,61.0,aquatics,0,0,0 -366810881,Maha Amer,EGY,female,1999-03-27,1.65,54.0,aquatics,0,0,0 -479172585,Maha Haddioui,MAR,female,1988-05-15,1.68,67.0,golf,0,0,0 -40811723,Mahama Cho,GBR,male,1989-08-16,1.96,100.0,taekwondo,0,0,0 -514748025,Mahaman Smaila,CMR,male,1986-02-28,1.78,,boxing,0,0,0 -219888752,Mahammadrasul Majidov,AZE,male,1986-09-27,1.9,,boxing,0,0,0 -34377778,Maharu Yoshimura,JPN,male,1993-08-03,1.77,61.0,table tennis,0,1,0 -874841774,Mahau Suguimati,BRA,male,1984-11-13,1.84,78.0,athletics,0,0,0 -448445626,Mahdi Kamil,IRQ,male,1995-01-06,1.7,60.0,football,0,0,0 -452893279,Mahdi Khodabakhshi,IRI,male,1991-04-21,1.92,87.0,taekwondo,0,0,0 -167441839,Mahdi Marandi,IRI,male,1986-05-12,1.72,69.0,volleyball,0,0,0 -472129595,Mahe Drysdale,NZL,male,1978-11-19,2.0,102.0,rowing,1,0,0 -150879921,Mahiedine Mekhissi,FRA,male,1985-03-15,1.9,75.0,athletics,0,0,1 -526960763,Mahlagha Jambozorg,IRI,female,1991-08-15,1.69,80.0,shooting,0,0,0 -74259823,Mahmood Haji,BRN,male,1991-03-11,1.75,69.0,shooting,0,0,0 -782024861,Mahmoud Abdelaal,EGY,male,1992-01-01,1.76,,boxing,0,0,0 -905443127,Mahmoud Fawzy Rashad Sebie,EGY,male,1992-06-20,1.65,75.0,wrestling,0,0,0 -381110402,Mahmoud Khalil,EGY,male,1991-06-01,1.92,85.0,handball,0,0,0 -352998140,Mahmoud Samimi,IRI,male,1988-09-18,1.86,90.0,athletics,0,0,0 -940342444,Mahsa Javar,IRI,female,1994-05-12,1.73,62.0,rowing,0,0,0 -192746204,Mai Ito,JPN,female,1984-05-23,1.56,41.0,athletics,0,0,0 -859543886,Mai Murakami,JPN,female,1996-08-05,1.46,48.0,gymnastics,0,0,0 -217231294,Mai Nakamura,JPN,female,1989-01-13,1.63,50.0,aquatics,0,0,1 -197311311,Mai Yamaguchi,JPN,female,1983-07-03,1.76,62.0,volleyball,0,0,0 -284015405,Maia Agerup,NOR,female,1995-06-22,,,sailing,0,0,0 -699569335,Maialen Chourraut,ESP,female,1983-03-08,1.61,55.0,canoe,1,0,0 -358834161,Maica Garcia Godoy,ESP,female,1990-10-17,1.88,90.0,aquatics,0,0,0 -583991719,Maicel Uibo,EST,male,1992-12-27,1.88,84.0,athletics,0,0,0 -587074263,Maicol Verzotto,ITA,male,1988-05-24,1.72,68.0,aquatics,0,0,0 -202168250,Maicon Siqueira,BRA,male,1993-01-09,1.9,90.0,taekwondo,0,0,1 -219639057,Maik dos Santos,BRA,male,1980-09-06,1.8,88.0,handball,0,0,0 -323417214,Maikel van der Vleuten,NED,male,1988-02-10,1.7,60.0,equestrian,0,0,0 -288399104,Maila Machado,BRA,female,1981-01-22,1.67,67.0,athletics,0,0,0 -638173103,Maimouna Diarra,SEN,female,1991-01-30,1.98,90.0,basketball,0,0,0 -768555779,Mairaj Ahmad Khan,IND,male,1975-11-02,1.81,82.0,shooting,0,0,0 -572989050,Maizurah Abdul Rahim,BRU,female,1999-04-15,1.47,50.0,athletics,0,0,0 -448106253,Maja Mihalinec,SLO,female,1989-12-17,1.68,65.0,athletics,0,0,0 -713439185,Maja Neuenschwander,SUI,female,1980-02-13,1.68,53.0,athletics,0,0,0 -959018491,Maja Ognjenovic,SRB,female,1984-08-06,1.83,67.0,volleyball,0,1,0 -745686878,Maja Siegenthaler,SUI,female,1992-11-11,1.72,66.0,sailing,0,0,0 -793000755,Maja Wloszczowska,POL,female,1983-11-09,1.7,54.0,cycling,0,1,0 -435088187,Majd Eddin Ghazal,SYR,male,1987-04-21,2.05,72.0,athletics,0,0,0 -613581224,Majda Mehmedovic,MNE,female,1990-05-25,1.7,67.0,handball,0,0,0 -338727227,Majlinda Kelmendi,KOS,female,1991-05-09,1.62,52.0,judo,1,0,0 -925805828,Maka Unufe,USA,male,1991-09-28,1.88,92.0,rugby sevens,0,0,0 -565676183,Makenzie Fischer,USA,female,1997-03-29,1.86,74.0,aquatics,1,0,0 -8530170,Maki Sakaguchi,JPN,female,1989-06-08,1.58,56.0,hockey,0,0,0 -777674143,Maki Takada,JPN,female,1989-08-23,1.83,78.0,basketball,0,0,0 -870613136,Makiko Tomita,JPN,female,1991-08-02,1.7,68.0,rugby sevens,0,0,0 -548581122,Makoto Tomizawa,JPN,male,1984-07-19,1.81,73.0,sailing,0,0,0 -256394566,Makoura Keita,GUI,female,1994-11-01,1.75,71.0,athletics,0,0,0 -841352991,Makrem Missaoui,TUN,male,1981-02-14,1.88,98.0,handball,0,0,0 -501195133,Maksim Inic,MNE,male,1996-05-26,1.86,80.0,aquatics,0,0,0 -950293205,Maksim Manukyan,ARM,male,1987-12-10,1.77,86.0,wrestling,0,0,0 -545871127,Maksim Niastsiarenka,BLR,male,1992-09-01,1.93,82.0,athletics,0,0,0 -244208041,Maksim Oberemko,RUS,male,1978-01-25,1.86,73.0,sailing,0,0,0 -608604431,Maksym Averin,AZE,male,1985-11-28,1.89,75.0,cycling,0,0,0 -607942437,Maksym Dolgov,UKR,male,1996-06-16,1.76,71.0,aquatics,0,0,0 -204225713,Maksym Khvorost,UKR,male,1982-07-15,1.86,85.0,fencing,0,0,0 -563022056,Maksym Semiankiv,UKR,male,1992-01-20,1.72,63.0,gymnastics,0,0,0 -157150042,Malaika Mihambo,GER,female,1994-02-03,1.7,55.0,athletics,0,0,0 -394127915,Malek Jaziri,TUN,male,1984-01-20,1.85,82.0,tennis,0,0,0 -422468531,Malgorzata Bialecka,POL,female,1988-04-02,1.64,54.0,sailing,0,0,0 -205523142,Malgorzata Holub,POL,female,1992-10-30,1.68,57.0,athletics,0,0,0 -607442240,Malgorzata Kozaczuk,POL,female,1988-06-06,1.7,60.0,fencing,0,0,0 -159953794,Malgorzata Wojtyra,POL,female,1989-09-21,1.76,70.0,cycling,0,0,0 -185574911,Malgorzta Jasinska,POL,female,1984-01-18,1.69,57.0,cycling,0,0,0 -980102210,Malika Akkaoui,MAR,female,1987-12-25,1.6,49.0,athletics,0,0,0 -656054953,Malin Baryard-Johnsson,SWE,female,1975-04-10,1.72,52.0,equestrian,0,0,0 -819426524,Malin Johanna Mattsson,SWE,female,1988-05-02,1.69,63.0,wrestling,0,0,0 -258262021,Malin Westerheim,NOR,female,1993-11-10,,,shooting,0,0,0 -295829504,Malina Calugareanu,ROU,female,1996-09-15,1.76,66.0,fencing,0,0,0 -91778108,Mallory Pugh,USA,female,1998-04-29,1.62,54.0,football,0,0,0 -997297757,Malte Jakschik,GER,male,1993-08-03,1.94,93.0,rowing,0,1,0 -759848453,Malwina Kopron,POL,female,1994-11-16,,,athletics,0,0,0 -4025069,Mamadama Bangoura,GUI,female,1993-11-10,1.76,63.0,judo,0,0,0 -590513028,Mamadou Cherif Dia,MLI,male,1984-10-16,1.8,70.0,athletics,0,0,0 -744094711,Mamadou Kasse Hann,FRA,male,1986-10-10,1.89,79.0,athletics,0,0,0 -362133096,Mamdouh Abdelrehim,EGY,male,1989-08-05,2.07,90.0,volleyball,0,0,0 -771530809,Mamdouh Taha Abouebaid,EGY,male,1988-01-01,1.89,80.0,handball,0,0,0 -765515466,Mame Marie Sy,SEN,female,1985-03-25,1.85,82.0,basketball,0,0,0 -224743356,Mame-Ibra Anne,FRA,male,1989-11-07,1.84,70.0,athletics,0,0,0 -797336359,Mamed Ibragimov,KAZ,male,1992-06-09,1.71,97.0,wrestling,0,0,0 -782337187,Mamello Makhabane,RSA,female,1988-02-24,1.59,53.0,football,0,0,0 -693611627,Mami Umeki,JPN,female,1994-12-06,1.74,78.0,judo,0,0,0 -119339549,Mamina Kone,CIV,female,1988-12-27,1.73,,taekwondo,0,0,0 -214760754,Mammadali Mehdiyev,AZE,male,1993-04-09,1.88,90.0,judo,0,0,0 -795584159,Mamoudou Eliman Hanne,FRA,male,1988-03-06,1.86,72.0,athletics,0,0,0 -520226601,Man Asaad,SYR,male,1993-11-20,1.9,143.0,weightlifting,0,0,0 -684910206,Man Sun,CHN,male,1995-08-22,1.88,70.0,rowing,0,0,0 -443697349,Man Wai Vivian Kong,HKG,female,1994-02-08,1.78,66.0,fencing,0,0,0 -639340405,Man Yang,CHN,female,1995-11-02,1.86,73.0,football,0,0,0 -489273020,Manami Doi,JPN,female,1993-08-29,1.67,64.0,sailing,0,0,0 -919263114,Manavjit Singh Sandhu,IND,male,1976-11-03,1.92,90.0,shooting,0,0,0 -320003911,Mandakhnaran Ganzorig,MGL,male,1986-05-11,1.65,65.0,wrestling,0,0,0 -878393556,Mandy Bujold,CAN,female,1987-07-25,1.6,,boxing,0,0,0 -136508791,Mandy Islacker,GER,female,1988-08-08,1.64,55.0,football,1,0,0 -235018279,Mandy Mulder,NED,female,1987-08-03,1.7,61.0,sailing,0,0,0 -41706411,Mane,POR,male,1994-03-11,1.72,80.0,football,0,0,0 -977840087,Manel Terraza,ESP,male,1990-05-11,1.84,78.0,hockey,0,0,0 -804165037,Manfredi Rizza,ITA,male,1991-04-26,1.8,89.0,canoe,0,0,0 -974903059,Mangala Samarakoon,SRI,male,1980-08-26,1.62,60.0,shooting,0,0,0 -964160887,Manich Bech,DEN,male,1993-01-13,1.7,68.0,football,0,0,0 -12987305,Manika Batra,IND,female,1995-06-15,1.79,63.0,table tennis,0,0,0 -879168754,Manila Flamini,ITA,female,1987-09-18,1.63,57.0,aquatics,0,0,0 -643799158,Manish Singh,IND,male,1991-05-05,1.74,65.0,athletics,0,0,0 -408462176,Manoel Dall Igna,FRA,male,1985-03-12,1.83,92.0,rugby sevens,0,0,0 -761454606,Manoj Kumar,IND,male,1986-12-10,1.75,,boxing,0,0,0 -283175998,Manon Brunet,FRA,female,1996-02-07,1.65,55.0,fencing,0,0,0 -672441857,Manon Hostens,FRA,female,1994-06-07,1.69,62.0,canoe,0,0,0 -969062147,Manon Houette,FRA,female,1992-07-02,1.68,69.0,handball,0,1,0 -620704693,Manon Valentino,FRA,female,1990-08-25,1.72,64.0,cycling,0,0,0 -14769885,Manpreet Kaur,IND,female,1990-07-06,1.7,89.0,athletics,0,0,0 -479145600,Manpreet Singh,IND,male,1992-06-26,1.72,69.0,hockey,0,0,0 -748912024,Manqi Ge,CHN,female,1997-10-13,1.62,52.0,athletics,0,0,0 -857446492,Manrique Larduet,CUB,male,1996-07-10,1.6,63.0,gymnastics,0,0,0 -690679655,Mantas Kalnietis,LTU,male,1986-09-06,1.94,90.0,basketball,0,0,0 -737047784,Manu Attri,IND,male,1992-12-31,1.72,73.0,badminton,0,0,0 -949112523,Manu Ginobili,ARG,male,1977-07-28,1.98,91.0,basketball,0,0,0 -933012630,Manuel Alexander Torres,VEN,male,1987-01-20,1.76,66.0,wrestling,0,0,0 -653187190,Manuel Brunet,ARG,male,1985-11-16,1.79,79.0,hockey,1,0,0 -621320289,Manuel Cappai,ITA,male,1992-10-09,1.68,,boxing,0,0,0 -188149865,Manuel Esteban Soto,COL,male,1994-01-28,1.7,62.0,athletics,0,0,0 -454149473,Manuel Fernandez Saro,ESP,male,1975-01-27,1.78,67.0,equestrian,0,0,0 -896925712,Manuel Fumic,GER,male,1982-03-30,1.73,68.0,cycling,0,0,0 -53279336,Manuel Huerta,PUR,male,1984-03-22,1.68,56.0,triathlon,0,0,0 -715849692,Manuel Lajud,MEX,male,1993-12-25,1.88,85.0,football,0,0,0 -342632387,Manuel Lelo,ANG,male,1987-08-18,1.82,73.0,sailing,0,0,0 -91551077,Manuel Rodas Ochoa,GUA,male,1984-07-05,1.71,67.0,cycling,0,0,0 -484470226,Manuel Strlek,CRO,male,1988-12-01,1.82,82.0,handball,0,0,0 -16557121,Manuel Tavares de Almeida,BRA,male,1993-12-11,1.82,78.0,equestrian,0,0,0 -122886715,Manuela Pizzo,ARG,female,1991-11-13,1.77,73.0,handball,0,0,0 -869069541,Manuela Soccol,BEL,female,1988-06-16,1.57,42.0,athletics,0,0,0 -456553330,Manueli Tulo,FIJ,male,1990-03-25,1.51,56.0,weightlifting,0,0,0 -949340839,Manuella Lyrio,BRA,female,1989-07-27,1.63,57.0,aquatics,0,0,0 -990150557,Maor Tiyouri,ISR,female,1990-08-13,1.48,44.0,athletics,0,0,0 -947717856,Maoulida Daroueche,COM,male,1990-02-07,1.77,60.0,athletics,0,0,0 -547395563,Mara Abbott,USA,female,1985-11-14,1.63,52.0,cycling,0,0,0 -336448292,Marc Lopez,ESP,male,1982-07-31,1.74,70.0,tennis,1,0,0 -201149666,Marc Minguell Alferez,ESP,male,1985-01-14,1.85,94.0,aquatics,0,0,0 -591640221,Marc Mundell,RSA,male,1983-07-07,1.89,77.0,athletics,0,0,0 -317700233,Marc Odenthal,GER,male,1991-01-25,1.8,94.0,judo,0,0,0 -180431479,Marc Roca Barcelo,ESP,male,1988-01-21,1.88,92.0,aquatics,0,0,0 -471210431,Marc Salles,ESP,male,1987-05-06,1.7,64.0,hockey,0,0,0 -777668281,Marc Sanchez Torrens,ESP,male,1992-11-06,1.88,75.0,aquatics,0,0,0 -799451393,Marc Zwiebler,GER,male,1984-03-13,1.81,75.0,badminton,0,0,0 -970688075,Marc-Antoine Olivier,FRA,male,1996-06-18,1.83,64.0,aquatics,0,0,1 -237044090,Marcel Hacker,GER,male,1977-04-29,1.96,101.0,rowing,0,0,0 -706271089,Marcel Lomnicky,SVK,male,1987-07-06,1.77,106.0,athletics,0,0,0 -749012390,Marcel Nguyen,GER,male,1987-09-08,1.62,55.0,gymnastics,0,0,0 -380803494,Marcela Krinke Susmelj,SUI,female,1965-10-18,1.75,54.0,equestrian,0,0,0 -80452686,Marcela Maric,CRO,female,1996-10-18,1.58,55.0,aquatics,0,0,0 -474468976,Marcelinho Huertas,BRA,male,1983-05-25,1.91,90.0,basketball,0,0,0 -919986691,Marcello Miani,ITA,male,1984-03-05,1.83,72.0,rowing,0,0,0 -855138617,Marcelo Aguirre,PAR,male,1993-01-21,1.74,66.0,table tennis,0,0,0 -627766210,Marcelo Alberto Acosta Jimenez,ESA,male,1996-07-11,,,aquatics,0,0,0 -225061272,Marcelo Chierighini,BRA,male,1991-01-15,1.9,86.0,aquatics,0,0,0 -850637390,Marcelo Espinal,HON,male,1993-02-24,1.75,78.0,football,0,0,0 -118521548,Marcelo Melo,BRA,male,1983-09-23,2.0,87.0,tennis,0,0,0 -549105619,Marcelo Pereira,HON,male,1995-05-27,1.81,70.0,football,0,0,0 -279325237,Marcia Vidiaux,CUB,female,1999-07-21,1.43,41.0,gymnastics,0,0,0 -278352429,Marcin Brzezinski,POL,male,1984-01-06,1.94,96.0,rowing,0,0,0 -884298028,Marcin Krukowski,POL,male,1992-06-14,1.85,96.0,athletics,0,0,0 -838211917,Marcin Lewandowski,POL,male,1987-06-13,1.79,64.0,athletics,0,0,0 -8595112,Marcin Matkowski,POL,male,1981-01-15,1.85,90.0,tennis,0,0,0 -746849065,Marcin Pochwala,POL,male,1984-02-14,1.82,80.0,canoe,0,0,0 -382951256,Marcin Stolarski,POL,male,1996-01-04,1.87,79.0,aquatics,0,0,0 -166826094,Marcio Appel,BRA,male,1979-01-01,1.72,73.0,equestrian,0,0,0 -692236481,Marcio Carvalho Jorge,BRA,male,1975-01-28,1.86,80.0,equestrian,0,0,0 -432314163,Marcio Teles,BRA,male,1994-01-27,1.8,68.0,athletics,0,0,0 -995865749,Marco Antonio Rodriguez,BOL,male,1994-01-24,1.8,66.0,athletics,0,0,0 -271177180,Marco Aurelio Fontana,ITA,male,1984-10-12,1.72,68.0,cycling,0,0,0 -527776420,Marco Belotti,ITA,male,1988-11-29,1.84,70.0,aquatics,0,0,0 -171354772,Marco Bueno,MEX,male,1994-03-31,1.82,68.0,football,0,0,0 -406226635,Marco DI COSTANZO,ITA,male,1992-06-09,1.84,87.0,rowing,0,0,1 -483777206,Marco Fichera,ITA,male,1993-09-04,1.8,74.0,fencing,0,1,0 -156642960,Marco Galiazzo,ITA,male,1983-05-07,1.78,98.0,archery,0,0,0 -331908129,Marco Grael,BRA,male,1989-06-09,1.81,82.0,sailing,0,0,0 -886667539,Marco Grimalt,CHI,male,1989-07-11,1.96,92.0,volleyball,0,0,0 -975072988,Marco Innocenti,ITA,male,1978-08-16,1.78,97.0,shooting,0,1,0 -861351035,Marco Koch,GER,male,1990-01-25,1.85,83.0,aquatics,0,0,0 -379037954,Marco Lingua,ITA,male,1978-06-04,1.77,116.0,athletics,0,0,0 -225215794,Marco Orsi,ITA,male,1990-12-11,1.89,89.0,aquatics,0,0,0 -778682814,Marco de Luca,ITA,male,1981-05-12,1.88,70.0,athletics,0,0,0 -491996482,Marco de Nicolo,ITA,male,1976-09-30,1.8,85.0,shooting,0,0,0 -473758676,Marco del Lungo,ITA,male,1990-03-01,1.9,97.0,aquatics,0,0,1 -995047482,Marcos Delia,ARG,male,1992-04-08,2.09,105.0,basketball,0,0,0 -842606539,Marcos Freitas,POR,male,1988-04-08,1.8,75.0,table tennis,0,0,0 -634165369,Marcos Macedo,BRA,male,1990-09-09,1.89,85.0,aquatics,0,0,0 -408897211,Marcos Madrid,MEX,male,1986-09-06,1.8,68.0,table tennis,0,0,0 -23863163,Marcos Pasin,BRA,male,1993-05-27,1.73,69.0,hockey,0,0,0 -372806643,Marcos Poggi,ESP,male,1987-08-31,1.82,86.0,rugby sevens,0,0,0 -481791889,Marcos Pulido Rodriguez,MEX,male,1995-08-18,1.79,74.0,canoe,0,0,0 -414759597,Marcus Child,NZL,male,1991-03-02,1.82,81.0,hockey,0,0,0 -728513422,Marcus D'almeida,BRA,male,1998-01-30,1.83,90.0,archery,0,0,0 -84514689,Marcus Daniell,NZL,male,1989-11-09,1.9,76.0,tennis,0,0,0 -22207553,Marcus Duncan,TTO,male,1986-12-04,1.77,,athletics,0,0,0 -820182386,Marcus Ellis,GBR,male,1989-09-14,1.75,80.0,badminton,0,0,1 -456233177,Marcus Fraser,AUS,male,1978-07-26,1.83,89.0,golf,0,0,0 -282297654,Marcus Gross,GER,male,1989-09-28,1.82,85.0,canoe,2,0,0 -673529044,Marcus Mepstead,GBR,male,1990-05-11,1.83,78.0,fencing,0,0,0 -14800860,Marcus Nyman,SWE,male,1990-08-14,1.89,90.0,judo,0,0,0 -164432341,Marcus Svensson,SWE,male,1990-03-22,1.76,68.0,shooting,0,1,0 -799844777,Marcus Vinicius Marquinhos,BRA,male,1984-05-31,2.07,104.0,basketball,0,0,0 -855460068,Marcus Walz,ESP,male,1994-10-03,1.84,82.0,canoe,1,0,0 -372436745,Marcus Watson,GBR,male,1991-06-27,1.77,88.0,rugby sevens,0,1,0 -501561453,Mare Dibaba,ETH,female,1989-10-20,1.56,45.0,athletics,0,0,1 -655236481,Mareen Kraeh,GER,female,1984-01-28,1.61,54.0,judo,0,0,0 -377392612,Mareike Adams,GER,female,1990-02-27,1.74,73.0,rowing,0,0,0 -182348270,Marek Sindler,CZE,male,1992-07-21,1.86,74.0,canoe,0,0,0 -134225037,Mareks Arents,LAT,male,1986-08-06,1.9,85.0,athletics,0,0,0 -195571720,Marestella Sunang,PHI,female,1981-02-20,1.57,54.0,athletics,0,0,0 -188511447,Maret Balkestein-Grothues,NED,female,1988-09-16,1.8,68.0,volleyball,0,0,0 -919199035,Margaret Adeoye,GBR,female,1985-04-22,1.74,68.0,athletics,0,0,0 -978084317,Margaret Bamgbose,NGR,female,1993-10-19,1.71,63.0,athletics,0,0,0 -780545996,Margaret Nyairera Wambui,KEN,female,1995-09-15,,66.0,athletics,0,0,1 -644993560,Margarita Hernandez,MEX,female,1985-12-03,1.5,44.0,athletics,0,0,0 -592591211,Margarita Mamun,RUS,female,1995-10-31,1.7,50.0,gymnastics,1,0,0 -822811465,Margarita Mukasheva,KAZ,female,1986-01-04,1.65,50.0,athletics,0,0,0 -330489011,Margarita Yelisseyeva,KAZ,female,1992-07-20,1.5,48.0,weightlifting,0,0,0 -936647883,Margaux Chretien,FRA,female,1992-12-11,1.72,61.0,aquatics,0,0,0 -587959959,Margaux Fabre,FRA,female,1992-10-02,1.73,58.0,aquatics,0,0,0 -687712206,Margaux Isaksen,USA,female,1991-10-07,1.78,60.0,modern pentathlon,0,0,0 -16156775,Margherita Magnani,ITA,female,1987-02-26,1.6,45.0,athletics,0,0,0 -354533,Margherita Panziera,ITA,female,1995-08-12,1.8,65.0,aquatics,0,0,0 -618086900,Margit Vanek,HUN,female,1986-02-25,1.78,58.0,triathlon,0,0,0 -999465722,Margot van Geffen,NED,female,1989-11-23,1.72,63.0,hockey,0,1,0 -713835897,Margret Hassan,SSD,female,1997-08-12,1.6,55.0,athletics,0,0,0 -707146783,Marharyta Makhneva,BLR,female,1992-02-13,1.81,67.0,canoe,0,0,1 -188153732,Marhinde Verkerk,NED,female,1985-11-21,1.72,78.0,judo,0,0,0 -297444162,Mari Molid,NOR,female,1990-08-08,1.78,,handball,0,0,1 -207842990,Mari Rabie,RSA,female,1986-09-10,1.71,60.0,triathlon,0,0,0 -192881723,Maria Alexandra Escobar Guerrero,ECU,female,1980-07-17,1.61,57.0,weightlifting,0,0,0 -220995361,Maria Andrade,CPV,female,1993-03-19,1.69,49.0,taekwondo,0,0,0 -289043459,Maria Andrejczyk,POL,female,1996-03-09,1.74,77.0,athletics,0,0,0 -60248392,Maria Barrionuevo,ARG,female,1984-05-16,1.71,58.0,hockey,0,0,0 -437720766,Maria Belen Perez Maurice,ARG,female,1985-07-12,1.8,68.0,fencing,0,0,0 -581742825,Maria Belimpasaki,GRE,female,1991-06-19,1.75,64.0,athletics,0,0,0 -671806829,Maria Bernabeu,ESP,female,1988-02-15,1.7,70.0,judo,0,0,0 -225216980,Maria Bernard,CAN,female,1993-04-06,1.65,53.0,athletics,0,0,0 -849987723,Maria Borisova,RUS,female,1997-07-28,1.84,95.0,aquatics,0,0,1 -173396847,Maria Branz,ARG,female,1990-02-06,1.7,68.0,sailing,0,0,0 -227880733,Maria Bruno,BRA,female,1992-08-28,1.6,54.0,aquatics,0,0,0 -129176268,Maria Camila Lopera Valle,COL,female,1995-04-18,1.54,54.0,rugby sevens,0,0,0 -548878175,Maria Campoy,ARG,female,1990-10-06,1.58,48.0,hockey,0,0,0 -641169323,Maria Casado,ESP,female,1985-12-25,1.67,63.0,rugby sevens,0,0,0 -680445450,Maria Clara Lobo,BRA,female,1998-09-03,1.67,55.0,aquatics,0,0,0 -971274141,Maria Czakova,SVK,female,1988-10-02,1.66,56.0,athletics,0,0,0 -95646367,Maria Dolgikh,RUS,female,1987-07-24,1.76,62.0,table tennis,0,0,0 -639719665,Maria Eduarda Miccuci,BRA,female,1995-06-07,1.67,56.0,aquatics,0,0,0 -42602097,Maria Elena Calle,ECU,female,1975-07-25,1.62,54.0,athletics,0,0,0 -166969862,Maria Elisabetta Marconi,ITA,female,1984-08-28,1.59,55.0,aquatics,0,0,0 -71121254,Maria Enrica Spacca,ITA,female,1986-03-20,1.64,55.0,athletics,0,0,0 -274533767,Maria Erdi,HUN,female,1998-02-18,1.75,67.0,sailing,0,0,0 -242817329,Maria Far,PAN,female,1998-01-06,1.63,130.0,aquatics,0,0,0 -395263582,Maria Fernanda Valdes Paris,CHI,female,1992-03-17,1.61,75.0,weightlifting,0,0,0 -821892550,Maria Gabriela Diaz,ARG,female,1981-01-02,1.6,53.0,cycling,0,0,0 -781399998,Maria Galikova,SVK,female,1980-08-21,1.61,50.0,athletics,0,0,0 -526855097,Maria Granatto,ARG,female,1995-04-21,1.58,58.0,hockey,0,0,0 -950854584,Maria Guadalupe Gonzalez,MEX,female,1989-01-09,1.62,47.0,athletics,0,1,0 -453427265,Maria Guadalupe Sanchez,MEX,female,1995-08-04,1.65,48.0,athletics,0,0,0 -141566641,Maria Jose Acosta Acosta,VEN,female,1991-11-26,1.72,69.0,wrestling,0,0,0 -722414167,Maria Kadobina,BLR,female,1997-02-04,1.74,51.0,gymnastics,0,0,0 -686608090,Maria Kurjo,GER,female,1989-12-10,1.58,52.0,aquatics,0,0,0 -813578958,Maria Leonor Tavares,POR,female,1985-09-24,1.65,55.0,athletics,0,0,0 -323047298,Maria Lopez,ESP,female,1990-02-16,1.7,62.0,hockey,0,0,0 -937896884,Maria Lopez de Equilaz,ESP,female,1984-07-12,1.64,56.0,hockey,0,0,0 -766954463,Maria Michta-Coffey,USA,female,1986-06-23,1.66,49.0,athletics,0,0,0 -82454552,Maria Mollestad,NOR,female,1992-07-23,,,sailing,0,0,0 -949792080,Maria Mutio,ARG,female,1984-11-20,1.71,68.0,hockey,0,0,0 -359873953,Maria Natalia Londa,INA,female,1990-10-29,1.65,56.0,athletics,0,0,0 -397999900,Maria Ortiz,ARG,female,1997-04-16,1.62,50.0,hockey,0,0,0 -80802864,Maria Paseka,RUS,female,1995-07-19,1.61,48.0,gymnastics,0,2,0 -649684578,Maria Peralta,ARG,female,1977-11-30,1.67,49.0,athletics,0,0,0 -546111531,Maria Perez,PUR,female,1989-04-01,1.67,69.0,judo,0,0,0 -917128574,Maria Portela,BRA,female,1988-01-14,1.58,70.0,judo,0,0,0 -902674669,Maria Prevolaraki,GRE,female,1991-12-21,1.63,57.0,wrestling,0,0,0 -887573602,Maria Ribera,ESP,female,1986-07-08,1.72,76.0,rugby sevens,0,0,0 -206006000,Maria Romanjuk,EST,female,1996-08-15,1.69,65.0,aquatics,0,0,0 -520469214,Maria Ruiz,ESP,female,1990-03-18,1.69,73.0,hockey,0,0,0 -865389407,Maria Selmaier,GER,female,1991-12-12,1.75,79.0,wrestling,0,0,0 -861477967,Maria Springwald,POL,female,1991-07-30,1.74,69.0,rowing,0,0,1 -960192083,Maria Suelen Altheman,BRA,female,1988-08-12,1.75,110.0,judo,0,0,0 -336010126,Maria Tejerina Mackern,ARG,female,1994-06-15,1.69,63.0,sailing,0,0,0 -975534750,Maria Tolkacheva,RUS,female,1997-08-08,1.76,53.0,gymnastics,1,0,0 -5915355,Maria Ugolkova,SUI,female,1989-07-18,1.73,65.0,aquatics,0,0,0 -655249498,Maria Ulitina,UKR,female,1991-11-05,1.78,65.0,badminton,0,0,0 -164601802,Maria Verchenova,RUS,female,1986-03-27,1.74,64.0,golf,0,0,0 -609643734,Maria Verschoor,NED,female,1994-04-22,1.64,58.0,hockey,0,1,0 -330387161,Maria Vilas Vidal,ESP,female,1996-05-31,1.68,64.0,aquatics,0,0,0 -9029196,Maria Wierzbowska,POL,female,1995-02-13,1.74,68.0,rowing,0,0,0 -375141341,Maria del Mar Jover,ESP,female,1988-04-21,1.67,61.0,athletics,0,0,0 -626430359,Maria del Pilar Pena Carrasco,ESP,female,1986-04-04,1.74,63.0,aquatics,0,0,0 -215800330,Maria del Rosario Espinoza Espinoza,MEX,female,1987-11-29,1.73,70.0,taekwondo,0,1,0 -271492907,Mariabenedicta Chigbolu,ITA,female,1989-07-27,1.72,52.0,athletics,0,0,0 -414150168,Mariafe Artacho del Solar,AUS,female,1993-10-24,1.74,70.0,volleyball,0,0,0 -177261311,Mariah Williams,AUS,female,1995-05-31,1.68,66.0,hockey,0,0,0 -990925025,Mariajo Uribe,COL,female,1990-02-27,1.68,65.0,golf,0,0,0 -406892958,Mariam Kromah,LBR,female,1994-01-01,,,athletics,0,0,0 -408046749,Mariama Djoulde Sow,GUI,female,2000-05-19,1.65,65.0,aquatics,0,0,0 -477864609,Mariama Mamoudou,NIG,female,1997-07-23,1.69,64.0,athletics,0,0,0 -342716060,Marian Dragulescu,ROU,male,1980-12-18,1.63,64.0,gymnastics,0,0,0 -84245800,Marian Kovacocy,SVK,male,1984-09-17,1.8,87.0,shooting,0,0,0 -6690107,Marian Oprea,ROU,male,1982-06-06,1.91,85.0,athletics,0,0,0 -699157085,Marian Urdabayeva,KAZ,female,1988-04-03,1.7,63.0,judo,0,0,0 -336224210,Mariana Cherdivara Esanu,MDA,female,1992-09-15,1.6,62.0,wrestling,0,0,0 -219248034,Mariana Cress,MHL,female,1998-08-12,1.58,52.0,athletics,0,0,0 -114574006,Mariana Duarte,BRA,female,1996-10-05,1.7,67.0,aquatics,0,0,0 -939892477,Mariana Duque-Marino,COL,female,1989-08-12,1.69,61.0,tennis,0,0,0 -123852277,Mariana Foglia Costa,URU,female,1982-06-28,1.73,69.0,sailing,0,0,0 -441871483,Mariana Pajon,COL,female,1991-10-10,1.58,50.0,cycling,1,0,0 -267541302,Mariana Ramalho,BRA,female,1987-08-17,,,rugby sevens,0,0,0 -583952076,Mariana Sahakian,LIB,female,1977-09-02,1.55,52.0,table tennis,0,0,0 -354121976,Mariana Silva,BRA,female,1990-02-22,1.73,63.0,judo,0,0,0 -472526288,Mariangela Perrupato,ITA,female,1988-09-15,1.73,56.0,aquatics,0,0,0 -385694443,Marianna Sastin,HUN,female,1983-07-10,1.61,65.0,wrestling,0,0,0 -813833760,Marianna Tolo,AUS,female,1989-07-02,1.96,86.0,basketball,0,0,0 -321680601,Marianne Skarpnord,NOR,female,1986-02-11,,,golf,0,0,0 -913351794,Marianne Vos,NED,female,1987-05-13,1.69,59.0,cycling,0,0,0 -610975661,Mariano Canepa,ARG,male,1987-05-07,1.85,98.0,handball,0,0,0 -393287429,Mariano Mastromarino,ARG,male,1982-09-15,1.69,56.0,athletics,0,0,0 -285433516,Maricet Espinosa,CUB,female,1990-01-02,1.63,63.0,judo,0,0,0 -76777877,Marie Gayot,FRA,female,1989-12-18,1.71,58.0,athletics,0,0,0 -849507397,Marie Laura Meza,CRC,female,1990-11-20,1.63,58.0,aquatics,0,0,0 -192037640,Marie Laure Delie,FRA,female,1988-01-29,1.72,65.0,football,0,0,0 -974746450,Marie Le Nepvou,FRA,female,1985-01-25,1.81,72.0,rowing,0,0,0 -494678680,Marie Mavers,GER,female,1991-02-13,1.7,67.0,hockey,0,0,1 -372606340,Marie Prouvensier,FRA,female,1994-02-09,1.65,52.0,handball,0,1,0 -34447682,Marie Riou,FRA,female,1981-08-21,1.71,68.0,sailing,0,0,0 -953237807,Marie Wattel,FRA,female,1997-06-02,1.81,71.0,aquatics,0,0,0 -230737913,Marie Yamaguchi,JPN,female,1989-10-22,1.59,58.0,rugby sevens,0,0,0 -351230490,Marie-Catherine Arnold,GER,female,1991-11-07,1.75,69.0,rowing,0,0,0 -851180853,Marie-Eve Beauchemin-Nadeau,CAN,female,1988-10-13,1.66,69.0,weightlifting,0,0,0 -223948924,Marie-Eve Nault,CAN,female,1982-02-16,1.7,64.0,football,0,0,0 -554344364,Marie-Florence Candassamy,FRA,female,1991-02-26,1.85,74.0,fencing,0,0,0 -322081073,Marie-Josee Ta Lou,CIV,female,1988-11-18,1.59,50.0,athletics,0,0,0 -2419718,Marie-Laurence Jungfleisch,GER,female,1990-10-07,1.82,68.0,athletics,0,0,0 -68066682,Marie-Louise Drager,GER,female,1981-04-11,1.7,59.0,rowing,0,0,0 -877716234,Marie-Sadio Rosche,SEN,female,1987-08-10,1.9,80.0,basketball,0,0,0 -307272735,Marie-Zelia Lafont,FRA,female,1987-01-09,1.71,63.0,canoe,0,0,0 -669462744,Mariel Zagunis,USA,female,1985-03-03,1.73,72.0,fencing,0,0,1 -462879813,Marielle Amant,FRA,female,1989-12-09,1.9,84.0,basketball,0,0,0 -132988064,Marielle Hall,USA,female,1992-01-28,1.61,50.0,athletics,0,0,0 -778284527,Mariely Sanchez,DOM,female,1988-12-30,1.61,52.0,athletics,0,0,0 -273450205,Mariia Shorets,RUS,female,1990-08-09,1.66,55.0,triathlon,0,0,0 -258800663,Mariia Shurochkina,RUS,female,1995-06-30,1.64,50.0,aquatics,1,0,0 -639025149,Marija Jovanovic,MNE,female,1985-12-26,1.81,77.0,handball,0,0,0 -892226840,Marija Marovic,CRO,female,1983-09-16,1.75,72.0,shooting,0,0,0 -604000736,Marija Vrajic,CRO,female,1976-09-23,1.69,51.0,athletics,0,0,0 -948844936,Marije van Hunenstijn,NED,female,1995-03-02,1.74,61.0,athletics,0,0,0 -259349407,Marika Popowicz-Drapala,POL,female,1988-04-28,1.64,55.0,athletics,0,0,0 -333749017,Marilson dos Santos,BRA,male,1977-08-06,1.74,58.0,athletics,0,0,0 -970045887,Marily dos Santos,BRA,female,1978-02-05,1.58,46.0,athletics,0,0,0 -500168497,Marin Cilic,CRO,male,1988-09-28,1.98,89.0,tennis,0,0,0 -461854211,Marin Draganja,CRO,male,1991-05-13,1.86,80.0,tennis,0,0,0 -161253255,Marina Aframeeva,RUS,female,1991-03-04,1.71,62.0,equestrian,0,0,0 -521412968,Marina Alabau Neira,ESP,female,1985-08-31,1.64,55.0,sailing,0,0,0 -430360045,Marina Arzamasova,BLR,female,1987-12-17,1.73,58.0,athletics,0,0,0 -276990393,Marina Bravo,ESP,female,1989-07-02,1.73,68.0,rugby sevens,0,0,0 -190849137,Marina Canetta,BRA,female,1989-04-01,1.62,51.0,archery,0,0,0 -38754507,Marina Canetti,BRA,female,1983-01-24,1.69,63.0,aquatics,0,0,0 -653594093,Marina Durunda,AZE,female,1997-06-12,1.7,51.0,gymnastics,0,0,0 -823803768,Marina Hmelevskaya,UZB,female,1990-07-30,1.65,50.0,athletics,0,0,0 -864041951,Marina Marghieva-Nikisenko,MDA,female,1986-06-28,1.86,82.0,athletics,0,0,0 -787075445,Marina Rajcic,MNE,female,1993-08-24,1.75,70.0,handball,0,0,0 -95985131,Marina Sudakova,RUS,female,1989-02-17,1.65,66.0,handball,1,0,0 -175326336,Marina Toribiong,PLW,female,1994-06-13,1.53,69.0,canoe,0,0,0 -488705922,Marina Zablith,BRA,female,1987-03-04,1.8,77.0,aquatics,0,0,0 -887926099,Marina de la Caridad Rodriguez Mitjan,CUB,female,1995-03-02,1.55,62.0,weightlifting,0,0,0 -744691889,Marine Boyer,FRA,female,2000-05-22,1.6,50.0,gymnastics,0,0,0 -178736852,Marine Brevet,FRA,female,1994-11-23,1.6,52.0,gymnastics,0,0,0 -617653407,Marine Johannes,FRA,female,1995-01-21,1.77,61.0,basketball,0,0,0 -513001410,Marine Jurbert,FRA,female,1992-12-11,1.64,53.0,gymnastics,0,0,0 -19543044,Mario Alfonso Bran,GUA,male,1989-10-17,1.68,64.0,athletics,0,0,0 -368806729,Mario Fernandez,ESP,male,1992-04-26,1.8,80.0,hockey,0,0,0 -266700854,Mario Gyr,SUI,male,1985-05-02,1.87,76.0,rowing,1,0,0 -224028378,Mario Hezonja,CRO,male,1995-02-25,2.03,100.0,basketball,0,0,0 -430914854,Mario Jose dos Santos Jr,BRA,male,1979-09-10,1.71,60.0,athletics,0,0,0 -601460358,Mario Leitner,AUT,male,1997-02-02,1.77,75.0,canoe,0,0,0 -384009312,Mario Luis Rivera Sanchez,CUB,male,1982-10-26,1.8,92.0,volleyball,0,0,0 -242810977,Mario Mola,ESP,male,1990-02-23,1.78,63.0,triathlon,0,0,0 -605135800,Mario Paonessa,ITA,male,1990-12-09,1.9,85.0,rowing,0,0,0 -506325009,Mario Todorovic,CRO,male,1988-10-11,1.85,82.0,aquatics,0,0,0 -31095617,Marion Lepert,USA,female,1995-09-18,1.78,61.0,sailing,0,0,0 -653316415,Marios Georgiou,CYP,male,1997-11-10,1.67,60.0,gymnastics,0,0,0 -63803519,Maris Strombergs,LAT,male,1987-03-10,1.86,90.0,cycling,0,0,0 -476920635,Marisa Dick,TTO,female,1997-05-26,1.53,47.0,gymnastics,0,0,0 -935870296,Marisa Lavanchy,SUI,female,1990-01-04,1.7,58.0,athletics,0,0,0 -257657428,Marisol Carratu,ARG,female,1986-07-15,1.74,85.0,handball,0,0,0 -792010625,Marisol Romero,MEX,female,1983-01-26,1.55,46.0,athletics,0,0,0 -296517803,Marissa Kurtimah,CAN,female,1994-05-25,1.57,57.0,athletics,0,0,0 -625766463,Marit Bouwmeester,NED,female,1988-06-17,1.77,68.0,sailing,1,0,0 -780954637,Marit Malm Frafjord,NOR,female,1985-11-25,1.82,,handball,0,0,1 -668487554,Maritza Guaman,ECU,female,1988-01-15,1.55,47.0,athletics,0,0,0 -650610457,Maritza Poncio,GUA,female,1994-12-03,1.58,41.0,athletics,0,0,0 -628714452,Marius Cocioran,ROU,male,1983-07-10,1.73,64.0,athletics,0,0,0 -472041377,Marius Cozmiuc,ROU,male,1992-09-07,1.97,94.0,rowing,0,0,0 -585219962,Marius Grigonis,LTU,male,1994-04-26,1.98,92.0,basketball,0,0,0 -288653875,Marius Ionescu,ROU,male,1984-12-18,1.7,55.0,athletics,0,0,0 -231726077,Marius Radu,ROU,male,1992-06-18,1.94,85.0,aquatics,0,0,0 -161847988,Marius Savelskis,LTU,male,1994-07-30,1.79,70.0,athletics,0,0,0 -278897276,Marius Ziukas,LTU,male,1985-06-29,1.85,72.0,athletics,0,0,0 -40046360,Mariusz Jurkiewicz,POL,male,1982-02-03,1.99,103.0,handball,0,0,0 -744484721,Mariusz Prudel,POL,male,1986-01-21,1.92,83.0,volleyball,0,0,0 -979280840,Mariya Dmitriyenko,KAZ,female,1988-03-24,1.67,56.0,shooting,0,0,0 -249371752,Mariya Korobitskaya,KGZ,female,1990-05-10,1.55,50.0,athletics,0,0,0 -778874000,Mariya Koroleva,USA,female,1990-04-10,1.66,55.0,aquatics,0,0,0 -478444296,Mariya Povkh,UKR,female,1989-01-08,1.66,64.0,canoe,0,0,0 -954387962,Mariya Ryemyen,UKR,female,1987-08-02,1.71,61.0,athletics,0,0,0 -819994350,Mariya Shatalova,UKR,female,1989-03-01,1.69,56.0,athletics,0,0,0 -951698977,Mariya Stadnik,AZE,female,1988-06-03,1.57,48.0,wrestling,0,1,0 -477823690,Mariya Telushkina,KAZ,female,1994-04-03,1.78,90.0,athletics,0,0,0 -510987328,Marjorie Mayans,FRA,female,1990-11-17,1.71,68.0,rugby sevens,0,0,0 -549331807,Marjory Nyaumwe,ZIM,female,1987-07-10,1.58,57.0,football,0,0,0 -129571712,Mark Aldred,GBR,male,1987-04-18,1.88,71.0,rowing,0,0,0 -251655789,Mark Bennett,GBR,male,1993-02-03,1.83,89.0,rugby sevens,0,1,0 -261061286,Mark Cavendish,GBR,male,1985-05-21,1.75,70.0,cycling,0,1,0 -968767341,Mark Dry,GBR,male,1987-10-11,1.84,112.0,athletics,0,0,0 -718324705,Mark English,IRL,male,1993-03-18,1.87,76.0,athletics,0,0,0 -175225943,Mark Gleghorne,GBR,male,1985-05-19,1.8,82.0,hockey,0,0,0 -412389237,Mark Knowles,AUS,male,1984-03-10,1.83,74.0,hockey,0,0,0 -343466585,Mark Kyle,IRL,male,1973-06-05,1.83,80.0,equestrian,0,0,0 -53785702,Mark Oldershaw,CAN,male,1983-02-07,1.86,94.0,canoe,0,0,0 -44374926,Mark Overgaard Madsen,DEN,male,1984-09-23,1.77,80.0,wrestling,0,1,0 -324844405,Mark Papp,HUN,male,1994-01-08,1.85,78.0,aquatics,0,0,0 -855611571,Mark Pearson,CAN,male,1987-06-18,1.81,76.0,hockey,0,0,0 -746387093,Mark Robertson,GBR,male,1984-12-30,1.89,96.0,rugby sevens,0,1,0 -780309052,Mark Todd,NZL,male,1956-03-01,1.9,78.0,equestrian,0,0,0 -338059475,Mark de Jonge,CAN,male,1984-02-15,1.8,91.0,canoe,0,0,0 -947055071,Markel Alberdi Sarobe,ESP,male,1991-10-22,1.87,76.0,aquatics,0,0,0 -461844557,Marketa Slukova,CZE,female,1988-06-28,1.8,68.0,volleyball,0,0,0 -670673572,Marko Arapovic,CRO,male,1996-07-20,2.07,105.0,basketball,0,0,0 -587055200,Marko Bagaric,QAT,male,1985-12-31,2.01,100.0,handball,0,0,0 -136688597,Marko Bezjak,SLO,male,1986-06-26,1.84,87.0,handball,0,0,0 -442704681,Marko Bijac,CRO,male,1991-01-12,2.01,85.0,aquatics,0,1,0 -624346920,Marko Blazhevski,MKD,male,1992-11-10,1.81,69.0,aquatics,0,0,0 -61318903,Marko Carrillo,PER,male,1988-07-04,1.84,75.0,shooting,0,0,0 -177653630,Marko Kopljar,CRO,male,1986-02-12,2.1,112.0,handball,0,0,0 -279090752,Marko Macan,CRO,male,1993-04-26,1.96,109.0,aquatics,0,1,0 -478905853,Marko Mamic,CRO,male,1994-03-06,2.02,109.0,handball,0,0,0 -341559685,Marko Marjanovic,SRB,male,1985-11-24,1.93,93.0,rowing,0,0,0 -951327387,Marko Novakovic,SRB,male,1989-01-04,1.86,95.0,canoe,0,0,0 -638559155,Marko Simonovic,SRB,male,1986-05-30,2.03,96.0,basketball,0,1,0 -345574422,Marko Tomicevic,SRB,male,1990-04-19,1.74,80.0,canoe,0,1,0 -165894715,Markus Bockermann,GER,male,1986-01-14,1.99,97.0,volleyball,0,0,0 -842346983,Markus Thormeyer,CAN,male,1997-08-25,1.95,84.0,aquatics,0,0,0 -488345537,Marleen van Iersel,NED,female,1988-01-07,1.78,68.0,volleyball,0,0,0 -689557957,Marlene Steinherr,GER,female,1985-09-10,1.74,69.0,sailing,0,0,0 -349029560,Marlies Mejias Garcia,CUB,female,1992-12-29,1.68,60.0,cycling,0,0,0 -786699109,Marlo Javier Delgado,ECU,male,1993-05-06,1.84,,boxing,0,0,0 -527228323,Marloes Keetels,NED,female,1993-05-04,1.72,66.0,hockey,0,1,0 -484645032,Marlon Acacio,MOZ,male,1982-07-09,1.8,81.0,judo,0,0,0 -461251275,Maro Jokovic,CRO,male,1987-10-01,2.03,95.0,aquatics,0,1,0 -21035215,Marouan Chouiref,TUN,male,1990-05-27,1.95,109.0,handball,0,0,0 -867476170,Marouen Maggaiz,TUN,male,1983-07-28,1.92,110.0,handball,0,0,0 -983343139,Maroussia Pare,FRA,female,1996-07-18,1.63,52.0,athletics,0,0,0 -906005564,Marquinhos,BRA,male,1994-05-14,1.85,70.0,football,1,0,0 -709840432,Marrit Steenbergen,NED,female,2000-01-11,1.78,62.0,aquatics,0,0,0 -505440878,Mart Seim,EST,male,1990-10-24,1.85,149.0,weightlifting,0,0,0 -919289341,Marta,BRA,female,1986-02-19,1.62,56.0,football,0,0,0 -159109190,Marta Bach Pascual,ESP,female,1993-02-17,1.76,66.0,aquatics,0,0,0 -552371572,Marta Baeza Centurion,BRA,female,1992-03-02,1.64,52.0,fencing,0,0,0 -817886966,Marta Gonzalez Crivillers,ESP,female,1995-04-09,1.81,73.0,aquatics,0,0,0 -582411443,Marta Kharitonova,RUS,female,1984-09-26,1.67,62.0,canoe,0,0,0 -888086557,Marta Lopez,ESP,female,1990-02-04,1.68,64.0,handball,0,0,0 -740922098,Marta Mangue,ESP,female,1983-04-23,1.7,75.0,handball,0,0,0 -91738730,Marta Menegatti,ITA,female,1990-08-16,1.8,58.0,volleyball,0,0,0 -854942046,Marta Milani,ITA,female,1987-03-09,1.72,59.0,athletics,0,0,0 -876465611,Marta Onofre,POR,female,1991-01-28,1.7,64.0,athletics,0,0,0 -165484226,Marta Pagnini,ITA,female,1991-01-21,1.75,58.0,gymnastics,0,0,0 -759213345,Marta Pen Freitas,POR,female,1993-07-31,1.53,46.0,athletics,0,0,0 -885028719,Marta Puda,POL,female,1991-01-13,1.72,59.0,fencing,0,0,0 -500211600,Marta Rostoburova,UZB,female,1996-03-29,1.63,56.0,gymnastics,0,0,0 -166820643,Marta Walczykiewicz,POL,female,1987-08-01,1.65,63.0,canoe,0,1,0 -693968979,Marta Xargay,ESP,female,1990-12-20,1.8,71.0,basketball,0,1,0 -783066900,Marten van Riel,BEL,male,1992-12-15,1.83,64.0,triathlon,0,0,0 -820822830,Martha Bayona Pineda,COL,female,1995-08-12,1.55,60.0,cycling,0,0,0 -537064907,Martha McCabe,CAN,female,1989-08-04,1.68,57.0,aquatics,0,0,0 -186596657,Marthe Koala,BUR,female,1994-03-08,1.77,69.0,athletics,0,0,0 -913653914,Marti Malloy,USA,female,1986-06-23,1.61,58.0,judo,0,0,0 -237753405,Martin Allikvee,EST,male,1995-03-21,1.82,77.0,aquatics,0,0,0 -246553438,Martin Andres Melconian Alvez,URU,male,1990-01-02,1.83,77.0,aquatics,0,0,0 -693642928,Martin Bau,SLO,male,1994-10-08,1.82,74.0,aquatics,0,0,0 -305251897,Martin Benitez,ARG,male,1994-06-17,1.75,76.0,football,0,0,0 -39842194,Martin Esteban Cuestas,URU,male,1986-12-08,1.82,62.0,athletics,0,0,0 -759991813,Martin Fuchs,SUI,male,1992-07-13,1.84,71.0,equestrian,0,0,0 -659392236,Martin Fuksa,CZE,male,1993-04-30,1.8,83.0,canoe,0,0,0 -411643054,Martin Giuffre,CAN,male,1990-10-05,1.85,82.0,badminton,0,0,0 -358461011,Martin Haner,GER,male,1988-08-27,1.84,83.0,hockey,0,0,1 -742548457,Martin Iosefo,USA,male,1990-01-13,1.87,95.0,rugby sevens,0,0,0 -275759196,Martin Kaymer,GER,male,1984-12-28,1.84,76.0,golf,0,0,0 -898863862,Martin Kucera,SVK,male,1990-05-10,1.93,74.0,athletics,0,0,0 -121446595,Martin Kupper,EST,male,1989-05-31,1.98,119.0,athletics,0,0,0 -936041821,Martin Marinov,AUS,male,1967-10-25,1.78,88.0,canoe,0,0,0 -803793508,Martin Michel,BOL,male,1994-09-05,1.9,90.0,judo,0,0,0 -483044526,Martin Naidich,ARG,male,1990-12-17,1.85,74.0,aquatics,0,0,0 -27576453,Martin Pacek,SWE,male,1987-04-28,1.97,100.0,judo,0,0,0 -915011043,Martin Ramos,ARG,male,1991-08-26,1.97,94.0,volleyball,0,0,0 -842376118,Martin Sauer,GER,male,1982-12-17,1.69,55.0,rowing,0,1,0 -916498956,Martin Schaefer,BRA,male,1989-10-18,1.83,82.0,rugby sevens,0,0,0 -997372571,Martin Sinkovic,CRO,male,1989-11-10,1.88,95.0,rowing,1,0,0 -678966888,Martin Strobel,GER,male,1986-06-05,1.89,90.0,handball,0,0,1 -887718773,Martin Tistan,SVK,male,1992-11-12,1.73,65.0,athletics,0,0,0 -134099692,Martin Wolfram,GER,male,1992-01-29,1.64,58.0,aquatics,0,0,0 -969111501,Martin Zwicker,GER,male,1987-02-27,1.75,64.0,hockey,0,0,1 -907069049,Martina Carraro,ITA,female,1993-06-21,1.75,60.0,aquatics,0,0,0 -765683238,Martina Cavallero,ARG,female,1990-05-07,1.63,58.0,hockey,0,0,0 -642385203,Martina Centofanti,ITA,female,1998-05-19,1.7,47.0,gymnastics,0,0,0 -524633511,Martina Guiggi,ITA,female,1984-05-01,1.87,80.0,volleyball,0,0,0 -213466972,Martina Hingis,SUI,female,1980-09-30,1.7,59.0,tennis,0,1,0 -688040735,Martina Hrasnova,SVK,female,1983-03-21,1.77,80.0,athletics,0,0,0 -420210959,Martina Kohlova,SVK,female,1984-11-16,1.69,68.0,canoe,0,0,0 -77888504,Martina Moravcikova,CZE,female,1988-08-13,1.74,64.0,aquatics,0,0,0 -290254894,Martina Ratej,SLO,female,1981-11-02,1.78,80.0,athletics,0,0,0 -12278954,Martina Ritter,AUT,female,1982-09-23,1.74,59.0,cycling,0,0,0 -670614279,Martina Rizzelli,ITA,female,1998-03-24,1.53,47.0,gymnastics,0,0,0 -190010311,Martina Strutz,GER,female,1981-11-04,1.59,58.0,athletics,0,0,0 -761227644,Martina de Memme,ITA,female,1991-08-07,1.75,65.0,aquatics,0,0,0 -373707575,Martina van Berkel,SUI,female,1989-01-23,1.66,54.0,aquatics,0,0,0 -984490167,Martine Grael,BRA,female,1991-02-12,1.68,62.0,sailing,1,0,0 -395476042,Martine Smeets,NED,female,1990-05-05,1.72,69.0,handball,0,0,0 -917191537,Martino Goretti,ITA,male,1985-09-27,1.86,73.0,rowing,0,0,0 -483109753,Marton Szivos,HUN,male,1981-08-19,1.92,95.0,aquatics,0,0,0 -397414268,Marton Vamos,HUN,male,1992-06-24,2.02,105.0,aquatics,0,0,0 -410826022,Marty McDowell,NZL,male,1987-01-16,1.84,85.0,canoe,0,0,0 -732634662,Martyn Rooney,GBR,male,1987-04-03,1.98,81.0,athletics,0,0,0 -472349121,Martyna Dabrowska,POL,female,1994-04-05,1.76,56.0,athletics,0,0,0 -300217673,Martyna Mikolajczak,POL,female,1991-05-12,,,rowing,0,0,0 -568668037,Martyna Trajdos,GER,female,1989-04-05,1.71,66.0,judo,0,0,0 -255248240,Martynas Dziaugys,LTU,male,1986-11-08,1.89,95.0,rowing,0,0,0 -62434883,Maru Teferi,ISR,male,1992-08-17,1.64,52.0,athletics,0,0,0 -674506453,Marusa Cernjul,SLO,female,1992-06-30,1.77,56.0,athletics,0,0,0 -615165685,Marvin Bracy,USA,male,1993-12-15,1.76,77.0,athletics,0,0,0 -360462839,Marvin Rene,FRA,male,1995-04-11,1.77,60.0,athletics,0,0,0 -782811562,Marwa Amri,TUN,female,1989-01-08,1.6,58.0,wrestling,0,0,1 -897941970,Marwan Ahmed Aly Morsy Elamrawy,EGY,male,1995-04-14,1.94,93.0,aquatics,0,0,0 -203919226,Marwan Elkamash,EGY,male,1993-11-14,1.83,77.0,aquatics,0,0,0 -590552399,Mary Hanna,AUS,female,1954-12-01,1.73,63.0,equestrian,0,0,0 -699555088,Mary Joy Tabal,PHI,female,1989-07-13,1.49,45.0,athletics,0,0,0 -161117607,Mary Opeloge,SAM,female,1992-01-24,1.52,75.0,weightlifting,0,0,0 -62290445,Maryam Usman,NGR,female,1990-11-09,1.66,122.0,weightlifting,0,0,0 -104377411,Maryan Muse,SOM,female,1997-01-01,,,athletics,0,0,0 -387651976,Maryia Filonchyk,BLR,female,1992-01-10,1.89,74.0,basketball,0,0,0 -402117502,Maryia Katsiak,BLR,female,1997-03-02,1.67,45.0,gymnastics,0,0,0 -408549485,Maryia Mamashuk,BLR,female,1992-08-31,1.63,65.0,wrestling,0,1,0 -2923353,Maryia Papova,BLR,female,1994-07-13,1.89,84.0,basketball,0,0,0 -536189027,Maryna Bekh,UKR,female,1995-07-18,1.74,62.0,athletics,0,0,0 -401157026,Maryna Cherniak,UKR,female,1988-03-26,1.62,48.0,judo,0,0,0 -858452425,Maryna Damantsevich,BLR,female,1984-02-10,1.62,51.0,athletics,0,0,0 -884349889,Maryna Kylypko,UKR,female,1995-11-10,1.64,58.0,athletics,0,0,0 -951115413,Maryna Litvinchuk,BLR,female,1988-03-12,1.78,68.0,canoe,0,0,1 -870110736,Marzia Caravelli,ITA,female,1981-10-23,1.77,62.0,athletics,0,0,0 -162278204,Masakatsu Hikosaka,JPN,male,1991-01-18,1.77,90.0,rugby sevens,0,0,0 -535827746,Masaki Ito,JPN,male,1988-11-02,1.67,62.0,gymnastics,0,0,0 -976787167,Masaki Kaneko,JPN,male,1992-03-27,1.81,75.0,aquatics,0,0,0 -106215469,Masanao Takahashi,JPN,male,1982-01-18,1.7,65.0,equestrian,0,0,0 -219895211,Masashi Ebinuma,JPN,male,1990-02-15,1.7,66.0,judo,0,0,1 -418990510,Masashi Kamekawa,JPN,male,1993-05-28,1.77,68.0,football,0,0,0 -48986256,Masato Sakai,JPN,male,1995-06-06,1.81,77.0,aquatics,0,1,0 -801397124,Masatoshi Kushibiki,JPN,male,1993-01-29,1.86,82.0,football,0,0,0 -566847633,Masbah Ahmmed,BAN,male,1995-03-11,,,athletics,0,0,0 -143690875,Mashu Baker,JPN,male,1994-09-25,1.78,90.0,judo,1,0,0 -620850796,Mason Finley,USA,male,1990-10-07,2.04,157.0,athletics,0,0,0 -20695020,Massimo Colaci,ITA,male,1985-02-21,1.8,75.0,volleyball,0,1,0 -653938951,Massimo Fabbrizi,ITA,male,1977-08-27,1.77,110.0,shooting,0,0,0 -306036644,Mate Helebrandt,HUN,male,1989-01-12,1.74,59.0,athletics,0,0,0 -56718574,Matea Matosevic,CRO,female,1989-03-14,1.73,52.0,athletics,0,0,0 -48994654,Matea Samardzic,CRO,female,1995-01-17,1.77,61.0,aquatics,0,0,0 -886265349,Matej Benus,SVK,male,1987-11-02,1.96,83.0,canoe,0,1,0 -218621574,Matej Gaber,SLO,male,1991-07-22,1.97,114.0,handball,0,0,0 -798424406,Matej Mohoric,SLO,male,1994-10-19,1.85,67.0,cycling,0,0,0 -301324995,Matej Toth,SVK,male,1983-02-10,1.85,73.0,athletics,1,0,0 -217355877,Mateja Simic,SLO,female,1980-03-11,1.69,58.0,triathlon,0,0,0 -454434360,Matelita Buadromo,FIJ,female,1996-01-15,,,aquatics,0,0,0 -987983731,Mateo Sanz Lanz,SUI,male,1993-11-06,1.72,64.0,sailing,0,0,0 -254178473,Mateus Filipe Gregorio Machado,BRA,male,1993-07-05,1.85,104.0,weightlifting,0,0,0 -765197800,Mateusz Bieniek,POL,male,1994-04-05,2.1,98.0,volleyball,0,0,0 -186521340,Mateusz Biskup,POL,male,1994-02-08,1.9,91.0,rowing,0,0,0 -610688503,Mateusz Jachlewski,POL,male,1984-12-27,1.84,88.0,handball,0,0,0 -211225504,Mateusz Kaminski,POL,male,1991-05-03,1.89,83.0,canoe,0,0,0 -487122930,Mateusz Kus,POL,male,1987-07-14,2.0,112.0,handball,0,0,0 -652295351,Mateusz Mika,POL,male,1991-01-21,2.06,86.0,volleyball,0,0,0 -403394715,Mateusz Przybylko,GER,male,1992-03-09,1.95,78.0,athletics,0,0,0 -377872587,Mateusz Sawrymowicz,POL,male,1987-04-22,1.85,75.0,aquatics,0,0,0 -113410495,Mateusz Wilangowski,POL,male,1991-10-07,1.95,89.0,rowing,0,0,0 -164089137,Matevz Skok,SLO,male,1986-09-02,1.88,95.0,handball,0,0,0 -657155974,Matheus Borges,BRA,male,1993-07-20,1.76,78.0,hockey,0,0,0 -898031635,Matheus Santana,BRA,male,1996-04-02,1.91,90.0,aquatics,0,0,0 -370742926,Mathew Belcher,AUS,male,1982-09-20,1.73,62.0,sailing,0,1,0 -36282420,Mathews Punza,ZAM,male,1988-04-27,1.7,66.0,judo,0,0,0 -477624913,Mathias Boe,DEN,male,1980-07-11,1.85,75.0,badminton,0,0,0 -286373803,Mathias Fluckiger,SUI,male,1988-09-27,1.72,65.0,cycling,0,0,0 -883500515,Mathias Hebo Rasmussen,DEN,male,1995-08-02,1.9,84.0,football,0,0,0 -533908779,Mathias Muller,GER,male,1992-04-03,1.87,80.0,hockey,0,0,1 -152701098,Mathias Tulyoongeleni Hamunyela,NAM,male,1992-10-15,1.6,,boxing,0,0,0 -791421238,Mathieu Albert Daniel Bauderlique,FRA,male,1989-07-03,1.85,,boxing,0,0,1 -414004191,Mathieu Bilodeau,CAN,male,1983-11-27,1.85,72.0,athletics,0,0,0 -981983025,Mathieu Grebille,FRA,male,1991-10-06,1.98,100.0,handball,0,1,0 -484330084,Mathieu Lemoine,FRA,male,1984-04-17,1.76,70.0,equestrian,1,0,0 -774702640,Mathieu Peisson,FRA,male,1982-09-29,1.85,102.0,aquatics,0,0,0 -36826829,Mathilde Andraud,FRA,female,1989-04-28,1.73,70.0,athletics,0,0,0 -802971626,Mathilde Cini,FRA,female,1994-11-18,1.66,62.0,aquatics,0,0,0 -48760156,Mathilde Lamolle,FRA,female,1997-04-07,1.67,58.0,shooting,0,0,0 -652013285,Mathilde de Kerangat,FRA,female,1991-10-14,1.72,67.0,sailing,0,0,0 -746005417,Mathlynn Sasser,MHL,female,1996-12-25,1.58,57.0,weightlifting,0,0,0 -728437436,Matias Albarracin,ARG,male,1979-10-25,1.74,65.0,equestrian,0,0,0 -322320446,Matias Buhler,SUI,male,1983-01-15,1.76,77.0,sailing,0,0,0 -304306143,Matias Koski,FIN,male,1994-05-18,1.95,93.0,aquatics,0,0,0 -64937045,Matias Montinho,ANG,male,1990-07-15,1.67,63.0,sailing,0,0,0 -958190853,Matias Moroni,ARG,male,1991-03-29,1.85,84.0,rugby sevens,0,0,0 -482423754,Matias Paredes,ARG,male,1982-02-01,1.76,74.0,hockey,1,0,0 -478914190,Matias Rey,ARG,male,1984-12-01,1.78,72.0,hockey,1,0,0 -492966481,Matias Schulz,ARG,male,1982-02-12,1.9,88.0,handball,0,0,0 -120488238,Matias Tudela,ESP,male,1984-10-06,1.88,95.0,rugby sevens,0,0,0 -931233756,Matias del Solar,CHI,male,1975-11-29,1.84,87.0,sailing,0,0,0 -537360256,Matija Kvasina,CRO,male,1981-12-04,1.8,72.0,cycling,0,0,0 -706656825,Matilda Ekholm,SWE,female,1982-06-15,1.72,67.0,table tennis,0,0,0 -403290407,Matilde Ortiz Reyes,ESP,female,1990-09-16,1.74,67.0,aquatics,0,0,0 -21739330,Mats Grambusch,GER,male,1992-11-04,1.77,74.0,hockey,0,0,1 -298706269,Matt Gohdes,AUS,male,1990-05-08,1.8,80.0,hockey,0,0,0 -472009304,Matt Gotrel,GBR,male,1989-03-01,1.95,95.0,rowing,1,0,0 -935178102,Matt Hutchins,NZL,male,1994-09-19,1.9,75.0,aquatics,0,0,0 -153631965,Matt Kuchar,USA,male,1978-06-21,1.94,86.0,golf,0,0,1 -984564500,Matt Langridge,GBR,male,1983-05-20,1.94,93.0,rowing,1,0,0 -915798442,Matt Williams,AUS,male,1985-05-09,1.78,72.0,equestrian,0,0,0 -395178195,Matteo Aicardi,ITA,male,1986-04-19,1.92,102.0,aquatics,0,0,1 -690029131,Matteo Castaldo,ITA,male,1985-12-11,1.88,86.0,rowing,0,0,1 -427248664,Matteo Galvan,ITA,male,1988-08-24,1.82,78.0,athletics,0,0,0 -381850551,Matteo Giupponi,ITA,male,1988-10-08,1.89,70.0,athletics,0,0,0 -38214425,Matteo Lodo,ITA,male,1994-10-25,1.96,90.0,rowing,0,0,1 -289265001,Matteo Manassero,ITA,male,1993-04-19,1.83,79.0,golf,0,0,0 -872073653,Matteo Marconcini,ITA,male,1989-08-26,1.85,81.0,judo,0,0,0 -911422793,Matteo Piano,ITA,male,1990-10-24,2.08,102.0,volleyball,0,1,0 -171065867,Matteo Rivolta,ITA,male,1991-11-16,1.93,83.0,aquatics,0,0,0 -58032086,Matteo Stefanini,ITA,male,1984-04-29,1.9,91.0,rowing,0,0,0 -43202713,Matthew Abood,AUS,male,1986-06-28,1.97,92.0,aquatics,0,0,1 -99590760,Matthew Anderson,USA,male,1987-04-18,2.02,100.0,volleyball,0,0,1 -925341714,Matthew Baranoski,USA,male,1993-07-27,1.83,96.0,cycling,0,0,0 -730877927,Matthew Centrowitz,USA,male,1989-10-18,1.76,65.0,athletics,1,0,0 -71745896,Matthew Chau,AUS,male,1994-11-09,1.85,77.0,badminton,0,0,0 -335841832,Matthew Dawson,AUS,male,1994-04-27,1.76,66.0,hockey,0,0,0 -496386141,Matthew Dellavedova,AUS,male,1990-09-08,1.91,89.0,basketball,0,0,0 -566672226,Matthew Denny,AUS,male,1996-06-02,1.95,118.0,athletics,0,0,0 -425434844,Matthew Duncan Abeysinghe,SRI,male,1996-03-19,1.8,74.0,aquatics,0,0,0 -908655978,Matthew Emmons,USA,male,1981-04-05,1.76,83.0,shooting,0,0,0 -986397793,Matthew Glaetzer,AUS,male,1992-08-24,1.9,86.0,cycling,0,0,0 -101015451,Matthew Guest,CAN,male,1985-04-26,1.9,90.0,hockey,0,0,0 -550707976,Matthew Hudson-Smith,GBR,male,1994-10-26,1.94,78.0,athletics,0,0,0 -897031531,Matthew Hughes,CAN,male,1989-08-03,1.8,66.0,athletics,0,0,0 -766785466,Matthew Mark Meyer,RSA,male,1998-03-04,1.85,70.0,aquatics,0,0,0 -475158866,Matthew McGovern,IRL,male,1984-09-20,1.8,82.0,sailing,0,0,0 -874367808,Matthew Miller,USA,male,1989-01-13,1.96,98.0,rowing,0,0,0 -439268682,Matthew Sarmento,CAN,male,1991-06-23,1.74,76.0,hockey,0,0,0 -405824354,Matthew Stanley,NZL,male,1992-01-15,1.98,82.0,aquatics,0,0,0 -483679471,Matthew Swann,AUS,male,1989-05-16,1.7,64.0,hockey,0,0,0 -388169524,Matthias Buhler,GER,male,1986-09-02,1.89,78.0,athletics,0,0,0 -554191048,Matthias Ginter,GER,male,1994-01-19,1.9,88.0,football,0,1,0 -963881256,Matthias Schmid,AUT,male,1980-12-12,1.75,66.0,sailing,0,0,0 -719850911,Matthieu Androdias,FRA,male,1990-06-11,1.94,94.0,rowing,0,0,0 -74294229,Matthieu Peche,FRA,male,1987-10-07,1.75,75.0,canoe,0,0,1 -437683546,Matthieu Rosset,FRA,male,1990-05-26,1.7,68.0,aquatics,0,0,0 -103170142,Matthijs Buchli,NED,male,1992-12-13,1.88,90.0,cycling,0,1,0 -420949330,Matti Mattsson,FIN,male,1993-10-05,1.97,98.0,aquatics,0,0,0 -499246333,Mattia Camboni,ITA,male,1996-04-26,1.8,70.0,sailing,0,0,0 -280493573,Mattias Andersson,SWE,male,1978-03-29,1.86,95.0,handball,0,0,0 -515599241,Mattias Karlsson,SWE,male,1991-09-07,1.91,88.0,table tennis,0,0,0 -730518978,Mattias Zachrisson,SWE,male,1990-08-22,1.78,89.0,handball,0,0,0 -676985214,Matus Bubenik,SVK,male,1989-11-14,1.97,78.0,athletics,0,0,0 -896939838,Matyas Szabo,GER,male,1991-08-19,1.83,82.0,fencing,0,0,0 -178284851,Matylda Kowal,POL,female,1989-01-11,1.7,51.0,athletics,0,0,0 -431865511,Maud van der Meer,NED,female,1992-05-20,1.78,68.0,aquatics,0,0,0 -498567900,Maureen Jelagat Maiyo,KEN,female,1985-05-28,1.67,58.0,athletics,0,0,0 -322346494,Maureen Koster,NED,female,1992-07-03,1.76,56.0,athletics,0,0,0 -237676786,Mauricio Arteaga,ECU,male,1988-08-08,1.77,68.0,athletics,0,0,0 -756883817,Mauricio Borges Almeida Silva,BRA,male,1989-02-04,1.99,99.0,volleyball,1,0,0 -646748093,Mauricio Luiz de Souza,BRA,male,1988-09-29,2.09,93.0,volleyball,1,0,0 -293694228,Mauricio Martinez,ARG,male,1993-02-20,1.67,70.0,football,0,0,0 -76978968,Mauricio Ortega,COL,male,1994-08-04,1.8,90.0,athletics,0,0,0 -853068624,Mauro Crenna,ITA,male,1991-11-02,1.86,93.0,canoe,0,0,0 -574835225,Mauro Nespoli,ITA,male,1987-11-22,1.81,78.0,archery,0,0,0 -167844391,Mavis Chirandu,ZIM,female,1995-01-15,1.57,53.0,football,0,0,0 -384641451,Max Christiansen,GER,male,1996-09-25,1.85,84.0,football,0,1,0 -81864285,Max Esposito,AUS,male,1997-06-16,1.74,64.0,modern pentathlon,0,0,0 -210303328,Max Hartung,GER,male,1989-10-08,1.89,86.0,fencing,0,0,0 -89080288,Max Heinzer,SUI,male,1987-08-07,1.78,77.0,fencing,0,0,0 -508219874,Max Hess,GER,male,1996-07-13,1.85,80.0,athletics,0,0,0 -907301904,Max Hoff,GER,male,1982-09-12,1.97,95.0,canoe,1,0,0 -934682595,Max Litchfield,GBR,male,1995-03-04,1.68,78.0,aquatics,0,0,0 -832967843,Max Mirnyi,BLR,male,1977-07-06,1.96,99.0,tennis,0,0,0 -232790653,Max Niederlag,GER,male,1993-05-05,1.83,85.0,cycling,0,0,0 -733043738,Max Rendschmidt,GER,male,1993-12-12,1.86,90.0,canoe,2,0,0 -302494517,Max Salminen,SWE,male,1988-09-22,1.97,98.0,sailing,0,0,0 -93066253,Max Whitlock,GBR,male,1993-01-13,1.67,56.0,gymnastics,2,0,1 -386159479,Maxemillion Kassman,PNG,male,1998-07-17,1.75,64.0,taekwondo,0,0,0 -89475411,Maxim Bouchard,CAN,male,1990-09-18,1.8,71.0,aquatics,0,0,0 -2552703,Maxim Mikhaylov,RUS,male,1988-03-19,2.02,103.0,volleyball,0,0,0 -567764171,Maxim Rakov,KAZ,male,1986-02-07,1.81,100.0,judo,0,0,0 -429841022,Maxime Beaumont,FRA,male,1982-04-23,1.91,94.0,canoe,0,1,0 -690024255,Maxime Brinck-Croteau,CAN,male,1986-03-29,1.77,78.0,fencing,0,0,0 -232147471,Maxime Marotte,FRA,male,1986-12-05,1.72,62.0,cycling,0,0,0 -162317183,Maxime Mottet,BEL,male,1991-05-17,1.78,84.0,shooting,0,0,0 -844686899,Maximilian Korge,GER,male,1994-10-12,1.93,92.0,rowing,0,0,0 -887043487,Maximilian Levy,GER,male,1987-06-26,1.82,87.0,cycling,0,0,0 -91221039,Maximilian Meyer,GER,male,1995-09-18,1.73,69.0,football,0,1,0 -718400252,Maximilian Munski,GER,male,1988-01-10,1.95,96.0,rowing,0,1,0 -382717900,Maximilian Planer,GER,male,1991-01-28,1.98,94.0,rowing,0,0,0 -65092723,Maximilian Reinelt,GER,male,1988-08-24,1.95,98.0,rowing,0,1,0 -243609847,Maximiliano Ariel Richeze,ARG,male,1983-03-07,1.77,70.0,cycling,0,0,0 -488438622,Maximilien van Haaster,CAN,male,1992-06-19,1.85,88.0,fencing,0,0,0 -624320550,Maximo Gonzalez,ARG,male,1983-07-20,1.76,77.0,tennis,0,0,0 -781644314,Maximo Mercedes,DOM,male,1988-09-14,1.86,70.0,athletics,0,0,0 -812967807,Maxwell Holt,USA,male,1987-03-12,2.05,90.0,volleyball,0,0,1 -526196879,Maxwell Lattimer,CAN,male,1993-07-14,1.85,72.0,rowing,0,0,0 -967402790,Maya Moore,USA,female,1989-06-11,1.83,79.0,basketball,1,0,0 -500075689,Maya Rehberg,GER,female,1994-04-28,1.7,58.0,athletics,0,0,0 -919598411,Mayada Sayyad,PLE,female,1992-10-26,,,athletics,0,0,0 -297050524,Mayara Fier de Moura,BRA,female,1986-12-05,1.68,71.0,handball,0,0,0 -341245309,Maykel Masso,CUB,male,1999-05-08,1.74,61.0,athletics,0,0,0 -761032254,Mayobanex de Oleo,DOM,male,1993-05-26,1.71,61.0,athletics,0,0,0 -135012058,Mayra Aguiar,BRA,female,1991-08-03,1.77,78.0,judo,0,0,1 -750703136,Mayra Carolina Herrera,GUA,female,1988-12-20,1.63,54.0,athletics,0,0,0 -179987194,Mayssa Raquel Pessoa,BRA,female,1984-09-11,1.8,66.0,handball,0,0,0 -947011927,Mayu Hamada,JPN,female,1994-01-31,1.74,57.0,taekwondo,0,0,0 -466510848,Mayumi Ono,JPN,female,1984-08-14,1.7,62.0,hockey,0,0,0 -110852352,Mayya Petrova,RUS,female,1982-05-26,1.78,71.0,handball,1,0,0 -128985176,Mazoon Al-Alawi,OMA,female,1997-11-14,,,athletics,0,0,0 -409204459,McLain Ward,USA,male,1975-10-17,1.76,70.0,equestrian,0,1,0 -299427906,McQuin Baron,USA,male,1995-10-27,2.06,104.0,aquatics,0,0,0 -965246251,Md Fakhri Ismail,BRU,male,1991-03-06,1.6,62.0,athletics,0,0,0 -877915055,Meaghan Benfeito,CAN,female,1989-03-02,1.55,48.0,aquatics,0,0,2 -647839865,Meaghan Volker,AUS,female,1990-10-20,,,rowing,0,0,0 -968097436,Mebrahtom Keflezighi,USA,male,1975-05-05,1.66,56.0,athletics,0,0,0 -654095521,Mechiel Versluis,NED,male,1987-07-29,1.97,95.0,rowing,0,0,1 -702444425,Megan Guarnier,USA,female,1985-05-04,1.63,54.0,cycling,0,0,0 -248091988,Megan Kalmoe,USA,female,1983-08-21,1.78,72.0,rowing,0,0,0 -158155836,Megan Lane,CAN,female,1991-05-04,1.78,66.0,equestrian,0,0,0 -338613319,Megan Lukan,CAN,female,1992-02-14,1.7,58.0,rugby sevens,0,0,1 -156676980,Megan Rapinoe,USA,female,1985-07-05,1.67,59.0,football,0,0,0 -350561307,Megan Simmonds,JAM,female,1994-03-18,1.57,49.0,athletics,0,0,0 -618204486,Meghan Klingenberg,USA,female,1988-08-02,1.58,56.0,football,0,0,0 -26643473,Meghan Musnicki,USA,female,1983-02-05,1.83,73.0,rowing,1,0,0 -626601674,Meghan O'Leary,USA,female,1984-08-24,1.83,73.0,rowing,0,0,0 -86610737,Megumi Iseda,JPN,female,1987-06-30,1.57,56.0,sailing,0,0,0 -463812760,Mehboob Ali,PAK,male,1990-04-10,,,athletics,0,0,0 -44950691,Mehdi Mahdavi,IRI,male,1984-02-13,1.91,96.0,volleyball,0,0,0 -909022255,Mehdi Marzouki,FRA,male,1987-05-26,1.92,101.0,aquatics,0,0,0 -211496382,Mehdy Metella,FRA,male,1992-07-17,1.91,99.0,aquatics,0,1,0 -510649353,Mehmet Nadir Unal,TUR,male,1993-01-13,1.81,,boxing,0,0,0 -513851313,Mehtap Kurnaz,TUR,female,1995-05-01,1.6,62.0,weightlifting,0,0,0 -595667372,Mei Yu Hsiao,TPE,female,1985-01-07,1.62,58.0,cycling,0,0,0 -508723001,Meichen Sun,CHN,female,1998-11-01,1.75,63.0,aquatics,0,0,0 -786377606,Meikayla Moore,NZL,female,1996-06-04,1.75,67.0,football,0,0,0 -742356260,Meisam Abolfazl Nasiri,IRI,male,1989-06-01,1.69,71.0,wrestling,0,0,0 -531197956,Meiyu Liang,CHN,female,1994-01-08,1.66,63.0,hockey,0,0,0 -694559348,Mekonnen Gebremedhin,ETH,male,1988-10-11,1.81,61.0,athletics,0,0,0 -279948149,Melania Costa Schmid,ESP,female,1989-04-24,1.79,68.0,aquatics,0,0,0 -241238603,Melanie Behringer,GER,female,1985-11-18,1.72,71.0,football,1,0,0 -309505211,Melanie Henique,FRA,female,1992-12-22,1.72,60.0,aquatics,0,0,0 -234453586,Melanie Leupolz,GER,female,1994-04-14,1.73,52.0,football,1,0,0 -694013113,Melanie Margalis,USA,female,1991-12-30,1.73,65.0,aquatics,1,0,0 -142783294,Melanie McCann,CAN,female,1989-10-08,1.73,58.0,modern pentathlon,0,0,0 -832907922,Melanie Pfeifer,GER,female,1986-08-25,1.67,54.0,canoe,0,0,0 -643682523,Melanie Wilson,GBR,female,1984-06-25,1.84,75.0,rowing,0,1,0 -734867601,Melany Hernandez,MEX,female,1998-07-26,1.65,60.0,aquatics,0,0,0 -152354066,Melek Hu,TUR,female,1989-01-27,1.65,53.0,table tennis,0,0,0 -559761961,Meli Malani,FIJ,male,1996-11-17,,94.0,aquatics,0,0,0 -667446420,Melina Robert-Michon,FRA,female,1979-07-18,1.8,85.0,athletics,0,1,0 -227958831,Melinda Geiger,ROU,female,1987-03-28,1.77,69.0,handball,0,0,0 -395742388,Meline Gerard,FRA,female,1990-05-30,1.68,61.0,football,0,0,0 -638389336,Melisa Gil,ARG,female,1984-08-09,1.6,65.0,shooting,0,0,0 -314522649,Melissa Bishop,CAN,female,1988-08-05,1.73,56.0,athletics,0,0,0 -371776247,Melissa Boekelman,NED,female,1989-05-11,1.76,87.0,athletics,0,0,0 -351096848,Melissa Breen,AUS,female,1990-09-17,1.74,65.0,athletics,0,0,0 -357241098,Melissa Gonzalez,USA,female,1989-01-24,1.61,58.0,hockey,0,0,0 -910455517,Melissa Hoskins,AUS,female,1991-02-24,1.75,64.0,cycling,0,0,0 -37881707,Melissa Mojica,PUR,female,1983-12-29,1.78,82.0,judo,0,0,0 -31451946,Melissa Ortiz,COL,female,1990-01-24,1.72,52.0,football,0,0,0 -424629723,Melissa Pagnotta,CAN,female,1988-09-22,1.8,64.0,taekwondo,0,0,0 -92926686,Melissa Seidemann,USA,female,1990-06-26,1.83,104.0,aquatics,1,0,0 -283491400,Melissa Tancredi,CAN,female,1981-12-27,1.77,77.0,football,0,0,1 -388744533,Melissa Tapper,AUS,female,1990-03-01,1.66,65.0,table tennis,0,0,0 -853786282,Melissa Wu,AUS,female,1992-05-03,1.52,50.0,aquatics,0,0,0 -242511677,Melita Abraham,CHI,female,1997-07-07,1.7,62.0,rowing,0,0,0 -967171979,Melitina Staniouta,BLR,female,1993-11-14,1.73,53.0,gymnastics,0,0,0 -488010523,Melker Svard Jacobsson,SWE,male,1994-01-08,1.88,79.0,athletics,0,0,0 -114894232,Memo,INA,male,1995-01-08,1.92,88.0,rowing,0,0,0 -511927027,Menatalla Karim,EGY,female,1995-11-12,1.6,65.0,canoe,0,0,0 -958603253,Meng Wei,CHN,female,1989-06-14,1.69,57.0,shooting,0,0,0 -715981032,Menghui Zhu,CHN,female,1999-03-23,1.77,62.0,aquatics,0,0,0 -401470235,Menglu Ma,CHN,female,1997-09-24,1.67,61.0,cycling,0,0,0 -271606334,Mengni Tang,CHN,female,1994-04-08,1.69,59.0,aquatics,0,1,0 -658441949,Mengqian Ren,CHN,female,1993-10-04,1.75,66.0,athletics,0,0,0 -899791888,Mengran Sun,CHN,female,1992-07-16,1.95,83.0,basketball,0,0,0 -525021516,Mengxin Sun,CHN,female,1993-04-08,1.9,84.0,basketball,0,0,0 -304831992,Mengxue Zhang,CHN,female,1991-02-06,1.62,70.0,shooting,1,0,0 -566918843,Mengyu Wang,CHN,female,1992-05-25,1.7,71.0,hockey,0,0,0 -469860412,Mengyu Yu,SIN,female,1989-08-18,1.66,50.0,table tennis,0,0,0 -237464508,Menzi Masuku,RSA,male,1993-04-15,1.69,61.0,football,0,0,0 -273574012,Meraf Bahta,SWE,female,1989-06-24,1.76,52.0,athletics,0,0,0 -337904141,Mercedes Isabel Perez Tigrero,COL,female,1987-08-07,1.57,63.0,weightlifting,0,0,0 -726189235,Mercy Cherono,KEN,female,1991-05-07,,51.0,athletics,0,0,0 -7993281,Merdan Atayev,TKM,male,1995-05-08,1.96,80.0,aquatics,0,0,0 -12306725,Meredith Michaels-Beerbaum,GER,female,1969-12-26,1.62,51.0,equestrian,0,0,1 -712487973,Merel Blom,NED,female,1986-08-19,1.71,60.0,equestrian,0,0,0 -757366284,Merewai Cumu,FIJ,female,1997-08-31,1.74,73.0,rugby sevens,0,0,0 -905398230,Merle van Benthem,NED,female,1992-12-07,1.7,68.0,cycling,0,0,0 -571759273,Merrill Moses,USA,male,1977-08-13,1.91,97.0,aquatics,0,0,0 -228859028,Mert Atli,TUR,male,1993-07-23,1.91,70.0,athletics,0,0,0 -447162714,Merven Clair,MRI,male,1993-07-02,1.78,,boxing,0,0,0 -679179495,Meryem Akdag,TUR,female,1992-08-05,1.71,51.0,athletics,0,0,0 -746996056,Meryem Erdogan,TUR,female,1990-04-24,1.72,55.0,athletics,0,0,0 -795596801,Messala Merbah,ALG,male,1994-07-22,1.78,65.0,football,0,0,0 -428462407,Mesud Pezer,BIH,male,1994-08-27,1.96,120.0,athletics,0,0,0 -334554979,Mete Gazoz,TUR,male,1999-06-08,1.81,60.0,archery,0,0,0 -30835928,Methkal Abu Drais,JOR,male,1983-12-29,1.68,67.0,athletics,0,0,0 -478490266,Mi Gyong Ri,PRK,female,1990-09-30,1.65,56.0,table tennis,0,0,0 -309196317,Mi Rae Kim,PRK,female,2001-04-07,1.5,40.0,aquatics,0,0,0 -534654032,Mia Hermansson,SWE,female,1992-12-12,1.75,63.0,judo,0,0,0 -585217466,Miah-Marie Langlois,CAN,female,1991-09-21,1.72,63.0,basketball,0,0,0 -759154793,Micah Christenson,USA,male,1993-05-08,1.98,88.0,volleyball,0,0,1 -440955726,Micha Powell,CAN,female,1995-01-12,1.77,55.0,athletics,0,0,0 -909720625,Michael Albasini,SUI,male,1980-12-20,1.72,67.0,cycling,0,0,0 -828603866,Michael Alexandre Bodegas,ITA,male,1987-05-03,1.92,102.0,aquatics,0,0,1 -500190525,Michael Brake,NZL,male,1994-10-22,1.87,91.0,rowing,0,0,0 -814037531,Michael Bultheel,BEL,male,1986-06-30,1.89,81.0,athletics,0,0,0 -992884097,Michael D'Almeida,FRA,male,1987-09-03,1.76,80.0,cycling,0,0,1 -969061469,Michael Damgaard Nielsen,DEN,male,1990-03-18,1.92,92.0,handball,1,0,0 -386535330,Michael Darling,IRL,male,1988-07-03,1.7,65.0,hockey,0,0,0 -442019186,Michael Di Santo,USA,male,1989-12-10,1.86,90.0,rowing,0,0,0 -144352723,Michael Fuchs,GER,male,1982-04-22,1.81,86.0,badminton,0,0,0 -253286352,Michael Gbinije,NGR,male,1992-06-05,2.01,90.0,basketball,0,0,0 -128789851,Michael Guigou,FRA,male,1982-01-28,1.79,79.0,handball,0,1,0 -563835725,Michael Hansen,DEN,male,1990-02-14,1.82,82.0,sailing,0,0,0 -156951365,Michael Hepburn,AUS,male,1991-08-17,1.86,77.0,cycling,0,1,0 -746795729,Michael Hoare,GBR,male,1985-11-14,1.76,77.0,hockey,0,0,0 -46089311,Michael Janker,GER,male,1992-03-23,1.8,75.0,shooting,0,0,0 -360677958,Michael John Conlan,IRL,male,1991-11-19,1.7,,boxing,0,0,0 -245131295,Michael Julian Meyer,RSA,male,1992-09-01,1.84,76.0,aquatics,0,0,0 -878101089,Michael Jung,GER,male,1982-07-31,1.68,70.0,equestrian,1,1,0 -415356488,Michael Kalomiris,GRE,male,1986-10-09,1.81,65.0,athletics,0,0,0 -753189621,Michael Maskell,BAR,male,1966-11-24,1.75,114.0,shooting,0,0,0 -255856000,Michael Mason,CAN,male,1986-09-30,1.86,67.0,athletics,0,0,0 -882282575,Michael Mathieu,BAH,male,1984-06-24,1.78,76.0,athletics,0,0,1 -359582375,Michael McPhail,USA,male,1981-12-15,1.83,88.0,shooting,0,0,0 -876833914,"Michael O,Reilly",IRL,male,1993-04-30,1.8,,boxing,0,0,0 -672059782,Michael Perez,MEX,male,1993-02-14,1.69,75.0,football,0,0,0 -491565031,Michael Phelps,USA,male,1985-06-30,1.94,90.0,aquatics,5,1,0 -963798182,Michael Rimmer,GBR,male,1986-02-03,1.82,66.0,athletics,0,0,0 -212707556,Michael Robson,IRL,male,1995-04-18,1.75,70.0,hockey,0,0,0 -776809154,Michael Schmid,SUI,male,1988-01-02,1.79,73.0,rowing,0,0,0 -509141582,Michael Sean James Nicholson,ZIM,male,1973-11-09,1.82,105.0,shooting,0,0,0 -719475341,Michael Shelley,AUS,male,1983-10-10,1.83,60.0,athletics,0,0,0 -717870725,Michael Tayler,CAN,male,1992-02-06,1.75,69.0,canoe,0,0,0 -591796882,Michael Tinsley,USA,male,1984-04-21,1.86,74.0,athletics,0,0,0 -868365619,Michael Umeh,NGR,male,1984-09-18,1.87,88.0,basketball,0,0,0 -646681270,Michael Venus,NZL,male,1987-10-16,1.92,84.0,tennis,0,0,0 -247341718,Michael Watt,IRL,male,1987-04-13,1.78,79.0,hockey,0,0,0 -941406525,Michael Whitaker,GBR,male,1960-03-17,1.72,69.0,equestrian,0,0,0 -871224571,Michael Woods,CAN,male,1986-10-12,1.75,63.0,cycling,0,0,0 -542540504,Michaela Ek,SWE,female,1988-02-01,1.74,68.0,handball,0,0,0 -891750964,Michaela Hruba,CZE,female,1998-02-21,1.9,75.0,athletics,0,0,0 -504352027,Michaela Meijer,SWE,female,1993-07-30,1.72,60.0,athletics,0,0,0 -616439080,Michaela Metallidou,GRE,female,1993-01-23,1.63,46.0,gymnastics,0,0,0 -339385583,Michail Pateniotis,GRE,male,1984-02-05,1.8,74.0,sailing,0,0,0 -35167157,Michal Balner,CZE,male,1982-09-12,1.91,80.0,athletics,0,0,0 -479154630,Michal Daszek,POL,male,1992-06-27,1.82,88.0,handball,0,0,0 -513183631,Michal Golas,POL,male,1984-04-29,1.8,66.0,cycling,0,0,0 -715508816,Michal Haratyk,POL,male,1992-04-10,1.94,135.0,athletics,0,0,0 -831920477,Michal Izdinsky,FRA,male,1992-07-23,1.78,75.0,aquatics,0,0,0 -903235278,Michal Jurecki,POL,male,1984-10-27,1.98,111.0,handball,0,0,0 -441671788,Michal Kubiak,POL,male,1988-02-23,1.91,80.0,volleyball,0,0,0 -902138771,Michal Kudla,POL,male,1997-10-17,1.88,90.0,canoe,0,0,0 -223037038,Michal Kwiatkowski,POL,male,1990-06-02,1.76,66.0,cycling,0,0,0 -491450653,Michal Pietrzak,POL,male,1989-04-03,1.85,80.0,athletics,0,0,0 -921968213,Michal Smolen,USA,male,1993-09-13,1.81,70.0,canoe,0,0,0 -38283369,Michal Szpakowski,POL,male,1989-04-23,1.95,103.0,rowing,0,0,0 -360956359,Michal Szyba,POL,male,1988-03-18,1.96,97.0,handball,0,0,0 -663753553,Micheen Barbara Thornycroft,ZIM,female,1987-06-26,1.75,72.0,rowing,0,0,0 -297451205,Michel Borges,BRA,male,1991-06-16,1.82,,boxing,0,0,0 -397244117,Michel Torneus,SWE,male,1986-05-26,1.84,73.0,athletics,0,0,0 -954819684,Michele Benedetti,ITA,male,1984-12-17,1.74,68.0,aquatics,0,0,0 -473802345,Michele Santucci,ITA,male,1989-05-30,1.88,82.0,aquatics,0,0,0 -760953520,Michele Scartezzini,ITA,male,1992-01-10,,,cycling,0,0,0 -970855976,Michelle Carter,USA,female,1985-10-12,1.76,136.0,athletics,1,0,0 -829005830,Michelle Coleman,SWE,female,1993-10-31,1.86,74.0,aquatics,0,0,0 -959543328,Michelle Cristina Fazzari,CAN,female,1987-07-10,1.73,58.0,wrestling,0,0,0 -794770350,Michelle Finn,IRL,female,1989-12-16,1.52,51.0,athletics,0,0,0 -694416814,Michelle Goos,NED,female,1989-12-27,1.78,62.0,handball,0,0,0 -900638495,Michelle Heyman,AUS,female,1988-07-04,1.78,66.0,football,0,0,0 -822214721,Michelle Jenneke,AUS,female,1993-06-23,1.72,67.0,athletics,0,0,0 -801535491,Michelle Kasold,USA,female,1987-05-26,1.61,53.0,hockey,0,0,0 -628031032,Michelle Koh,MAS,female,1990-09-30,1.7,70.0,golf,0,0,0 -827836935,Michelle Li,CAN,female,1991-11-03,1.72,66.0,badminton,0,0,0 -949879254,Michelle Pearson,BER,female,1991-04-16,1.77,70.0,rowing,0,0,0 -24450560,Michelle Plouffe,CAN,female,1992-09-15,1.9,82.0,basketball,0,0,0 -190326446,Michelle Vittese,USA,female,1989-12-06,1.61,57.0,hockey,0,0,0 -987215853,Michelle Vorster,NAM,female,1978-09-12,1.67,57.0,cycling,0,0,0 -360738649,Michelle Weber,RSA,female,1996-09-28,1.66,63.0,aquatics,0,0,0 -876460837,Michelle Williams,CAN,female,1991-01-02,1.7,61.0,aquatics,0,0,1 -283186204,Michelle van der Pols,NED,female,1989-01-06,1.72,61.0,hockey,0,0,0 -133448411,Michelle-Lee Ahye,TTO,female,1992-04-10,1.6,64.0,athletics,0,0,0 -478258348,Mick Clohissey,IRL,male,1986-01-13,1.8,64.0,athletics,0,0,0 -814967349,Mickael Gelabale,FRA,male,1983-05-22,2.01,90.0,basketball,0,0,0 -348230526,Mickael Marteau,FRA,male,1992-09-28,1.79,86.0,rowing,0,0,0 -770839524,Mickael-Meba Zeze,FRA,male,1994-05-19,1.75,65.0,athletics,0,0,0 -75146232,Mie Nakashima,JPN,female,1986-06-18,1.6,52.0,hockey,0,0,0 -926621084,Mie Nielsen,DEN,female,1996-09-25,1.85,68.0,aquatics,0,0,1 -521050855,Mieke Kroger,GER,female,1993-07-18,1.81,78.0,cycling,0,0,0 -124498151,Miesinnei Mercy Genesis,NGR,female,1997-09-20,1.52,48.0,wrestling,0,0,0 -585358336,Mifuyu Koide,JPN,female,1995-12-21,1.65,59.0,rugby sevens,0,0,0 -5116041,Migran Arutyunyan,ARM,male,1989-03-25,1.66,67.0,wrestling,0,1,0 -127831066,Miguel Alvarino Garcia,ESP,male,1994-05-31,1.8,84.0,archery,0,0,0 -149765178,Miguel Angel Almachi,ECU,male,1985-05-02,1.65,55.0,athletics,0,0,0 -397125199,Miguel Angel Lopez,ESP,male,1988-07-03,1.81,70.0,athletics,0,0,0 -323518925,Miguel Angel Lopez Castro,CUB,male,1997-03-25,1.89,75.0,volleyball,0,0,0 -280398491,Miguel Angel Lopez Moreno,COL,male,1994-02-04,1.7,58.0,cycling,0,0,0 -937294944,Miguel Arraiolos,POR,male,1988-07-12,1.73,62.0,triathlon,0,0,0 -792866236,Miguel Borja,COL,male,1993-01-26,1.82,84.0,football,0,0,0 -604365100,Miguel Carvalho,POR,male,1994-09-02,1.86,72.0,athletics,0,0,0 -57899480,Miguel Delas,ESP,male,1984-04-13,1.7,72.0,hockey,0,0,0 -308381513,Miguel Duran Navia,ESP,male,1995-09-02,1.93,80.0,aquatics,0,0,0 -36249566,Miguel Ferrera,HON,male,1981-05-25,1.8,80.0,taekwondo,0,0,0 -491799396,Miguel Francis,ANT,male,1995-03-28,1.92,83.0,athletics,0,0,0 -558930917,Miguel Marriaga,VEN,male,1984-06-06,2.06,118.0,basketball,0,0,0 -390736343,Miguel Martinez Palacio,CUB,male,1991-03-23,1.7,66.0,wrestling,0,0,0 -464051241,Miguel Mena,NCA,male,1997-07-07,1.76,69.0,aquatics,0,0,0 -703973257,Miguel Murillo,CRC,male,1993-09-29,1.73,73.0,judo,0,0,0 -656749782,Miguel Ortiz Canavate Ozeki,ESP,male,1991-02-19,1.89,89.0,aquatics,0,0,0 -185068275,Miguel Ruiz,VEN,male,1990-12-20,2.02,104.0,basketball,0,0,0 -430176512,Miguel Tabuena,PHI,male,1994-10-13,1.74,66.0,golf,0,0,0 -658229421,Miguel Ubeto Aponte,VEN,male,1976-09-02,1.68,70.0,cycling,0,0,0 -309781378,Miguel Valente,BRA,male,1993-07-16,1.76,70.0,aquatics,0,0,0 -887734626,Miguel-Angel Reyes-Varela,MEX,male,1987-06-21,1.74,74.0,tennis,0,0,0 -41587201,Miha Zarabec,SLO,male,1991-10-12,1.77,77.0,handball,0,0,0 -623311312,Mihael Zgank,SLO,male,1994-02-01,1.81,90.0,judo,0,0,0 -992573561,Mihaela Maevska,BUL,female,1990-10-04,1.72,53.0,gymnastics,0,0,1 -450996043,Mihaela Petrila,ROU,female,1991-05-07,1.9,74.0,rowing,0,0,1 -81613069,Mihai Nistor,ROU,male,1990-11-05,1.8,,boxing,0,0,0 -467928578,Mihail Anastasakis,GRE,male,1994-12-03,1.84,102.0,athletics,0,0,0 -752147956,Mihail Dudas,SRB,male,1989-11-01,1.84,85.0,athletics,0,0,0 -175275854,Mihail Krassilov,KAZ,male,1990-03-05,1.83,70.0,athletics,0,0,0 -4843017,Mihail Petrov Ganev,BUL,male,1985-01-05,1.72,92.0,wrestling,0,0,0 -369995646,Mihajlo Ceprkalo,BIH,male,1999-06-09,1.72,74.0,aquatics,0,0,0 -830196213,Miho Takahashi,JPN,female,1992-12-01,1.61,56.0,aquatics,0,0,0 -231091970,Miho Teramura,JPN,female,1994-09-27,1.65,53.0,aquatics,0,0,0 -569747933,Miho Yoshioka,JPN,female,1990-08-27,1.77,68.0,sailing,0,0,0 -218617298,Mihyun Park,KOR,female,1986-01-26,1.6,63.0,hockey,0,0,0 -659143143,Mijain Lopez Nunez,CUB,male,1982-08-20,1.98,130.0,wrestling,1,0,0 -427135506,Mika Kurihara,JPN,female,1989-05-14,1.76,68.0,basketball,0,0,0 -945541721,Mikael Appelgren,SWE,male,1989-09-06,1.91,103.0,handball,0,0,0 -629406000,Mikael Ishak,SWE,male,1993-03-31,1.84,74.0,football,0,0,0 -2710032,Mikaela Joslin Mayer,USA,female,1990-07-04,1.76,,boxing,0,0,0 -878544662,Mikalai Sharlap,BLR,male,1994-03-30,1.98,103.0,rowing,0,0,0 -166082362,Mike Dawson,NZL,male,1986-10-15,1.83,75.0,canoe,0,0,0 -281525534,Mike Hartfield,USA,male,1990-03-29,1.91,77.0,athletics,0,0,0 -302902046,Mike Hixon,USA,male,1994-07-16,1.73,68.0,aquatics,0,1,0 -999394723,Mike Mokamba Nyang'au,KEN,male,1994-08-28,1.7,70.0,athletics,0,0,0 -716059894,Mike Rodgers,USA,male,1985-04-24,1.76,80.0,athletics,0,0,0 -929371856,Mikel Schreuders,ARU,male,1998-09-21,1.89,90.0,aquatics,0,0,0 -627661082,Mikel Thomas,TTO,male,1987-11-23,1.7,79.0,athletics,0,0,0 -109189780,Mikhail Dauhaliavets,BLR,male,1990-05-18,1.77,,boxing,0,0,0 -217263774,Mikhail Dovgalyuk,RUS,male,1995-06-03,,,aquatics,0,0,0 -138028765,Mikhail Koudinov,NZL,male,1991-06-23,1.6,59.0,gymnastics,0,0,0 -666617738,Mikhail Kuznetsov,RUS,male,1985-05-14,1.71,72.0,canoe,0,0,0 -161664881,Mikhail Puliaev,RUS,male,1987-06-22,1.66,66.0,judo,0,0,0 -775068064,Miki Uchida,JPN,female,1995-02-21,1.73,62.0,aquatics,0,0,0 -861670470,Mikiko Andoh,JPN,female,1992-09-30,1.55,58.0,weightlifting,0,0,0 -832567924,Mikita Tsirkun,BLR,male,1997-03-24,1.84,72.0,sailing,0,0,0 -386269278,Mikita Tsmyh,BLR,male,1997-04-15,1.9,80.0,aquatics,0,0,0 -178113839,Mikkel Hansen,DEN,male,1987-10-22,1.96,98.0,handball,1,0,0 -342879718,Mikko Ilonen,FIN,male,1979-12-18,1.87,81.0,golf,0,0,0 -172389499,Miklos Cirjenics,HUN,male,1990-03-11,1.9,105.0,judo,0,0,0 -848330802,Miklos Srp,HUN,male,1993-03-06,2.0,83.0,athletics,0,0,0 -814553840,Miklos Tatrai,HUN,male,1986-09-19,1.83,96.0,shooting,0,0,0 -535398234,Miklos Ungvari,HUN,male,1980-10-15,1.76,76.0,judo,0,0,0 -518737451,Mikola Milchev,UKR,male,1967-11-03,1.8,80.0,shooting,0,0,0 -67855529,Mikolaj Burda,POL,male,1982-07-08,1.92,98.0,rowing,0,0,0 -220680579,Miku Tashiro,JPN,female,1994-04-07,1.63,63.0,judo,0,0,0 -817880722,Milad Beigi Harchegani,AZE,male,1991-03-01,1.97,80.0,taekwondo,0,0,1 -305207237,Milad Ebadipour Ghara H.,IRI,male,1993-10-17,1.96,78.0,volleyball,0,0,0 -825689633,Milagro Mena,CRC,female,1993-04-30,,,cycling,0,0,0 -588122554,Milan Aleksic,SRB,male,1986-05-13,1.93,96.0,aquatics,1,0,0 -34031810,Milan Macvan,SRB,male,1989-11-16,2.05,107.0,basketball,0,1,0 -994546507,Milan Ristic,SRB,male,1991-08-08,1.87,83.0,athletics,0,0,0 -812447465,Milan Trajkovic,CYP,male,1992-09-17,1.87,82.0,athletics,0,0,0 -39526304,Milana Dadasheva,RUS,female,1995-02-20,1.65,48.0,wrestling,0,0,0 -997943868,Milda Valciukaite,LTU,female,1994-05-24,1.76,66.0,rowing,0,0,1 -757771656,Mildrey Pineda,COL,female,1989-10-01,1.64,54.0,football,0,0,0 -816442333,Milena Raicevic,MNE,female,1990-03-12,1.77,71.0,handball,0,0,0 -627051763,Milena Rasic,SRB,female,1990-10-25,1.91,72.0,volleyball,0,1,0 -930041912,Milenko Sebic,SRB,male,1984-12-30,1.97,95.0,shooting,0,0,0 -162979267,Milenko Zoric,SRB,male,1989-04-02,1.79,73.0,canoe,0,1,0 -689604423,Miles Chamley-Watson,USA,male,1989-12-03,1.94,79.0,fencing,0,0,1 -669028913,Miles Ukaoma,NGR,male,1992-07-21,1.86,84.0,athletics,0,0,0 -696295930,Milica Dabovic,SRB,female,1982-02-16,1.73,63.0,basketball,0,0,1 -545251812,Milica Mandic,SRB,female,1991-12-06,1.8,72.0,taekwondo,0,0,0 -294142,Milica Starovic,SRB,female,1988-05-19,1.76,74.0,canoe,0,0,0 -939197606,Militsa Mircheva,BUL,female,1994-05-24,1.65,50.0,athletics,0,0,0 -745870544,Milivoj Dukic,MNE,male,1993-03-26,1.85,83.0,sailing,0,0,0 -263697348,Milko Olavi Tokola,FIN,male,1992-10-10,1.63,85.0,weightlifting,0,0,0 -806789845,Milly Clark,AUS,female,1989-03-01,,,athletics,0,0,0 -543042822,Milos Cuk,SRB,male,1990-12-21,1.91,91.0,aquatics,1,0,0 -824446870,Milos Scepanovic,MNE,male,1982-10-09,1.85,86.0,aquatics,0,0,0 -696058975,Milos Teodosic,SRB,male,1987-03-19,1.95,90.0,basketball,0,1,0 -434422235,Milos Vasic,SRB,male,1991-01-10,1.96,94.0,rowing,0,0,0 -619216736,Milosz Jankowski,POL,male,1990-01-17,1.93,70.0,rowing,0,0,0 -392630546,Miloud Rahmani,ALG,male,1982-12-30,1.78,62.0,athletics,0,0,0 -923155291,Miloud Rebiai,ALG,male,1993-12-12,1.7,65.0,football,0,0,0 -917223658,Miltiadis Tentoglou,GRE,male,1998-03-18,1.85,75.0,athletics,0,0,0 -939168667,Milutin Stefanovic,SRB,male,1985-01-23,1.8,74.0,shooting,0,0,0 -94698500,Mima Ito,JPN,female,2000-10-21,1.5,45.0,table tennis,0,0,1 -730531395,Mimi Belete,BRN,female,1988-06-09,1.69,56.0,athletics,0,0,0 -299491184,Mimi Nikolova Hristova,BUL,female,1993-07-19,1.63,58.0,wrestling,0,0,0 -956895312,Mimosa Jallow,FIN,female,1994-06-17,1.76,67.0,aquatics,0,0,0 -84728158,Min Zhang,CHN,female,1993-06-20,1.8,84.0,rowing,0,0,0 -879015845,Min Zhou,CHN,female,1997-12-16,1.72,64.0,aquatics,0,0,0 -682581945,Minami Itahashi,JPN,female,2000-01-28,1.5,47.0,aquatics,0,0,0 -454894249,Minami Shimizu,JPN,female,1993-07-14,1.58,53.0,hockey,0,0,0 -352623895,Mindaugas Griskonis,LTU,male,1986-01-17,1.9,92.0,rowing,0,1,0 -790457754,Mindaugas Kuzminskas,LTU,male,1989-10-19,2.04,100.0,basketball,0,0,0 -489620720,Ming Tai Chan,HKG,male,1995-01-30,1.75,60.0,athletics,0,0,0 -405125709,Ming-Yen Tsai,TPE,male,1996-01-07,1.67,63.0,judo,0,0,0 -44722504,Minggang Zhao,CHN,male,1988-05-30,1.85,,boxing,0,0,0 -142526569,Minhal Sohail,PAK,female,1995-01-03,1.59,63.0,shooting,0,0,0 -40888029,Minjee Lee,AUS,female,1996-05-27,1.65,60.0,golf,0,0,0 -695744582,Minjeong Kim,KOR,female,1988-08-08,1.79,95.0,judo,0,0,0 -55604401,Minjung Kim,KOR,female,1997-03-26,1.6,50.0,shooting,0,0,0 -353713812,Mink van der Weerden,NED,male,1988-10-19,1.78,83.0,hockey,0,0,0 -504800343,Minkyu Choi,KOR,male,1992-08-31,1.84,84.0,canoe,0,0,0 -995871061,Minna Nikkanen,FIN,female,1988-04-09,1.69,54.0,athletics,0,0,0 -215136501,Minsoo Park,KOR,male,1994-11-21,1.63,,gymnastics,0,0,0 -575636398,Minsu Kang,KOR,male,1986-03-31,1.73,76.0,shooting,0,0,0 -800631700,Mintae Kim,KOR,male,1993-11-26,1.87,78.0,football,0,0,0 -875570651,Minxia Wu,CHN,female,1985-11-10,1.67,53.0,aquatics,1,0,0 -989837218,Mio Yamanaka,JPN,female,1995-10-27,1.57,56.0,rugby sevens,0,0,0 -646593349,Mir Saeid Marouflakrani,IRI,male,1985-10-20,1.89,81.0,volleyball,0,0,0 -254218441,Mira Park,KOR,female,1987-04-20,1.73,66.0,handball,0,0,0 -793097599,Mira Potkonen,FIN,female,1980-11-17,1.65,,boxing,0,0,1 -700649053,Mirali Sharipov,UZB,male,1987-10-30,1.73,73.0,judo,0,0,0 -419020833,Miranda Ayim,CAN,female,1988-05-06,1.9,72.0,basketball,0,0,0 -329752486,Miranda Giambelli,AUS,female,1992-05-22,1.69,78.0,judo,0,0,0 -589013959,Miranda Melville,USA,female,1989-03-20,1.58,49.0,athletics,0,0,0 -539312354,Mirco Pruijser,NED,male,1989-08-11,1.94,87.0,hockey,0,0,0 -16778625,Mirco Scarantino,ITA,male,1995-01-16,1.66,56.0,weightlifting,0,0,0 -837308127,Mireia Belmonte Garcia,ESP,female,1990-11-10,1.7,59.0,aquatics,1,0,1 -146069322,Mirela Demireva,BUL,female,1989-09-28,1.8,58.0,athletics,0,1,0 -164294275,Miri Alatrash,PLE,female,1994-06-27,1.7,57.0,aquatics,0,0,0 -95824314,Miriam Casillas Garcia,ESP,female,1992-06-24,1.64,52.0,triathlon,0,0,0 -638255791,Miriam Nagl,BRA,female,1981-01-22,1.72,63.0,golf,0,0,0 -929012474,Miriam Welte,GER,female,1986-12-09,1.71,67.0,cycling,0,0,1 -1578040,Mirna Ortiz,GUA,female,1987-02-28,1.58,42.0,athletics,0,0,0 -232798946,Miro Bilan,CRO,male,1989-07-21,2.13,121.0,basketball,0,0,0 -69797437,Miroslav Kirchev,BUL,male,1990-06-12,1.86,78.0,canoe,0,0,0 -727165956,Miroslav Raduljica,SRB,male,1988-01-05,2.13,130.0,basketball,0,1,0 -702761980,Miroslav Vrastil,CZE,male,1982-10-17,1.84,83.0,rowing,0,0,0 -237763343,Miroslava Topinkova Knapkova,CZE,female,1980-09-19,1.81,72.0,rowing,0,0,0 -28185060,Miroslaw Zietarski,POL,male,1993-03-09,1.9,90.0,rowing,0,0,0 -508919033,Mirsamad Pourseyedigolakhour,IRI,male,1985-10-15,1.8,65.0,cycling,0,0,0 -515513440,Miryam Roper,GER,female,1982-06-26,1.65,59.0,judo,0,0,0 -824109825,Mirza Basic,BIH,male,1991-07-12,1.87,85.0,tennis,0,0,0 -401258599,Misael Uziel Rodriguez,MEX,male,1994-04-07,1.78,,boxing,0,0,1 -446490717,Misaki Doi,JPN,female,1991-04-29,1.59,52.0,tennis,0,0,0 -719250032,Misaki Matsutomo,JPN,female,1992-02-08,1.6,50.0,badminton,1,0,0 -387566065,Misaki Onishi,JPN,female,1985-02-24,1.64,46.0,athletics,0,0,0 -779502274,Misaki Yamaguchi,JPN,female,1990-01-20,1.65,60.0,aquatics,0,0,0 -958405399,Misato Nakamura,JPN,female,1989-04-28,1.57,52.0,judo,0,0,1 -355342299,Misha Aloian,RUS,male,1988-08-23,1.65,,boxing,0,1,0 -787920390,Misha Zilberman,ISR,male,1989-01-30,1.71,61.0,badminton,0,0,0 -460301568,Missy Franklin,USA,female,1995-05-10,1.88,77.0,aquatics,1,0,0 -240018946,Misun Choi,KOR,female,1996-07-01,1.68,53.0,archery,1,0,0 -396364855,Mitch Dielemans,NED,male,1993-01-06,1.86,75.0,archery,0,0,0 -858784147,Mitch Emery,AUS,male,1990-09-27,1.85,89.0,aquatics,0,0,0 -481579548,Mitchel Steenman,NED,male,1984-06-17,2.01,94.0,rowing,0,0,0 -852265607,Mitchell Iles-Crevatin,AUS,male,1999-03-25,1.78,,shooting,0,0,0 -248884983,Mitchell Larkin,AUS,male,1993-07-09,1.87,72.0,aquatics,0,1,1 -882242965,Mitko Tsenov,BUL,male,1993-06-13,1.85,65.0,athletics,0,0,0 -211027286,Mitsuaki Shiga,JPN,male,1991-09-16,1.77,75.0,aquatics,0,0,0 -478951301,Miyu Nagaoka,JPN,female,1991-07-25,1.79,68.0,volleyball,0,0,0 -267807948,Miyuki Nakagawa,JPN,female,1986-12-20,1.61,53.0,hockey,0,0,0 -224192302,Miyuki Uehara,JPN,female,1995-11-22,1.54,39.0,athletics,0,0,0 -238146440,Mladan JANOVIC,MNE,male,1984-06-11,1.8,97.0,aquatics,0,0,0 -803073046,Mo Zhang,CAN,female,1989-01-17,1.73,57.0,table tennis,0,0,0 -716769230,Moacir Zimmermann,BRA,male,1983-12-30,1.84,70.0,athletics,0,0,0 -177889659,Mobolade Ajomale,CAN,male,1995-08-31,1.8,62.0,athletics,0,0,1 -30183396,Moeko Nagaoka,JPN,female,1993-12-29,1.77,73.0,basketball,0,0,0 -446613849,Mohab Ishak,EGY,male,1997-03-21,1.72,68.0,aquatics,0,0,0 -810751335,Mohamad Kasem,SYR,male,1993-10-03,1.72,73.0,judo,0,0,0 -812147248,Mohamed Abdelaal,EGY,male,1990-07-23,1.75,81.0,judo,0,0,0 -597913544,Mohamed Abdeldjalil Bourguieg,ALG,male,1996-08-31,1.55,50.0,gymnastics,0,0,0 -836418464,Mohamed Alaa Hashem,EGY,male,1988-01-23,1.78,75.0,handball,0,0,0 -414844442,Mohamed Ali Bhar,TUN,male,1989-09-17,1.8,82.0,handball,0,0,0 -300150573,Mohamed Ali Mrabet,TUN,male,1990-01-01,1.82,84.0,canoe,0,0,0 -803678104,Mohamed Aly Zaghloul Mohamed,EGY,male,1993-08-31,1.65,86.0,wrestling,0,0,0 -583592517,Mohamed Amer,EGY,male,1997-12-18,1.99,80.0,fencing,0,0,0 -452585583,Mohamed Amer,EGY,male,1987-12-12,1.86,72.0,handball,0,0,0 -150604584,Mohamed Andhumdine Nazlati,COM,female,1997-12-20,1.6,62.0,aquatics,0,0,0 -148746639,Mohamed Arafet Naceur,TUN,male,1988-11-04,1.9,76.0,volleyball,0,0,0 -509599615,Mohamed Ayoub Ferjani,TUN,male,1986-07-27,1.75,75.0,fencing,0,0,0 -219235142,Mohamed Daud Mohamed,SOM,male,1996-03-01,,,athletics,0,0,0 -310689410,Mohamed Elhadi Elkawisah,LBA,male,1987-03-08,,,judo,0,0,0 -956253727,Mohamed Essam,EGY,male,1994-12-06,1.8,74.0,fencing,0,0,0 -302433626,Mohamed Farah,GBR,male,1983-03-23,1.75,58.0,athletics,2,0,0 -464902728,Mohamed Flissi,ALG,male,1990-02-13,1.74,,boxing,0,0,0 -662693282,Mohamed Hamout,MAR,male,1993-12-11,1.67,,boxing,0,0,0 -189738440,Mohamed Hamza,EGY,male,2000-09-11,1.72,62.0,fencing,0,0,0 -653185102,Mohamed Hesham Elbassiouny,EGY,male,1990-05-10,1.84,75.0,handball,0,0,0 -173845600,Mohamed Hrezi,LBA,male,1991-10-28,,,athletics,0,0,0 -53291565,Mohamed Hussein,EGY,male,1991-09-10,1.88,78.0,aquatics,0,0,0 -705810811,Mohamed Ibrahim Ramadan,EGY,male,1984-03-07,1.87,89.0,handball,0,0,0 -606398332,Mohamed Ismail Ibrahim,DJI,male,1997-01-01,1.7,63.0,athletics,0,0,0 -777542037,Mohamed Jilani Maaref,TUN,male,1991-10-27,1.92,100.0,handball,0,0,0 -438310446,Mohamed Lamine Dansoko,GUI,male,1998-03-15,1.81,78.0,athletics,0,0,0 -726944048,Mohamed Mahmoud,EGY,male,1989-11-21,1.65,77.0,weightlifting,0,0,1 -927181230,Mohamed Mamdouh Shebib,EGY,male,1989-04-01,1.86,85.0,handball,0,0,0 -608771921,Mohamed Masoud,EGY,male,1994-05-01,2.11,105.0,volleyball,0,0,0 -422177442,Mohamed Mohyeldin,EGY,male,1991-10-01,1.7,73.0,judo,0,0,0 -463171596,Mohamed Ramah,MAR,male,1986-01-02,1.86,102.0,shooting,0,0,0 -407211801,Mohamed Saadaoui,TUN,male,1995-05-11,1.76,86.0,wrestling,0,0,0 -109252911,Mohamed Sbihi,GBR,male,1988-03-27,2.02,110.0,rowing,1,0,0 -385884277,Mohamed Sghaier,TUN,male,1988-07-18,1.85,79.0,athletics,0,0,0 -645636204,Mohamed Soussi,TUN,male,1993-01-17,1.94,98.0,handball,0,0,0 -307662595,Mohamed Taieb,TUN,male,1996-10-15,1.87,73.0,rowing,0,0,0 -823399635,Mohamed Thakil,EGY,male,1986-07-12,1.84,71.0,volleyball,0,0,0 -514503427,Mohammad Ahsan,INA,male,1987-09-07,1.73,72.0,badminton,0,0,0 -653470021,Mohammad Arzandeh,IRI,male,1987-10-30,1.89,77.0,athletics,0,0,0 -855226703,Mohammad Mahfizur Rahman,BAN,male,1993-05-15,,,aquatics,0,0,0 -663027391,Mohammad Sanad,EGY,male,1991-01-16,1.84,78.0,handball,0,0,0 -289057786,Mohammad Tawfiq Bakhshi,AFG,male,1986-03-11,1.81,99.0,judo,0,0,0 -175246089,Mohammadjafar Moradi,IRI,male,1990-04-10,1.69,62.0,athletics,0,0,0 -545080658,Mohammadreza Barari,IRI,male,1988-03-31,1.8,105.0,weightlifting,0,0,0 -416345678,Mohammed Abukhousa,PLE,male,1992-12-30,,,athletics,0,0,0 -926792518,Mohammed Ahmed,CAN,male,1991-01-05,1.82,56.0,athletics,0,0,0 -404954189,Mohammed Al-Khafaji,IRQ,male,1994-02-20,1.81,75.0,rowing,0,0,0 -597431856,Mohammed Aman,ETH,male,1994-01-10,1.65,58.0,athletics,0,0,0 -690873472,Mohammed Amine Tayeb,ALG,male,1985-09-28,1.9,100.0,judo,0,0,0 -207078605,Mohammed Arjaoui,MAR,male,1987-06-06,1.86,,boxing,0,0,0 -955962862,Mohammed Benkablia,ALG,male,1993-02-02,1.8,72.0,football,0,0,0 -268626951,Mohammed Benkhemassa,ALG,male,1993-06-28,1.6,60.0,football,0,0,0 -963049864,Mohammed Hameed,IRQ,male,1993-01-24,1.85,75.0,football,0,0,0 -104121143,Mohammed Maan,IRQ,male,1994-07-10,1.85,78.0,football,0,0,0 -200493972,Mohammed Rabii,MAR,male,1993-07-13,1.8,,boxing,0,0,1 -984836225,Mohammed Rageh,YEM,male,1998-01-01,1.69,61.0,athletics,0,0,0 -106274200,Mohanad Abdulraheem,IRQ,male,1993-09-22,1.84,75.0,football,0,0,0 -582863169,Mohsen Al Duhaylib,KSA,male,1994-05-01,1.63,69.0,weightlifting,0,0,0 -412108690,Moises Daniel Hernandez Encarnacion,DOM,male,1993-03-22,1.91,79.0,taekwondo,0,0,0 -282645376,Moises Duque,BRA,male,1988-12-21,1.73,75.0,rugby sevens,0,0,0 -974378104,Mojtaba Abedini,IRI,male,1984-08-11,1.8,83.0,fencing,0,0,0 -790224130,Mojtaba Mirzajanpour M.,IRI,male,1991-10-07,2.05,88.0,volleyball,0,0,0 -421422970,Molly Goodman,AUS,female,1993-02-19,,,rowing,0,0,0 -942675571,Molly Hannis,USA,female,1992-03-13,1.71,70.0,aquatics,0,0,0 -509619259,Molly Huddle,USA,female,1984-08-31,1.66,48.0,athletics,0,0,0 -736619678,Molly Meech,NZL,female,1993-03-31,1.78,74.0,sailing,0,1,0 -770966520,Molly Renshaw,GBR,female,1996-05-06,1.76,66.0,aquatics,0,0,0 -146611617,Mona Shaito,LIB,female,1994-05-12,1.52,52.0,fencing,0,0,0 -330073847,Monica,BRA,female,1987-04-21,1.68,64.0,football,0,0,0 -465263667,Monica Aksamit,USA,female,1990-02-18,1.83,74.0,fencing,0,0,1 -2092982,Monica Brennan,AUS,female,1994-01-22,1.75,58.0,athletics,0,0,0 -964250913,Monica Lanz,NED,female,1991-04-08,1.8,70.0,rowing,0,0,0 -251342702,Monica Niculescu,ROU,female,1987-09-25,1.68,64.0,tennis,0,0,0 -839524352,Monica Patricia Dominguez Lara,MEX,female,1988-03-05,1.62,57.0,weightlifting,0,0,0 -9559634,Monica Pimentel Rodriguez,ARU,female,1989-01-07,1.71,49.0,taekwondo,0,0,0 -490158961,Monica Puig,PUR,female,1993-09-27,1.68,64.0,tennis,1,0,0 -335904579,Monica Ramirez Abella,AND,female,1993-12-27,1.7,55.0,aquatics,0,0,0 -31059637,Monica Rokhman,USA,female,1997-05-27,1.73,55.0,gymnastics,0,0,0 -292580115,Monica Sarai Arango Estrada,COL,female,1992-06-05,1.65,60.0,aquatics,0,0,0 -413592133,Monica Ungureanu,ROU,female,1988-01-09,1.64,48.0,judo,0,0,0 -364893024,Monica de Gennaro,ITA,female,1987-01-08,1.74,67.0,volleyball,0,0,0 -494339957,Monika,IND,female,1993-11-05,1.62,51.0,hockey,0,0,0 -560189163,Monika Brzostek,POL,female,1989-07-28,1.73,62.0,volleyball,0,0,0 -737506601,Monika Ciaciuch,POL,female,1992-05-10,1.82,74.0,rowing,0,0,1 -313838269,Monika Ewa Michalik,POL,female,1980-05-02,1.67,63.0,wrestling,0,0,1 -335727290,Monika Karsch,GER,female,1982-12-22,1.58,55.0,shooting,0,1,0 -216289131,Monika Stefanowicz,POL,female,1980-05-15,1.6,46.0,athletics,0,0,0 -138520050,Monika Vasilyan,ARM,female,1995-10-08,1.66,59.0,aquatics,0,0,0 -643397164,Monique Sullivan,CAN,female,1989-02-21,1.67,70.0,cycling,0,0,0 -256721799,Morad Zemouri,QAT,male,1993-03-03,1.73,73.0,judo,0,0,0 -288223126,Morales Silva,CUB,male,1996-02-08,1.8,81.0,judo,0,0,0 -42332624,Morea Baru,PNG,male,1990-04-15,1.62,62.0,weightlifting,0,0,0 -522289399,Morena Martinez Franchi,ARG,female,1993-02-19,1.64,62.0,volleyball,0,0,0 -629625273,Morgan Brian,USA,female,1993-02-26,1.7,56.0,football,0,0,0 -933274052,Morgan Craft,USA,female,1993-05-11,1.61,63.0,shooting,0,0,0 -539265571,Morgan Lake,GBR,female,1997-05-12,1.78,68.0,athletics,0,0,0 -678637275,Morgan Mitchell,AUS,female,1994-10-03,1.77,63.0,athletics,0,0,0 -776634450,Morgana Gmach,BRA,female,1994-06-17,1.59,44.0,gymnastics,0,0,0 -350635188,Morghan Whitney King,USA,female,1985-10-08,1.53,48.0,weightlifting,0,0,0 -249898083,Moritz Furste,GER,male,1984-10-28,1.9,89.0,hockey,0,0,1 -613123178,Moritz Milatz,GER,male,1982-06-24,1.73,65.0,cycling,0,0,0 -820494787,Moritz Moos,GER,male,1994-03-15,1.76,71.0,rowing,0,0,0 -354728238,Moritz Trompertz,GER,male,1995-09-21,1.8,76.0,hockey,0,0,1 -511776561,Moroke Jeremia Mokhotho,LES,male,1990-11-12,,,boxing,0,0,0 -177555215,Morolake Akinosun,USA,female,1994-05-17,1.63,60.0,athletics,1,0,0 -174861233,Morten Joergensen,DEN,male,1985-06-23,1.83,73.0,rowing,0,1,0 -981709933,Morten Olsen,DEN,male,1984-10-11,1.84,95.0,handball,1,0,0 -757235037,Moses Martin Kurong,UGA,male,1994-07-07,,,athletics,0,0,0 -305593512,Mosito Lehata,LES,male,1989-04-08,1.77,69.0,athletics,0,0,0 -775280640,Mostafa Sharifat,IRI,male,1987-09-16,2.04,85.0,volleyball,0,0,0 -648612446,Mostafa Smaili,MAR,male,1997-01-09,1.74,63.0,athletics,0,0,0 -164610408,Mothobi Mvala,RSA,male,1994-06-14,1.76,67.0,football,0,0,0 -906175629,Motomi Kawamura,JPN,female,1996-03-05,1.57,54.0,hockey,0,0,0 -853620753,Mouhssine Lahsaini,MAR,male,1985-08-23,1.84,77.0,cycling,0,0,0 -22631231,Mouma Das,IND,female,1984-02-24,1.49,47.0,table tennis,0,0,0 -61885653,Mpi Anauel Ngamissengue,CGO,male,1996-02-03,1.7,,boxing,0,0,0 -228902925,Mpumi Nyandeni,RSA,female,1987-08-19,1.62,50.0,football,0,0,0 -176457455,Muamer Tankovic,SWE,male,1995-02-22,1.8,70.0,football,0,0,0 -111349032,Muenfuh Sincere,NGR,male,1998-04-28,1.7,,football,0,0,1 -620003817,Muhamad Hasbi,INA,male,1992-07-12,1.58,62.0,weightlifting,0,0,0 -955910708,Muhammad Akmal Nor Hasrin,MAS,male,1995-07-15,1.8,74.0,archery,0,0,0 -545267756,Muhammad Ali,GBR,male,1996-06-20,1.73,,boxing,0,0,0 -758048391,Muhammad Halim,ISV,male,1986-10-26,1.91,82.0,athletics,0,0,0 -175347040,Muhammad Wijaya,INA,male,1996-08-22,1.71,70.0,archery,0,0,0 -864313645,Muhammed Anas Yahiya,IND,male,1994-09-17,1.77,69.0,athletics,0,0,0 -824855418,Muhao Li,CHN,male,1992-06-02,2.18,115.0,basketball,0,0,0 -137651650,Mujinga Kambundji,SUI,female,1992-06-17,1.68,65.0,athletics,0,0,0 -313782663,Mukhamadmurod Abdurakhmonov,TJK,male,1986-11-29,1.92,117.0,judo,0,0,0 -422893498,Mukhlid Alotaibi,KSA,male,1976-06-20,1.63,60.0,athletics,0,0,0 -933102788,Muktar Edris,ETH,male,1994-01-14,1.71,60.0,athletics,0,0,0 -501615561,Mulern Jean,HAI,female,1992-09-25,,,athletics,0,0,0 -752439313,Mulomowandau Mathoho,RSA,male,1990-03-01,1.78,75.0,football,0,0,0 -395323200,Mumin Gala,DJI,male,1986-09-06,1.85,70.0,athletics,0,0,0 -380366841,Muminjon Abdullaev,UZB,male,1989-12-24,1.9,130.0,wrestling,0,0,0 -700821397,Munkhzaya Bayartsogt,MGL,female,1993-10-10,1.6,43.0,athletics,0,0,0 -44840352,Munkhzaya Tsedevsuren,MGL,female,1986-06-13,1.65,65.0,judo,0,0,0 -242791485,Munkhzul Tsogbadrakh,MGL,female,1981-02-28,1.74,65.0,shooting,0,0,0 -703714683,Munyo Solomon Mutai,UGA,male,1992-10-22,,,athletics,0,0,0 -908231867,Murat Ramonov,KGZ,male,1990-07-21,1.92,120.0,wrestling,0,0,0 -241961956,Muriel Coneo,COL,female,1987-03-15,1.6,50.0,athletics,0,0,0 -845100767,Murielle Ahoure,CIV,female,1987-08-23,1.65,58.0,athletics,0,0,0 -313174082,Murilo Antonio Fischer,BRA,male,1979-06-16,1.7,68.0,cycling,0,0,0 -411476785,Murodjon Akhmadaliev,UZB,male,1994-11-02,1.65,,boxing,0,0,1 -534931107,Murphy Troy,USA,male,1989-05-31,2.02,99.0,volleyball,0,0,1 -294093955,Murray Stewart,AUS,male,1986-07-02,1.86,86.0,canoe,0,0,0 -774480033,Musa Hajdari,KOS,male,1987-10-11,1.87,74.0,athletics,0,0,0 -260677878,Musaeb Abdulrahman Balla,QAT,male,1989-03-19,1.85,65.0,athletics,0,0,0 -249810115,Musashi Suzuki,JPN,male,1994-02-11,1.85,75.0,football,0,0,0 -98192269,Mussa Chamaune,MOZ,male,1992-08-19,1.72,66.0,canoe,0,0,0 -315205581,Mustafa Alsaltialkrad,QAT,male,1987-03-16,1.86,90.0,handball,0,0,0 -759784132,Mustafa Kaya,TUR,male,1992-06-06,1.64,65.0,wrestling,0,0,0 -610760480,Mustafa Nadhim,IRQ,male,1993-09-23,1.82,75.0,football,0,0,0 -434308587,Mutaz Essa Barshim,QAT,male,1991-06-24,1.9,65.0,athletics,0,1,0 -963065622,Myeongjun Son,KOR,male,1994-01-17,1.81,62.0,athletics,0,0,0 -828123780,Myeongmok Han,KOR,male,1991-02-01,1.6,62.0,weightlifting,0,0,0 -94171394,Mykhailo Romanchuk,UKR,male,1996-08-07,1.9,82.0,aquatics,0,0,0 -462238804,Mykola Butsenko,UKR,male,1991-06-25,1.7,,boxing,0,0,0 -370313350,Mykyta Nesterenko,UKR,male,1991-04-15,2.08,120.0,athletics,0,0,0 -53536605,Mynhardt Mbeumuna Kawanivi,NAM,male,1984-03-03,1.78,59.0,athletics,0,0,0 -321314290,Myong Hyok Kim,PRK,male,1990-12-03,1.64,69.0,weightlifting,0,0,0 -450507588,Myong Suk Jong,PRK,female,1993-02-06,1.61,53.0,wrestling,0,0,0 -719432881,Myong Sun Ri,PRK,female,1992-01-26,1.63,53.0,table tennis,0,0,0 -985606742,Myriam Fatime Sylla,ITA,female,1995-01-08,1.84,80.0,volleyball,0,0,0 -530677071,Myrthe Schoot,NED,female,1988-08-29,1.83,70.0,volleyball,0,0,0 -143551773,Na Wang,CHN,female,1994-08-05,1.66,59.0,hockey,0,0,0 -834890793,Nacif Elias,LIB,male,1988-09-29,1.72,78.0,judo,0,0,0 -997747457,Nada Albedwawi,UAE,female,1997-08-15,1.62,53.0,aquatics,0,0,0 -829297928,Nada Arakji,QAT,female,1994-10-30,1.63,59.0,aquatics,0,0,0 -718461351,Nada Daabousova,SVK,female,1997-01-15,1.65,52.0,aquatics,0,0,0 -512802404,Nada Hafez,EGY,female,1997-08-28,1.68,58.0,fencing,0,0,0 -36934955,Nada Meawad,EGY,female,1998-04-12,1.8,71.0,volleyball,0,0,0 -704571555,Nada Saafan,EGY,female,1996-09-10,1.68,53.0,aquatics,0,0,0 -813043984,Nadeen El-Dawlatly,EGY,female,1993-06-22,1.62,54.0,table tennis,0,0,0 -494612343,Nadezhda Bazhina,RUS,female,1987-12-29,1.7,59.0,aquatics,0,0,0 -528001593,Nadezhda Glyzina,RUS,female,1988-05-20,1.75,68.0,aquatics,0,0,1 -139491644,Nadia Centoni,ITA,female,1981-06-19,1.82,63.0,volleyball,0,0,0 -895831974,Nadia Colhado,BRA,female,1989-02-25,1.94,88.0,basketball,0,0,0 -551473101,Nadia Negm,EGY,female,1998-07-23,1.7,69.0,rowing,0,0,0 -460024379,Nadiezda Zieba,POL,female,1984-05-21,1.72,64.0,badminton,0,0,0 -369509430,Nadiia Kichenok,UKR,female,1992-07-20,1.75,65.0,tennis,0,0,0 -313426980,Nadine Broersen,NED,female,1990-04-29,1.71,62.0,athletics,0,0,0 -938928683,Nadine Gonska,GER,female,1990-01-23,1.68,58.0,athletics,0,0,0 -520205566,Nadine Hildebrand,GER,female,1987-09-20,1.59,55.0,athletics,0,0,0 -3180101,Nadine Muller,GER,female,1985-11-21,1.93,94.0,athletics,0,0,0 -735909394,Nadine Visser,NED,female,1995-02-09,1.75,61.0,athletics,0,0,0 -774153676,Nadine Zumkehr,SUI,female,1985-02-05,1.72,65.0,volleyball,0,0,0 -963806165,Nadiya Borovska,UKR,female,1981-02-25,1.63,50.0,athletics,0,0,0 -228904239,Nadiya Dusanova,UZB,female,1987-11-17,1.74,56.0,athletics,0,0,0 -688425243,Nadja Horwitz,CHI,female,1996-03-10,1.66,58.0,sailing,0,0,0 -271635042,Nadja Pries,GER,female,1994-05-20,1.61,56.0,cycling,0,0,0 -322606304,Nadzeya Liapeshka,BLR,female,1989-04-26,1.72,67.0,canoe,0,0,1 -799009112,Nafissatou Thiam,BEL,female,1994-08-19,1.87,72.0,athletics,1,0,0 -523471843,Nagisa Hayashi,JPN,female,1986-08-29,1.65,58.0,hockey,0,0,0 -848586988,Naho Miyoshi,JPN,female,1993-12-21,1.65,62.0,basketball,0,0,0 -742568072,Naiara Egozkue,ESP,female,1983-10-21,1.73,70.0,handball,0,0,0 -701395318,Naima Bakkal,MAR,female,1990-08-28,1.7,57.0,taekwondo,0,0,0 -692114409,Naing Soe Yan,MYA,male,1979-01-31,1.8,98.0,judo,0,0,0 -276705406,Naito Ehara,JPN,male,1993-07-30,1.72,59.0,aquatics,0,0,1 -397860366,Najima Parveen,PAK,female,1990-12-20,,,athletics,0,0,0 -787936059,Najmeh Khedmati,IRI,female,1996-06-09,1.59,64.0,shooting,0,0,0 -414759552,Namakoe Nkhasi,LES,male,1993-01-10,,,athletics,0,0,0 -910960289,Namika Matsumoto,JPN,female,1992-02-07,1.58,63.0,weightlifting,0,0,0 -539278913,Namita Toppo,IND,female,1995-06-04,1.63,53.0,hockey,0,0,0 -921608416,Nan Chen,CHN,female,1983-01-08,1.95,94.0,basketball,0,0,0 -131634277,Nan Zhang,CHN,male,1990-03-01,1.83,75.0,badminton,1,0,1 -817788274,Nancy Chepkwemoi,KEN,female,1993-10-08,1.21,47.0,athletics,0,0,0 -533602756,Nandinzaya Gankhuyag,MGL,female,1994-06-27,1.55,56.0,shooting,0,0,0 -641363235,Nando De Colo,FRA,male,1987-06-23,1.96,90.0,basketball,0,0,0 -209921871,Nanna Koerstz Madsen,DEN,female,1994-10-23,1.71,67.0,golf,0,0,0 -634283561,Nanna Vainio,FIN,female,1991-05-29,1.7,60.0,badminton,0,0,0 -679632915,Nanthana Komwong,THA,female,1980-09-13,1.59,54.0,table tennis,0,0,0 -95609257,Nao Hibino,JPN,female,1994-11-28,1.63,58.0,tennis,0,0,0 -415843643,Naohisa Takato,JPN,male,1993-05-30,1.6,60.0,judo,0,0,1 -30264154,Naoko Ishihara,JPN,female,1974-10-22,1.57,64.0,shooting,0,0,0 -834851644,Naomi Flood,AUS,female,1986-04-17,1.75,,canoe,0,0,0 -807036162,Naomi Folkard,GBR,female,1983-09-18,1.69,76.0,archery,0,0,0 -268877038,Naomi Ruele,BOT,female,1997-01-13,,,aquatics,0,0,0 -217475768,Naomi Sedney,NED,female,1994-12-17,1.7,65.0,athletics,0,0,0 -937919946,Naomi van As,NED,female,1983-07-26,1.79,63.0,hockey,0,1,0 -827667838,Naomichi Ueda,JPN,male,1994-10-24,1.86,77.0,football,0,0,0 -230104226,Naomy Grand Pierre,HAI,female,1997-04-16,,,aquatics,0,0,0 -486171475,Naoya Okada,JPN,male,1990-10-10,1.85,70.0,shooting,0,0,0 -298380237,Napis Tortungpanich,THA,male,1995-03-29,1.74,56.0,shooting,0,0,0 -676294284,Naranjargal Tsend-Ayush,MGL,female,1992-01-27,1.75,70.0,judo,0,0,0 -772765503,Narcis Stefan Mihaila,ROU,male,1987-08-04,1.83,73.0,athletics,0,0,0 -871545516,Narcisa Landazuri,ECU,female,1991-06-13,1.66,55.0,athletics,0,0,0 -435983396,Nareg Guregian,USA,male,1989-01-20,1.96,97.0,rowing,0,0,0 -188253954,Narek Abgaryan,ARM,male,1992-01-06,1.66,,boxing,0,0,0 -302411064,Nariman Aly,EGY,female,1998-09-29,1.69,55.0,aquatics,0,0,0 -345522636,Narsingh Pancham Yadav,IND,male,1989-08-06,1.67,74.0,wrestling,0,0,0 -113824084,Nary Ly,CAM,female,1972-06-06,1.6,55.0,athletics,0,0,0 -238115739,Naryury Alexandra Perez Reveron,VEN,female,1992-09-29,1.68,100.0,weightlifting,0,0,0 -342677534,Nasanburmaa Ochirbat,MGL,female,1989-04-14,1.66,69.0,wrestling,0,0,0 -520381146,Nasreddine Megdich,QAT,male,1991-08-29,1.79,80.0,handball,0,0,0 -142174350,Nasser Al-Attiya,QAT,male,1970-12-21,1.78,82.0,shooting,0,0,0 -295414413,Nastassia Ivanova,BLR,female,1982-11-04,1.62,50.0,athletics,0,0,0 -244608144,Nastassia Yatsevich,BLR,female,1985-01-18,1.58,48.0,athletics,0,0,0 -442261024,Natalia Alfaro,CRC,female,1987-04-08,1.65,62.0,volleyball,0,0,0 -945555989,Natalia Duco,CHI,female,1989-01-31,1.77,100.0,athletics,0,0,0 -501264763,Natalia Gaitan,COL,female,1991-04-03,1.64,56.0,football,0,0,0 -29258479,Natalia Gaudio,BRA,female,1992-12-18,1.7,55.0,gymnastics,0,0,0 -102725469,Natalia Ishchenko,RUS,female,1986-04-08,1.77,56.0,aquatics,2,0,0 -508230574,Natalia Kuziutina,RUS,female,1989-05-08,1.6,52.0,judo,0,0,1 -131987947,Natalia Lovtcova,RUS,female,1988-04-14,,,aquatics,0,0,0 -482560028,Natalia Luccas,BRA,female,1996-09-13,1.67,63.0,aquatics,0,0,0 -91982471,Natalia Madaj,POL,female,1988-01-25,1.75,68.0,rowing,1,0,0 -934592033,Natalia Maria Bernardo,ANG,female,1986-12-25,1.7,65.0,handball,0,0,0 -260798105,Natalia Pacierpnik,POL,female,1988-08-14,1.69,58.0,canoe,0,0,0 -354025336,Natalia Partyka,POL,female,1989-07-27,1.73,64.0,table tennis,0,0,0 -121155817,Natalia Pereira,BRA,female,1989-04-04,1.84,76.0,volleyball,0,0,0 -53814036,Natalia Perminova,RUS,female,1991-11-14,1.64,55.0,badminton,0,0,0 -281240211,Natalia Pohrebniak,UKR,female,1988-02-19,1.71,63.0,athletics,0,0,0 -290312104,Natalia Priscepa,MDA,female,1989-10-17,1.68,74.0,weightlifting,0,0,0 -228374480,Natalia Romero,CHI,female,1980-02-26,1.63,57.0,athletics,0,0,0 -496598838,Natalia Rutkowska,POL,female,1991-01-21,1.62,56.0,cycling,0,0,0 -930260254,Natalia Sanchez,COL,female,1983-05-20,1.66,63.0,archery,0,0,0 -335301565,Natalia Semenova,UKR,female,1982-07-07,1.78,75.0,athletics,0,0,0 -868843791,Natalia Stratulat,MDA,female,1987-07-24,1.78,83.0,athletics,0,0,0 -681645907,Natalia Valentin,PUR,female,1989-09-12,1.7,61.0,volleyball,0,0,0 -233616560,Natalia Vorobeva,RUS,female,1991-05-27,1.74,69.0,wrestling,0,1,0 -104900032,Natalie Achonwa,CAN,female,1992-11-22,1.9,86.0,basketball,0,0,0 -501589607,Natalie Burton,AUS,female,1989-03-23,1.94,76.0,basketball,0,0,0 -325204288,Natalie Hermann,GER,female,1999-08-27,1.7,54.0,gymnastics,0,0,0 -235612415,Natalie Mastracci,CAN,female,1989-06-05,1.78,75.0,rowing,0,0,0 -243599235,Natalie Mc Giffert,USA,female,1997-03-14,1.61,43.0,gymnastics,0,0,0 -783121274,Natalie Powell,GBR,female,1990-10-16,1.75,77.0,judo,0,0,0 -469016028,Natalie Rooney,NZL,female,1988-06-01,1.81,95.0,shooting,0,1,0 -36343721,Nataliia Lupu,UKR,female,1987-11-04,1.75,60.0,athletics,0,0,0 -152619271,Nataliia Moskvina,UKR,female,1988-06-09,1.67,55.0,gymnastics,0,0,0 -417902852,Nataliia Pryshchepa,UKR,female,1994-09-11,1.63,48.0,athletics,0,0,0 -184555664,Nataliya Goncharova,RUS,female,1989-06-01,1.94,75.0,volleyball,0,0,0 -716355288,Nataliya Lehonkova,UKR,female,1982-12-27,1.61,48.0,athletics,0,0,0 -76086422,Nataliya Strohova,UKR,female,1992-12-26,1.69,65.0,athletics,0,0,0 -460143342,Nataliya Trafimava,BLR,female,1979-06-16,1.84,75.0,basketball,0,0,0 -383133261,Nataliya Zolotukhina,UKR,female,1985-01-04,1.8,87.0,athletics,0,0,0 -395541735,Natallia Kalnysh,UKR,female,1974-07-02,1.67,80.0,shooting,0,0,0 -326138845,Natallia Viatkina,BLR,female,1987-02-10,1.76,58.0,athletics,0,0,0 -972532499,Nataly Arias,COL,female,1986-04-02,1.72,64.0,football,0,0,0 -546626523,Nataly Michel,MEX,female,1990-07-09,1.71,62.0,fencing,0,0,0 -200672327,Natalya Asanova,UZB,female,1989-11-29,1.77,58.0,athletics,0,0,0 -189952338,Natalya Coyle,IRL,female,1990-12-11,1.7,60.0,modern pentathlon,0,0,0 -987315393,Natalya Sergeyeva,KAZ,female,1976-05-03,1.64,64.0,canoe,0,0,0 -975725248,Natalya Sinishin,AZE,female,1985-07-03,1.6,58.0,wrestling,0,0,1 -979223655,Natan Wegrzycki-Szymczyk,POL,male,1995-01-05,2.02,92.0,rowing,0,0,0 -928811908,Natasa Douchev-Janic,HUN,female,1982-06-24,1.74,68.0,canoe,0,0,0 -143252275,Natasha Hansen,NZL,female,1989-11-15,1.67,66.0,cycling,0,0,0 -306128745,Natasha Hastings,USA,female,1986-07-23,1.73,68.0,athletics,1,0,0 -161924940,Natasha Hunt,GBR,female,1989-03-21,1.65,62.0,rugby sevens,0,0,0 -566498103,Natasha Watcham-Roy,CAN,female,1992-04-28,1.7,67.0,rugby sevens,0,0,1 -71025804,Natasha Wodak,CAN,female,1981-12-17,1.6,47.0,athletics,0,0,0 -378410191,Nate Ebner,USA,male,1988-12-14,1.86,97.0,rugby sevens,0,0,0 -306477924,Nathalie Brugger,SUI,female,1985-12-25,1.74,69.0,sailing,0,0,0 -925043937,Nathalie Hagman,SWE,female,1991-07-19,1.67,62.0,handball,0,0,0 -238114237,Nathalie Moellhausen,BRA,female,1985-12-01,1.77,63.0,fencing,0,0,0 -146755228,Nathalie Nicole Viviane Marchino Urrutia,COL,female,1981-07-27,1.7,63.0,rugby sevens,0,0,0 -776128059,Nathan Adrian,USA,male,1988-12-07,1.99,102.0,aquatics,2,0,2 -934643545,Nathan Bailey,GBR,male,1993-07-24,1.78,71.0,gymnastics,0,0,0 -236734803,Nathan Brannen,CAN,male,1982-09-08,1.74,58.0,athletics,0,0,0 -440261722,Nathan Byukusenge,RWA,male,1980-08-08,,,cycling,0,0,0 -364644327,Nathan Flannery,NZL,male,1992-10-22,1.89,85.0,rowing,0,0,0 -532748337,Nathan Hart,AUS,male,1993-03-04,1.8,90.0,cycling,0,0,0 -880935011,Nathan Katz,AUS,male,1995-01-17,1.75,66.0,judo,0,0,0 -628333432,Nathan Outteridge,AUS,male,1986-01-28,1.79,75.0,sailing,0,1,0 -874878988,Nathan Schrimsher,USA,male,1992-05-22,1.88,78.0,modern pentathlon,0,0,0 -920855247,Nathon Allen,JAM,male,1995-10-28,,,athletics,0,1,0 -600380333,Natoya Goule,JAM,female,1991-03-30,1.52,57.0,athletics,0,0,0 -379684120,Natsumi Hoshi,JPN,female,1990-08-21,1.64,56.0,aquatics,0,0,1 -354644354,Natsumi Sakai,JPN,female,2001-06-19,1.72,58.0,aquatics,0,0,0 -626206435,Natsumi Tomonaga,JPN,female,1991-08-22,1.69,53.0,modern pentathlon,0,0,0 -507079558,Natthanan Junkrajang,THA,female,1986-04-13,1.66,57.0,aquatics,0,0,0 -585986660,Natthaphong Phonoppharat,THA,male,1988-05-16,1.77,72.0,sailing,0,0,0 -897039475,Natthaya Thanaronnawat,THA,female,1979-06-12,1.58,47.0,athletics,0,0,0 -521469837,Nauraj Singh Randhawa,MAS,male,1992-01-27,1.93,70.0,athletics,0,0,0 -383442577,Navjot Kaur,IND,female,1995-03-07,1.67,53.0,hockey,0,0,0 -848372593,Nayo Raincock-Ekunwe,CAN,female,1991-08-29,1.87,75.0,basketball,0,0,0 -797214230,Nazar Kovalenko,UKR,male,1987-02-09,1.77,68.0,athletics,0,0,0 -703930419,Nazik Avdalyan,ARM,female,1986-10-31,1.57,69.0,weightlifting,0,0,0 -613585930,Nazim Babayev,AZE,male,1997-10-08,1.87,74.0,athletics,0,0,0 -167982830,Nazli Donertas,TUR,female,1991-03-01,1.73,69.0,sailing,0,0,0 -328956324,Ndifreke Udo,NGR,male,1998-08-15,1.73,,football,0,0,1 -467504641,Nebiat Habtemariam,ERI,female,1978-12-29,,,athletics,0,0,0 -932895136,Nebojsa Grujic,SRB,male,1991-03-21,1.81,83.0,canoe,0,0,0 -213347925,Ned Justeen Azemia,SEY,male,1997-08-21,1.77,65.0,athletics,0,0,0 -393863560,Neda Shahsavari,IRI,female,1986-09-21,1.67,62.0,table tennis,0,0,0 -218738861,Nehal Saafan,EGY,female,1996-09-10,1.64,49.0,aquatics,0,0,0 -824920979,Neide Marisa de P. Barbosa,ANG,female,1980-09-23,1.8,87.0,handball,0,0,0 -229071441,Neisi Patricia Dajomes Barrera,ECU,female,1998-05-12,1.67,69.0,weightlifting,0,0,0 -868834976,Nekoda Smythe-Davis,GBR,female,1993-04-22,1.57,60.0,judo,0,0,0 -19709330,Neli Alberto,ESP,female,1983-07-02,1.79,90.0,handball,0,0,0 -251197905,Nelia Martins,TLS,female,1998-07-09,1.5,,athletics,0,0,0 -741011271,Nelson Evora,POR,male,1984-04-20,1.83,76.0,athletics,0,0,0 -526118539,Nelson Filipe Santos Simoes Oliveira,POR,male,1989-03-06,1.8,67.0,cycling,0,0,0 -454620893,Nelson Kipkosgei Cherutich,BRN,male,1993-03-09,1.7,60.0,athletics,0,0,0 -618007323,Nemanja Nedovic,SRB,male,1991-06-16,1.91,90.0,basketball,0,1,0 -761172837,Nenad Bedik,SRB,male,1989-04-14,2.02,96.0,rowing,0,0,0 -706411745,Nenad Filipovic,SRB,male,1978-10-05,1.82,74.0,athletics,0,0,0 -840921480,Nenad Zimonjic,SRB,male,1976-06-04,1.92,90.0,tennis,0,0,0 -188152707,Nene Hilario,BRA,male,1982-09-13,2.11,113.0,basketball,0,0,0 -838940374,Nercely Soto,VEN,female,1990-08-23,1.69,55.0,athletics,0,0,0 -41640598,Nerea Pena,ESP,female,1989-12-13,1.75,70.0,handball,0,0,0 -967371534,Neringa Aidietyte,LTU,female,1983-06-05,1.77,60.0,athletics,0,0,0 -459454489,Nery Brenes,CRC,male,1985-09-25,1.75,70.0,athletics,0,0,0 -274922701,Nestor Abad,ESP,male,1993-03-29,1.67,64.0,gymnastics,0,0,0 -358214485,Nestor Colmenares,VEN,male,1987-09-05,2.03,110.0,basketball,0,0,0 -662467395,Nestor Colonia,PHI,male,1992-02-16,1.58,55.0,weightlifting,0,0,0 -628099211,Nestor Nielsen van Hoff,URU,male,1972-11-13,1.81,83.0,equestrian,0,0,0 -321378771,Nestor Orellana,MEX,male,1992-01-07,1.92,84.0,volleyball,0,0,0 -406378010,Neta Rivkin,ISR,female,1991-06-19,1.7,50.0,gymnastics,0,0,0 -536696599,Nethaneel Mitchell-Blake,GBR,male,1994-04-02,1.87,86.0,athletics,0,0,0 -738180656,Nevena Jovanovic,SRB,female,1990-06-30,1.79,72.0,basketball,0,0,1 -892604107,Neviana Vladinova,BUL,female,1994-02-23,1.66,47.0,gymnastics,0,0,0 -255428335,Nevriye Yilmaz,TUR,female,1980-06-16,1.92,85.0,basketball,0,0,0 -780066644,Neymar,BRA,male,1992-02-05,1.74,68.0,football,1,0,0 -243033574,Nezir Karap,TUR,male,1994-01-02,1.74,74.0,aquatics,0,0,0 -708788077,Ngoc Tu Van,VIE,female,1987-08-11,1.58,46.0,judo,0,0,0 -350682364,Nguse Amlosom,ERI,male,1986-11-10,1.8,56.0,athletics,0,0,0 -978016075,Ni Yan,CHN,female,1987-03-02,1.92,74.0,volleyball,1,0,0 -294595887,Nia Ali,USA,female,1988-10-23,1.68,64.0,athletics,0,1,0 -806128359,Niall Williams,NZL,female,1988-04-21,1.73,74.0,rugby sevens,0,1,0 -959123687,Nic Woods,NZL,male,1995-08-26,1.8,90.0,hockey,0,0,0 -487583469,Niccolo Campriani,ITA,male,1987-11-06,1.77,80.0,shooting,2,0,0 -81940931,Niccolo' Gitto,ITA,male,1986-10-12,1.9,90.0,aquatics,0,0,1 -284825190,Nichelle Prince,CAN,female,1995-02-19,1.63,65.0,football,0,0,1 -832227468,Nicholas Delpopolo,USA,male,1989-02-08,1.73,77.0,judo,0,0,0 -451660058,Nicholas Hoag,CAN,male,1992-08-19,2.0,91.0,volleyball,0,0,0 -617073971,Nicholas Kiplagat Bett,KEN,male,1990-01-27,1.9,75.0,athletics,0,0,0 -550972021,Nicholas Long,USA,male,1989-10-06,1.88,86.0,cycling,0,0,0 -48265252,Nicholas Lucena,USA,male,1979-09-22,1.86,83.0,volleyball,0,0,0 -446810551,Nicholas Magana,PER,male,1996-06-18,1.98,90.0,aquatics,0,0,0 -186990387,Nicholas Presciutti,ITA,male,1993-12-14,1.89,93.0,aquatics,0,0,1 -411471821,Nicholas Quinn,IRL,male,1993-06-03,1.85,78.0,aquatics,0,0,0 -475985515,Nicholas Scarvelis,GRE,male,1993-02-02,1.86,125.0,athletics,0,0,0 -373381796,Nicholas Willis,NZL,male,1983-04-25,1.83,68.0,athletics,0,0,1 -624024995,Nick Dempsey,GBR,male,1980-08-13,1.8,71.0,sailing,0,1,0 -779437440,Nick Haig,NZL,male,1987-03-12,1.8,82.0,hockey,0,0,0 -572455894,Nick Malouf,AUS,male,1993-03-19,1.86,92.0,rugby sevens,0,0,0 -253306509,Nick Miller,GBR,male,1993-05-01,1.88,112.0,athletics,0,0,0 -764396400,Nick Skelton,GBR,male,1957-12-30,1.75,76.0,equestrian,1,0,0 -319742600,Nick Thompson,GBR,male,1986-05-05,1.79,80.0,sailing,0,0,0 -743923874,Nick Wilson,NZL,male,1990-08-06,1.8,81.0,hockey,0,0,0 -683971980,Nickel Ashmeade,JAM,male,1990-04-07,1.85,88.0,athletics,1,0,0 -317250144,Nickel Chand,FIJ,male,1995-07-28,1.67,60.0,football,0,0,0 -982320885,Nickiesha Wilson,JAM,female,1986-07-28,1.74,66.0,athletics,0,0,0 -129717034,Nickolas Catlin,GBR,male,1989-04-08,1.75,76.0,hockey,0,0,0 -891765771,Nicky Samuels,NZL,female,1983-02-28,1.7,56.0,triathlon,0,0,0 -427041039,Nicky van Leuveren,NED,female,1990-05-20,1.75,58.0,athletics,0,0,0 -199818017,Nico Delle-Karth,AUT,male,1984-01-21,1.8,74.0,sailing,0,0,0 -134647499,Nico Miguel Hernandez,USA,male,1996-01-04,1.66,,boxing,0,0,1 -28175452,Nico Mueller,GER,male,1993-11-02,1.68,77.0,weightlifting,0,0,0 -988453095,Nico Stahlberg,SUI,male,1991-11-15,1.92,95.0,rowing,0,0,0 -570863288,Nicol Ruprecht,AUT,female,1992-10-02,1.7,55.0,gymnastics,0,0,0 -410663669,Nicola Adams,GBR,female,1982-10-26,1.64,,boxing,1,0,0 -410776755,Nicola Groves,GBR,female,1989-04-04,1.68,63.0,sailing,0,0,0 -128217086,Nicola Muscat,MLT,female,1994-06-25,1.73,59.0,aquatics,0,0,0 -486899362,Nicola Philippaerts,BEL,male,1993-07-30,1.86,76.0,equestrian,0,0,0 -126244906,Nicola Ripamonti,ITA,male,1990-01-11,1.78,81.0,canoe,0,0,0 -332129646,Nicola Spirig Hug,SUI,female,1982-02-07,1.66,54.0,triathlon,0,1,0 -334459836,Nicola White,GBR,female,1988-01-20,1.72,65.0,hockey,1,0,0 -947910094,Nicola Zagame,AUS,female,1990-08-11,1.74,72.0,aquatics,0,0,0 -168904848,Nicolae-Alexandru Soare,ROU,male,1991-09-20,1.68,59.0,athletics,0,0,0 -302102692,Nicolai Brock-Madsen,DEN,male,1993-01-09,1.94,88.0,football,0,0,0 -730590153,Nicolai Ceban,MDA,male,1986-03-30,1.86,96.0,wrestling,0,0,0 -652169057,Nicolai Poulsen,DEN,male,1993-08-15,1.77,65.0,football,0,0,0 -860019279,Nicolas Batum,FRA,male,1988-12-14,2.03,105.0,basketball,0,0,0 -74210449,Nicolas Bruno,ARG,male,1989-02-24,1.87,85.0,volleyball,0,0,0 -388271463,Nicolas Brussino,ARG,male,1993-03-02,2.0,84.0,basketball,0,0,0 -582760864,Nicolas Bruzzone,ARG,male,1985-10-24,1.68,76.0,rugby sevens,0,0,0 -708871411,Nicolas Colsaerts,BEL,male,1982-11-14,1.88,75.0,golf,0,0,0 -768215755,Nicolas Cordoba,ARG,male,1989-11-20,1.65,71.0,gymnastics,0,0,0 -162021826,Nicolas Cuestas,URU,male,1986-12-08,1.8,63.0,athletics,0,0,0 -153786575,Nicolas D'Oriano,FRA,male,1997-05-05,1.75,68.0,aquatics,0,0,0 -997930388,Nicolas Ferreira,BRA,male,1992-09-25,1.85,84.0,fencing,0,0,0 -715985574,Nicolas Jacobi,GER,male,1987-04-13,1.93,95.0,hockey,0,0,1 -164743566,Nicolas Laprovittola,ARG,male,1990-01-31,1.93,88.0,basketball,0,0,0 -727420830,Nicolas Lionel Wettstein,ECU,male,1981-03-30,1.71,71.0,equestrian,0,0,0 -433876140,Nicolas Mahut,FRA,male,1982-01-21,1.9,82.0,tennis,0,0,0 -148338040,Nicolas Marechal,FRA,male,1987-03-04,1.98,93.0,volleyball,0,0,0 -586116656,Nicolas Martin Tripichio,ARG,male,1996-01-05,1.71,71.0,football,0,0,0 -27749600,Nicolas Nilo,BRA,male,1987-08-04,1.95,92.0,aquatics,0,0,0 -213136925,Nicolas Pratt,CAN,male,1985-07-10,1.83,72.0,rowing,0,0,0 -238155568,Nicolas Roche,IRL,male,1984-07-03,1.78,70.0,cycling,0,0,0 -797725236,Nicolas Touzaint,FRA,male,1980-05-10,1.75,62.0,equestrian,0,0,0 -228429250,Nicolas le Goff,FRA,male,1992-02-15,2.06,115.0,volleyball,0,0,0 -328461296,Nicole Acevedo,COL,female,1994-10-15,1.66,70.0,rugby sevens,0,0,0 -385843788,Nicole Ahsinger,USA,female,1998-05-12,1.63,58.0,gymnastics,0,0,0 -951251683,Nicole Beck,AUS,female,1988-05-28,1.68,66.0,rugby sevens,1,0,0 -773243020,Nicole Beukers,NED,female,1990-10-07,1.7,66.0,rowing,0,1,0 -87221734,Nicole Broch Larsen,DEN,female,1993-05-14,1.79,75.0,golf,0,0,0 -933579518,Nicole Buchler,SUI,female,1983-12-17,1.62,54.0,athletics,0,0,0 -982976366,Nicole Hare,CAN,female,1994-07-26,1.77,76.0,rowing,0,0,0 -25839351,Nicole Laird,AUS,female,1993-02-18,1.91,75.0,volleyball,0,0,0 -166434763,Nicole Regnier,COL,female,1995-02-28,1.7,62.0,football,0,0,0 -297449748,Nicole Sifuentes,CAN,female,1986-06-30,1.77,55.0,athletics,0,0,0 -543974028,Nicole van der Velden,ARU,female,1994-10-26,1.68,62.0,sailing,0,0,0 -853691804,Nida Ustundag,TUR,female,1996-10-21,1.79,69.0,aquatics,0,0,0 -681034829,Niek Kimmann,NED,male,1996-05-20,1.89,90.0,cycling,0,0,0 -768436561,Nien-Chin Chen,TPE,female,1997-05-10,1.69,,boxing,0,0,0 -450335121,Nigel Levine,GBR,male,1989-04-30,1.76,72.0,athletics,0,0,0 -970010589,Nigel Paul,TTO,male,1989-06-27,1.94,,boxing,0,0,0 -481034512,Nigina Sharipova,UZB,female,1995-08-10,,,athletics,0,0,0 -318020679,Nigora Tursunkulova,UZB,female,1999-04-04,1.81,67.0,taekwondo,0,0,0 -4669727,Nihel Cheikh Rouhou,TUN,female,1987-01-05,1.64,78.0,judo,0,0,0 -702207367,Nijat Rahimov,KAZ,male,1993-08-13,1.76,76.0,weightlifting,1,0,0 -17860991,Nijat Shikhalizada,AZE,male,1988-10-12,1.64,66.0,judo,0,0,0 -872456959,Nijel Amos,BOT,male,1994-03-15,1.79,60.0,athletics,0,0,0 -964306539,Nik Henigman,SLO,male,1995-12-04,1.99,95.0,handball,0,0,0 -88815979,Nike Lorenz,GER,female,1997-03-12,1.7,69.0,hockey,0,0,1 -528950819,Nikita Glasnovic,SWE,female,1995-01-17,1.75,57.0,taekwondo,0,0,0 -318898453,Nikita Liamin,RUS,male,1985-10-14,2.04,90.0,volleyball,0,0,0 -805742930,Nikita Lobintsev,RUS,male,1988-11-21,,,aquatics,0,0,0 -358430642,Nikita Morgachev,RUS,male,1981-05-03,1.96,98.0,rowing,0,0,0 -405340018,Nikita Nagornyy,RUS,male,1997-02-12,1.66,67.0,gymnastics,0,1,0 -407904416,Nikita Pablo,AUS,female,1995-01-08,1.68,63.0,aquatics,0,0,0 -564650240,Nikita Rafalovich,UZB,male,1993-10-10,1.9,78.0,taekwondo,0,0,0 -491633637,Nikita Shleikher,RUS,male,1998-06-10,1.68,66.0,aquatics,0,0,0 -250399527,Nikita Shurshin,RUS,male,1993-04-08,1.92,96.0,cycling,0,0,0 -681571834,Nikki Hamblin,NZL,female,1988-05-20,1.65,52.0,athletics,0,0,0 -298646249,Nikki Harris,GBR,female,1986-12-30,1.76,60.0,cycling,0,0,0 -532497224,Nikki Pradhan,IND,female,1993-12-08,1.53,45.0,hockey,0,0,0 -861176361,Nikkita Holder,CAN,female,1987-05-07,1.7,59.0,athletics,0,0,0 -548573991,Niklas Landin Jacobsen,DEN,male,1988-12-19,2.01,102.0,handball,1,0,0 -606084836,Niklas Larsen,DEN,male,1997-03-22,1.8,74.0,cycling,0,0,1 -898426175,Niklas Laustsen,DEN,male,1992-08-30,1.89,95.0,cycling,0,0,0 -852806194,Niklas Lindgren,FIN,male,1988-05-18,1.77,71.0,sailing,0,0,0 -497912437,Niklas Suele,GER,male,1995-09-03,1.94,100.0,football,0,1,0 -256324348,Niklas Wellen,GER,male,1994-12-14,1.85,77.0,hockey,0,0,1 -539021692,Nikol Merizaj,ALB,female,1998-08-07,1.8,65.0,aquatics,0,0,0 -440246912,Nikola Girke,CAN,female,1977-12-30,1.78,65.0,sailing,0,0,0 -680564901,Nikola Jaksic,SRB,male,1997-01-17,1.97,89.0,aquatics,1,0,0 -799131659,Nikola Jokic,SRB,male,1995-02-19,2.09,115.0,basketball,0,1,0 -195768771,Nikola Kalinic,SRB,male,1991-11-08,2.02,102.0,basketball,0,1,0 -483433829,Nikola Karabatic,FRA,male,1984-04-11,1.94,104.0,handball,0,1,0 -202450860,Nikola Mazurova,CZE,female,1994-11-22,1.6,68.0,shooting,0,0,0 -854142844,Nikola Mirotic,ESP,male,1991-02-11,2.08,100.0,basketball,0,0,1 -759289661,Nikolai Kuksenkov,RUS,male,1989-06-02,1.72,65.0,gymnastics,0,1,0 -111209519,Nikolai Novosjolov,EST,male,1980-06-09,1.91,93.0,fencing,0,0,0 -80208991,Nikolas Sylvester,VIN,male,2000-01-20,,,aquatics,0,0,0 -913573838,Nikolaus Resch,AUT,male,1984-08-30,1.88,84.0,sailing,0,0,0 -434491444,Nikolay Kovalev,RUS,male,1986-10-28,1.77,74.0,fencing,0,0,0 -705394191,Nikolay Nikolaev Bayryakov,BUL,male,1989-09-05,1.8,89.0,wrestling,0,0,0 -932397060,Nikoleta Kyriakopoulou,GRE,female,1986-03-21,1.67,57.0,athletics,0,0,0 -984974702,Nikolina Moldovan,SRB,female,1990-05-01,1.67,67.0,canoe,0,0,0 -553206872,Nikoloz Basilashvili,GEO,male,1992-02-23,1.85,80.0,tennis,0,0,0 -932198619,Nile Wilson,GBR,male,1996-01-17,1.66,60.0,gymnastics,0,0,1 -820507010,Nilla Fischer,SWE,female,1984-08-02,1.77,74.0,football,0,1,0 -426844727,Nils Brembach,GER,male,1993-02-23,1.84,70.0,athletics,0,0,0 -677508603,Nils Jakob Hoff,NOR,male,1985-02-05,,,rowing,0,0,0 -599711815,Nils Petersen,GER,male,1988-12-06,1.88,80.0,football,0,1,0 -471728310,Nils Schomber,GER,male,1994-03-15,1.83,72.0,cycling,0,0,0 -625149958,Nils van 't Hoenderdaal,NED,male,1993-10-03,1.78,86.0,cycling,0,0,0 -424937241,Nilson Moreira da Silva,BRA,male,1976-12-24,1.78,74.0,equestrian,0,0,0 -828507480,Niluka Karunaratne,SRI,male,1985-02-13,1.76,73.0,badminton,0,0,0 -690491812,Niluka Rajasekara,SRI,female,1982-03-17,1.55,42.0,athletics,0,0,0 -143552621,Nima Alamian,IRI,male,1992-12-24,1.74,70.0,table tennis,0,0,0 -624774853,Nina Amir,ISR,female,1999-01-17,1.7,62.0,sailing,0,0,0 -641314378,Nina Balaban,MKD,female,1995-11-02,1.58,58.0,shooting,0,0,0 -149794967,Nina Christen,SUI,female,1994-02-07,1.6,57.0,shooting,0,0,0 -61298807,Nina Derwael,BEL,female,2000-03-26,1.65,46.0,gymnastics,0,0,0 -116856979,Nina Hemmer,GER,female,1993-02-16,1.65,55.0,wrestling,0,0,0 -47803555,Nina Hollensen,DEN,female,1992-04-24,1.83,72.0,rowing,0,0,0 -322856280,Nina Rangelova,BUL,female,1990-10-22,1.7,56.0,aquatics,0,0,0 -844998028,Ning Ding,CHN,female,1990-06-20,1.71,63.0,table tennis,2,0,0 -137640462,Ning Gao,SIN,male,1982-10-11,1.79,80.0,table tennis,0,0,0 -802449703,Ning Wei,CHN,female,1982-08-05,1.67,60.0,shooting,0,0,0 -703857036,Nino Bertasio,ITA,male,1988-07-30,1.83,88.0,golf,0,0,0 -680794045,Nino Salukvadze,GEO,female,1969-02-01,1.68,58.0,shooting,0,0,0 -464025430,Nino Schurter,SUI,male,1986-05-13,1.73,68.0,cycling,1,0,0 -95604146,Nirmla,IND,female,1995-07-15,,,athletics,0,0,0 -800567218,Nirra Fields,CAN,female,1993-12-03,1.7,57.0,basketball,0,0,0 -769356228,Nisha Rawal,NEP,female,1995-09-11,1.68,73.0,taekwondo,0,0,0 -803251094,Nitendra Singh,IND,male,1986-09-29,1.72,58.0,athletics,0,0,0 -340570863,Nitya Krishinda Maheswari,INA,female,1988-12-16,1.68,65.0,badminton,0,0,0 -980419661,Nivaldo Nadhir Diaz Gomez,CUB,male,1994-03-24,2.0,81.0,volleyball,0,0,0 -399376832,Njisane Phillip,TTO,male,1991-05-29,,,cycling,0,0,0 -169340447,Nkosingiphile Gumede,RSA,male,1993-12-01,1.92,95.0,football,0,0,0 -228913165,Noah Al-Khulaifi,QAT,male,1999-05-10,1.9,86.0,aquatics,0,0,0 -649129831,Noah Mascoll-Gomes,ANT,male,1999-05-27,1.75,68.0,aquatics,0,0,0 -779523971,Noah Sonko Sundberg,SWE,male,1996-06-06,1.86,77.0,football,0,0,0 -585460949,Nobuhle Majika,ZIM,female,1991-05-09,1.59,53.0,football,0,0,0 -674436984,Nobukhosi Palma Ncube,ZIM,female,1993-02-17,1.59,54.0,football,0,0,0 -678262840,Nobuya Kato,JPN,male,1995-04-16,1.85,70.0,athletics,0,0,0 -26922064,Noe Delpech,FRA,male,1986-02-22,1.81,82.0,sailing,0,0,0 -932371435,Noel van T End,NED,male,1991-06-15,1.85,93.0,judo,0,0,0 -485719792,Noelie Yarigo,BEN,female,1985-12-26,1.68,56.0,athletics,0,0,0 -774261783,Noelle Montcalm,CAN,female,1988-04-03,1.68,53.0,athletics,0,0,0 -482317811,Noemi Batki,ITA,female,1987-10-12,1.67,62.0,aquatics,0,0,0 -815901454,Noemi Girardet,SUI,female,1994-12-12,1.74,61.0,aquatics,0,0,0 -656797479,Noemie Kober,FRA,female,1993-12-15,1.8,73.0,rowing,0,0,0 -698687305,Noemie Thomas,CAN,female,1996-02-04,1.63,59.0,aquatics,0,0,0 -486513538,Noko Matlou,RSA,female,1985-09-30,1.66,61.0,football,0,0,0 -124693732,Nomathemba Ntsibande,RSA,female,1986-04-19,1.69,59.0,football,0,0,0 -665606530,Non Stanford,GBR,female,1989-01-08,1.7,55.0,triathlon,0,0,0 -46541403,Noora Ruskola,FIN,female,1994-12-21,1.54,53.0,sailing,0,0,0 -45473346,Noora Tamminen,FIN,female,1990-10-30,1.68,59.0,golf,0,0,0 -547769017,Nooralotta Neziri,FIN,female,1992-11-09,1.74,63.0,athletics,0,0,0 -645011476,Nooran Ahmed Ali Ba Matraf,YEM,female,1999-11-25,1.66,60.0,aquatics,0,0,0 -385609976,Nora Gjakova,KOS,female,1992-08-17,1.65,57.0,judo,0,0,0 -483970756,Nora Mork,NOR,female,1991-04-05,1.69,,handball,0,0,1 -989374073,Nora Subschinski,GER,female,1988-06-05,1.59,57.0,aquatics,0,0,0 -350577535,Norbert Hosnyanszky,HUN,male,1984-03-04,1.96,101.0,aquatics,0,0,0 -126121033,Norbert Szabian,HUN,male,1982-09-24,1.82,97.0,shooting,0,0,0 -813300024,Norbert Trandafir,ROU,male,1988-02-08,1.9,85.0,aquatics,0,0,0 -993318561,Noriko Taniguchi,JPN,female,1992-09-07,1.66,66.0,rugby sevens,0,0,0 -718571072,Norisbeth Agudo,VEN,female,1992-05-22,1.63,55.0,volleyball,0,0,0 -253052328,Noshad Alamiyan,IRI,male,1991-11-21,1.7,67.0,table tennis,0,0,0 -129615,Nothando Vilakazi,RSA,female,1988-10-28,1.6,53.0,football,0,0,0 -742257212,Nouchka Fontijn,NED,female,1987-11-09,1.8,,boxing,0,1,0 -609989806,Nour Elayoubi,EGY,female,1997-01-16,1.67,58.0,aquatics,0,0,0 -753747298,Nour Elhouda Ettaieb,TUN,female,1996-10-15,1.7,57.0,rowing,0,0,0 -947989381,Noura Mana,MAR,female,1997-12-12,1.7,62.0,aquatics,0,0,0 -792186214,Noura Mohamed,EGY,female,1998-03-05,1.73,60.0,fencing,0,0,0 -913461449,Novak Djokovic,SRB,male,1987-05-22,1.88,88.0,tennis,0,0,0 -123381244,Novlene Williams-Mills,JAM,female,1982-04-26,1.68,57.0,athletics,0,1,0 -795015528,Nozomi Okuhara,JPN,female,1995-03-13,1.56,51.0,badminton,0,0,1 -860526701,Nozomi Sato,JPN,female,1986-07-03,1.73,61.0,fencing,0,0,0 -158456610,Nubia Soares,BRA,female,1996-03-26,1.76,52.0,athletics,0,0,0 -599504171,Nuno Saraiva,POR,male,1994-03-16,1.76,76.0,judo,0,0,0 -552664061,Nur Dhabitah Sabri,MAS,female,1999-07-12,1.51,53.0,aquatics,0,0,0 -135131175,Nur Shazrin Mohamad Latif,MAS,female,1998-02-02,1.67,63.0,sailing,0,0,0 -799693970,Nur Tatar,TUR,female,1992-08-16,1.8,67.0,taekwondo,0,0,1 -231587999,Nuria Diosdado,MEX,female,1990-08-22,1.7,55.0,aquatics,0,0,0 -1716955,Nurislam Sanayev,KAZ,male,1991-02-09,1.63,57.0,wrestling,0,0,0 -147846334,Nurmakhan Tinaliyev,KAZ,male,1988-01-10,1.98,130.0,wrestling,0,0,0 -997050552,Nwanneka Okwelogu,NGR,female,1995-05-05,1.73,100.0,athletics,0,0,0 -395961774,Nyakisi Adero,UGA,female,1986-07-02,,,athletics,0,0,0 -699476083,Nycke Groot,NED,female,1988-05-04,1.75,70.0,handball,0,0,0 -959020954,Nzingha Prescod,USA,female,1992-08-14,1.63,62.0,fencing,0,0,0 -998833102,O'dayne Richards,JAM,male,1988-12-14,1.77,120.0,athletics,0,0,0 -838284014,Oana Manea,ROU,female,1985-04-18,1.77,78.0,handball,0,0,0 -703472773,Obada Alkasbeh,JOR,male,1994-07-30,1.66,,boxing,0,0,0 -381088762,Odbayar Ganbaatar,MGL,male,1989-08-20,1.6,,judo,0,0,0 -2156402,Odd Arne Brekne,NOR,male,1984-09-01,,,shooting,0,0,0 -894451060,Odette Giuffrida,ITA,female,1994-10-12,1.6,52.0,judo,0,1,0 -603038504,Odunayo Folasade Adekuoroye,NGR,female,1993-12-10,1.69,53.0,wrestling,0,0,0 -115751224,Odyssefs Meladinis,GRE,male,1990-04-05,1.94,90.0,aquatics,0,0,0 -202190502,Offiong Edem,NGR,female,1986-12-31,1.5,70.0,table tennis,0,0,0 -598707040,Oghenekaro Etebo,NGR,male,1995-11-09,1.72,,football,0,0,1 -129929116,Ogho-Oghene Egwero,NGR,male,1988-11-26,1.52,72.0,athletics,0,0,0 -896056444,Ojie Edoburun,GBR,male,1996-06-02,1.85,80.0,athletics,0,0,0 -520738573,Okcheol Kim,KOR,male,1994-11-16,1.79,70.0,cycling,0,0,0 -800670857,Okechukwu Azubuike,NGR,male,1997-04-19,1.7,,football,0,0,1 -332705415,Oksana Chusovitina,UZB,female,1975-06-19,1.53,43.0,gymnastics,0,0,0 -962326331,Oksana Herhel,UKR,female,1994-06-20,1.64,58.0,wrestling,0,0,0 -466004791,Oksana Okuneva,UKR,female,1990-03-14,1.75,63.0,athletics,0,0,0 -845220778,Oksana Shkurat,UKR,female,1993-07-30,1.7,53.0,athletics,0,0,0 -629436932,Oktawia Nowacka,POL,female,1991-01-02,1.8,60.0,modern pentathlon,0,0,1 -602321842,Olaf Tufte,NOR,male,1976-04-27,,,rowing,0,0,1 -137938891,Olaseni Lawal,NGR,male,1986-10-08,2.08,102.0,basketball,0,0,0 -48085407,Olaya Perez Pazo,VEN,female,1983-06-07,1.8,66.0,volleyball,0,0,0 -224381692,Olcay Cakir,TUR,female,1993-07-13,1.82,60.0,basketball,0,0,0 -925700475,Ole Hesselbjerg,DEN,male,1990-04-23,1.85,67.0,athletics,0,0,0 -76211755,Ole Kristian Bryhn,NOR,male,1989-05-01,,,shooting,0,0,0 -489944165,Oleg Antonov,ITA,male,1988-07-28,1.98,88.0,volleyball,0,1,0 -265324138,Oleg Stepko,AZE,male,1994-03-25,1.63,55.0,gymnastics,0,0,0 -731979766,Oleg Tarnovschi,MDA,male,1992-04-10,1.81,80.0,canoe,0,0,0 -57591720,Oleg Verniaiev,UKR,male,1993-09-29,1.6,56.0,gymnastics,1,1,0 -400214523,Oleg Zhestkov,RUS,male,1987-01-20,1.88,98.0,canoe,0,0,0 -727773897,Oleh Omelchuk,UKR,male,1983-06-07,1.7,68.0,shooting,0,0,0 -768314513,Oleh Tsarkov,UKR,male,1988-03-22,1.74,70.0,shooting,0,0,0 -160961676,Oleksandr Chernetskyy,UKR,male,1984-02-17,1.95,130.0,wrestling,0,0,0 -926202159,Oleksandr Gorshkovozov,UKR,male,1991-07-18,1.73,66.0,aquatics,0,0,0 -497898586,Oleksandr Pielieshenko,UKR,male,1994-01-07,1.7,85.0,weightlifting,0,0,0 -801537785,Oleksandr Sitkovskyy,UKR,male,1978-06-09,1.84,68.0,athletics,0,0,0 -917048345,Oleksandr Tugaryev,UKR,male,1995-07-15,1.67,63.0,sailing,0,0,0 -822288525,Oleksandra Gridasova,UKR,female,1995-07-05,1.73,53.0,gymnastics,0,0,0 -651167532,Oleksandra Sabada,UKR,female,1991-01-06,1.68,55.0,aquatics,0,0,0 -79936055,Oleksiy Kasyanov,UKR,male,1985-08-26,1.91,88.0,athletics,0,0,0 -872028854,Oleksiy Semenov,UKR,male,1982-06-27,1.98,120.0,athletics,0,0,0 -338267081,Olena Buryak,UKR,female,1988-02-08,1.96,90.0,rowing,0,0,0 -571691076,Olena Dmytrash,UKR,female,1991-12-01,1.73,51.0,gymnastics,0,0,0 -112473834,Olena Fedorova,UKR,female,1986-11-14,1.64,57.0,aquatics,0,0,0 -282462242,Olena Grechykhina,UKR,female,1991-07-11,1.78,62.0,aquatics,0,0,0 -905135133,Olena Kolesnychenko,UKR,female,1993-06-03,1.72,57.0,athletics,0,0,0 -55258833,Olena Kostevych,UKR,female,1985-04-14,1.6,52.0,shooting,0,0,0 -759603563,Olena Kravatska,UKR,female,1992-06-22,1.76,66.0,fencing,0,1,0 -584752118,Olena Kryvytska,UKR,female,1987-02-23,1.74,62.0,fencing,0,0,0 -527015303,Olena Pavlukhina,AZE,female,1989-03-01,1.78,68.0,cycling,0,0,0 -982514520,Olena Voronina,UKR,female,1990-05-05,1.65,65.0,fencing,0,1,0 -972107687,Olena Yanovska,UKR,female,1990-02-15,1.71,62.0,athletics,0,0,0 -853982899,Olesya Povkh,UKR,female,1987-10-18,1.67,63.0,athletics,0,0,0 -3330883,Olexandr Nadtoka,UKR,male,1991-03-06,1.95,95.0,rowing,0,0,0 -651203892,Olfa Charni,TUN,female,1980-05-24,1.76,65.0,shooting,0,0,0 -514784334,Olga Akopian,RUS,female,1985-03-04,1.76,63.0,handball,1,0,0 -834977661,Olga Golodna,UKR,female,1991-11-14,1.83,96.0,athletics,0,0,0 -148544473,Olga Gorbunova,RUS,female,1993-08-27,1.69,60.0,aquatics,0,0,1 -839908037,Olga Ismayilova,AZE,female,1985-09-16,1.7,71.0,cycling,0,0,0 -700198310,Olga Kharlan,UKR,female,1990-09-04,1.72,68.0,fencing,0,1,1 -34701189,Olga Kochneva,RUS,female,1988-06-29,1.68,58.0,fencing,0,0,1 -959644793,Olga Leleiko,UKR,female,1977-07-21,1.78,68.0,fencing,0,0,0 -101246911,Olga Rypakova,KAZ,female,1984-11-30,1.83,62.0,athletics,0,0,1 -58276308,Olga Safronova,KAZ,female,1991-11-05,1.71,62.0,athletics,0,0,0 -847854167,Olga Saladukha,UKR,female,1983-06-04,1.76,57.0,athletics,0,0,0 -619903029,Olga Savchuk,UKR,female,1987-09-20,1.77,67.0,tennis,0,0,0 -874685403,Olga Senyuk,AZE,female,1991-01-23,1.75,53.0,archery,0,0,0 -257199398,Olga Umaralieva,UZB,female,1988-02-05,1.61,58.0,canoe,0,0,0 -341366636,Olga Zabelinskaya,RUS,female,1980-05-10,,,cycling,0,1,0 -62936804,Olha Bibik,UKR,female,1990-02-05,1.73,63.0,athletics,0,0,0 -876726793,Olha Kotovska,UKR,female,1983-12-05,1.67,50.0,athletics,0,0,0 -192536405,Olha Lyakhova,UKR,female,1992-03-18,1.74,60.0,athletics,0,0,0 -15281750,Olha Zemlyak,UKR,female,1990-01-16,1.65,57.0,athletics,0,0,0 -520170826,Olha Zolotarova,UKR,female,1994-12-27,1.78,61.0,aquatics,0,0,0 -479817783,Olim Kurbanov,TJK,male,1998-06-21,1.86,69.0,aquatics,0,0,0 -463296329,Oliver Dingley,IRL,male,1992-11-24,1.63,60.0,aquatics,0,0,0 -82337,Oliver Geis,GER,male,1991-06-20,1.76,86.0,shooting,0,0,0 -671132115,Oliver Hegi,SUI,male,1993-02-20,1.69,63.0,gymnastics,0,0,0 -212228401,Oliver Korn,GER,male,1984-06-10,1.81,76.0,hockey,0,0,0 -922393211,Oliver Lindsay-Hague,GBR,male,1990-10-08,1.8,83.0,rugby sevens,0,1,0 -65722266,Oliver Marach,AUT,male,1980-07-16,1.84,84.0,tennis,0,0,0 -626280477,Oliver Szymanski,GER,male,1990-07-27,1.83,73.0,sailing,0,0,0 -981211905,Olivera Jevtic,SRB,female,1977-07-24,1.74,51.0,athletics,0,0,0 -903316415,Olivera Moldovan,SRB,female,1989-03-01,1.58,62.0,canoe,0,0,0 -621595419,Olivia Borlee,BEL,female,1986-04-10,1.72,57.0,athletics,0,0,0 -631669580,Olivia Carnegie-Brown,GBR,female,1991-03-28,1.81,73.0,rowing,0,1,0 -929295902,Olivia Ekpone,NGR,female,1993-01-05,,59.0,athletics,0,0,0 -966757117,Olivia Epoupa,FRA,female,1994-04-30,1.64,53.0,basketball,0,0,0 -451474447,Olivia Federici,GBR,female,1990-02-13,1.67,54.0,aquatics,0,0,0 -285785090,Olivia Hofmann,AUT,female,1992-08-08,1.63,58.0,shooting,0,0,0 -724524822,Olivia Merry,NZL,female,1992-03-16,1.82,74.0,hockey,0,0,0 -77219667,Olivia Podmore,NZL,female,1997-05-24,1.74,64.0,cycling,0,0,0 -990163818,Olivia Schough,SWE,female,1991-03-11,1.72,60.0,football,0,1,0 -654242453,Olivia Smoliga,USA,female,1994-10-12,1.88,74.0,aquatics,1,0,0 -157435063,Olivia van Rooijen,NED,female,1988-10-29,1.82,71.0,rowing,0,0,0 -788666996,Olivier Beer,SUI,male,1990-10-18,1.8,69.0,cycling,0,0,0 -758344424,Olivier Irabaruta,BDI,male,1997-08-25,1.7,62.0,athletics,0,0,0 -207164016,Olivier Nyokas,FRA,male,1986-06-28,1.89,86.0,handball,0,1,0 -355963198,Olivier Siegelaar,NED,male,1986-10-24,1.97,100.0,rowing,0,0,1 -560709278,Olofunke Oshonaike,NGR,female,1975-10-28,1.67,59.0,table tennis,0,0,0 -627306424,Olu Olamigoke,NGR,male,1990-09-19,1.75,72.0,athletics,0,0,0 -299832039,Oluwafemi Ajayi,NGR,male,1996-01-29,1.72,,football,0,0,1 -255777326,Oluwakemi Adekoya,BRN,female,1993-01-16,1.66,63.0,athletics,0,0,0 -516591047,Oluwasegun Makinde,CAN,male,1991-07-06,1.79,81.0,athletics,0,0,0 -172510289,Oluwatobiloba Amusan,NGR,female,1997-04-23,1.38,55.0,athletics,0,0,0 -177654835,Olympia Aldersey,AUS,female,1992-07-26,1.83,75.0,rowing,0,0,0 -168982138,Olzhas Sattibayev,KAZ,male,1988-05-02,1.6,,boxing,0,0,0 -103517404,Omar Andres Pinzon Garcia,COL,male,1989-06-17,1.84,82.0,aquatics,0,0,0 -876447074,Omar Assar,EGY,male,1991-07-22,1.96,93.0,table tennis,0,0,0 -307391721,Omar Elgeziry,EGY,male,1985-01-20,1.85,77.0,modern pentathlon,0,0,0 -424746712,Omar Hajjami,MAR,male,1990-07-31,1.6,58.0,taekwondo,0,0,0 -791818154,Omar Hassan,EGY,male,1991-04-04,1.91,104.0,volleyball,0,0,0 -267497141,Omar Longart,VEN,male,1991-05-18,1.72,69.0,athletics,0,0,0 -671572172,Omar McLeod,JAM,male,1994-04-25,,,athletics,1,0,0 -494429472,Omar Zepeda,MEX,male,1977-06-08,1.75,70.0,athletics,0,0,0 -370119484,Omer Karaevli,TUR,male,1977-09-11,1.76,69.0,equestrian,0,0,0 -236501534,Omid Haji Noroozi,IRI,male,1986-02-18,1.75,70.0,wrestling,0,0,0 -565665727,Omolara Omotoso,NGR,female,1993-05-25,1.52,50.0,athletics,0,0,0 -163885592,Ona Carbonell,ESP,female,1990-06-05,1.73,52.0,aquatics,0,0,0 -339420011,Ona Kim,KOR,female,1988-09-06,1.69,62.0,handball,0,0,0 -980936428,Onder Sipal,TUR,male,1987-05-01,1.75,,boxing,0,0,0 -880358779,Ondrej Cink,CZE,male,1990-12-07,1.78,68.0,cycling,0,0,0 -141546523,Ondrej Kruzel,SVK,male,1988-08-23,1.9,119.0,weightlifting,0,0,0 -729983888,Ondrej Synek,CZE,male,1982-10-13,1.99,105.0,rowing,0,0,1 -88265028,Ondrej Vetesnik,CZE,male,1984-03-05,1.81,72.0,rowing,0,0,0 -47810803,Onkabetse Nkobolo,BOT,male,1993-07-22,,,athletics,0,0,0 -17450856,Ons Jabeur,TUN,female,1994-08-28,1.67,66.0,tennis,0,0,0 -31333880,Onur Balkan,TUR,male,1996-03-10,1.76,70.0,cycling,0,0,0 -676134889,Onur Biriz,TUR,male,1998-10-16,1.86,73.0,sailing,0,0,0 -338111921,Onur Sipal,TUR,male,1989-03-17,1.72,,boxing,0,0,0 -756542327,Or Sasson,ISR,male,1990-08-18,1.93,120.0,judo,0,0,1 -500712109,Oreane Lechenault,FRA,female,2000-08-31,1.34,37.0,gymnastics,0,0,0 -10035386,Oreoluwa Cherebin,GRN,female,1997-12-24,,,aquatics,0,0,0 -890917446,Orianica Velasquez,COL,female,1989-08-01,1.77,69.0,football,0,0,0 -391151189,Oribe Peralta,MEX,male,1984-01-12,1.78,76.0,football,0,0,0 -231740872,Orkhan Safarov,AZE,male,1991-08-10,1.71,60.0,judo,0,0,0 -34240662,Orkhon Purevdorj,MGL,female,1993-12-25,1.63,63.0,wrestling,0,0,0 -375338487,Orlando Ortega,ESP,male,1991-07-29,1.82,77.0,athletics,0,1,0 -781301320,Orsolya Kaso,HUN,female,1988-11-22,1.87,72.0,aquatics,0,0,0 -149626539,Orsolya Takacs,HUN,female,1985-05-20,1.9,83.0,aquatics,0,0,0 -878993673,Orukpe Eraiyokan,NGR,male,1993-12-20,1.5,73.0,athletics,0,0,0 -257208022,Oscar Albeiro Figueroa Mosquera,COL,male,1983-04-27,1.59,62.0,weightlifting,1,0,0 -904285900,Oscar Ayodi,KEN,male,1989-09-21,1.84,94.0,rugby sevens,0,0,0 -313345460,Oscar Carrera,ESP,male,1991-05-09,1.9,86.0,canoe,0,0,0 -199312817,Oscar Luis Munoz Oviedo,COL,male,1993-05-09,1.78,58.0,taekwondo,0,0,0 -12259956,Oscar Ouma,KEN,male,1989-05-03,1.86,105.0,rugby sevens,0,0,0 -497523485,Oscar Salas,HON,male,1993-12-08,1.72,73.0,football,0,0,0 -473764617,Oscar Soliz Vilca,BOL,male,1985-01-09,1.68,59.0,cycling,0,0,0 -935041171,Osea Kolinisau,FIJ,male,1985-11-17,1.74,90.0,rugby sevens,1,0,0 -448898364,Oskar Deecke,GER,male,1986-05-16,1.83,78.0,hockey,0,0,0 -553047126,Oskar Kirmes,FIN,male,1995-12-19,1.7,62.0,gymnastics,0,0,0 -674907192,Oskari Moro,FIN,male,1993-01-31,1.81,71.0,athletics,0,0,0 -96259345,Osleni Guerrero,CUB,male,1989-10-18,1.89,88.0,badminton,0,0,0 -109325919,Osmaidel Pellicier,CUB,male,1992-03-30,1.88,88.0,athletics,0,0,0 -324446347,Osman Kamara,SLE,male,1987-12-31,,78.0,aquatics,0,0,0 -115062630,Osmany Juantorena,ITA,male,1985-08-12,2.0,85.0,volleyball,0,1,0 -943830865,Osniel Cecilio Rendon Gonzalez,CUB,male,1996-10-26,2.02,90.0,volleyball,0,0,0 -961508449,Osniel Lazaro Melgarejo Hernandez,CUB,male,1997-12-18,1.95,83.0,volleyball,0,0,0 -657263947,Oswaldo dos Santos Guimaraes,BRA,male,1989-10-23,1.83,90.0,handball,0,0,0 -878075998,Otar Bestaev,KGZ,male,1991-10-28,1.6,60.0,judo,0,0,0 -953914833,Otgonbaatar Lkhagvasuren,MGL,male,1993-01-20,1.82,94.0,judo,0,0,0 -358198982,Otgonbayar Luvsanlundeg,MGL,female,1982-07-13,1.53,47.0,athletics,0,0,0 -349670408,Otgondalai Dorjnyambuu,MGL,male,1988-01-28,1.7,,boxing,0,0,1 -830288020,Otgontsetseg Galbadrakh,KAZ,female,1992-01-25,1.65,48.0,judo,0,0,1 -605590898,Oumar Toure,MLI,male,1996-01-24,1.59,57.0,aquatics,0,0,0 -570635423,Oumou Toure,SEN,female,1988-02-18,1.89,86.0,basketball,0,0,0 -748074466,Oumoul Thiam,SEN,female,1990-02-03,1.79,68.0,basketball,0,0,0 -517816469,Ourania Rebouli,GRE,female,1989-05-16,1.64,49.0,athletics,0,0,0 -819236187,Oussama Boughanmi,TUN,male,1990-02-05,1.85,87.0,handball,0,0,0 -304396524,Oussama Darfalou,ALG,male,1993-09-29,1.86,75.0,football,0,0,0 -311177552,Oussama Hosni,TUN,male,1992-09-17,1.92,88.0,handball,0,0,0 -518631495,Oussama Mellouli,TUN,male,1984-02-16,1.92,84.0,aquatics,0,0,0 -538663975,Oussama Methazem,ALG,male,1993-12-15,1.8,75.0,football,0,0,0 -374267293,Oussama Oueslati,TUN,male,1996-03-24,1.99,80.0,taekwondo,0,0,1 -16385389,Oussama Sahnoune,ALG,male,1992-08-02,1.87,84.0,aquatics,0,0,0 -242873978,Ousseini Djibo Idrissa,NIG,male,1998-12-28,1.78,72.0,athletics,0,0,0 -806202831,Ovidiu Ionescu,ROU,male,1989-06-28,1.82,72.0,table tennis,0,0,0 -347377740,Ovini Uera,NRU,male,1988-01-18,1.7,90.0,judo,0,0,0 -745286605,Owain Doull,GBR,male,1993-05-02,1.81,73.0,cycling,1,0,0 -581338975,Ozge Bayrak,TUR,female,1992-02-14,1.66,60.0,badminton,0,0,0 -726787607,Ozlem Kaya,TUR,female,1990-04-20,1.65,47.0,athletics,0,0,0 -489753983,Pa Konate,SWE,male,1994-04-25,1.71,71.0,football,0,0,0 -178909248,Pablo Abian,ESP,male,1985-06-12,1.77,68.0,badminton,0,0,0 -592233151,Pablo Aprahamian,URU,male,1985-09-13,1.8,97.0,judo,0,0,0 -360159830,Pablo Barrios,VEN,male,1964-07-14,1.79,72.0,equestrian,0,0,0 -373537560,Pablo Braegger,SUI,male,1992-11-27,1.69,64.0,gymnastics,0,0,0 -761571916,Pablo Carrera,ESP,male,1986-08-02,1.82,93.0,shooting,0,0,0 -800654428,Pablo Crer,ARG,male,1989-06-12,2.02,85.0,volleyball,0,0,0 -310836166,Pablo Cuevas,URU,male,1986-01-01,1.8,79.0,tennis,0,0,0 -505304017,Pablo Defazio Abella,URU,male,1981-05-15,1.7,67.0,sailing,0,0,0 -284444203,Pablo Feijoo,ESP,male,1982-05-18,1.74,74.0,rugby sevens,0,0,0 -76294513,Pablo Fontes,ESP,male,1995-12-25,1.75,83.0,rugby sevens,0,0,0 -872745031,Pablo Herrera Allepuz,ESP,male,1982-06-29,1.93,88.0,volleyball,0,0,0 -588392494,Pablo Portela,ARG,male,1980-06-21,1.92,92.0,handball,0,0,0 -761942978,Pablo Simonet,ARG,male,1992-05-04,1.85,90.0,handball,0,0,0 -485821252,Pablo Torrijos,ESP,male,1992-05-12,1.85,76.0,athletics,0,0,0 -883565359,Pablo Vaistein,ARG,male,1989-07-18,1.84,89.0,handball,0,0,0 -283173000,Pablo de Torres,ARG,male,1984-04-14,1.9,86.0,canoe,0,0,0 -44439135,Paciencia,POR,male,1994-08-01,1.87,80.0,football,0,0,0 -49599733,Padasak Tanviriyavechakul,THA,male,1996-05-17,1.68,60.0,table tennis,0,0,0 -423696002,Padraig Harrington,IRL,male,1971-08-31,1.85,86.0,golf,0,0,0 -74504490,Padraig McCarthy,IRL,male,1977-07-18,1.8,80.0,equestrian,0,0,0 -548161228,Paige McPherson,USA,female,1990-10-01,1.73,65.0,taekwondo,0,0,0 -722165425,Paige Railey,USA,female,1987-05-15,1.73,66.0,sailing,0,0,0 -419662300,Paige Satchell,NZL,female,1998-04-13,1.63,50.0,football,0,0,0 -938845959,Paige Selenski,USA,female,1990-06-30,1.71,61.0,hockey,0,0,0 -883350095,Paixao Afonso,ANG,male,1991-01-02,1.76,67.0,sailing,0,0,0 -674501891,Pal Joensen,DEN,male,1990-12-10,1.83,76.0,aquatics,0,0,0 -712874334,Palmira Marcal,BRA,female,1984-05-20,1.74,76.0,basketball,0,0,0 -221272276,Paloma Schmidt Gutierrez,PER,female,1987-01-24,1.65,65.0,sailing,0,0,0 -442939892,Pamela Dutkiewicz,GER,female,1991-09-28,1.7,61.0,athletics,0,0,0 -608348414,Pamela Nogueira,BRA,female,1988-07-17,1.64,54.0,aquatics,0,0,0 -603375979,Pamela Ware,CAN,female,1993-02-12,1.61,55.0,aquatics,0,0,0 -495081578,Pamella Oliveira,BRA,female,1987-10-06,1.65,60.0,triathlon,0,0,0 -984045803,Panagiotis Gionis,GRE,male,1980-01-07,1.87,85.0,table tennis,0,0,0 -795025850,Panagiotis Magdanis,GRE,male,1990-11-29,1.75,71.0,rowing,0,0,0 -604177499,Panagiotis Mantis,GRE,male,1981-09-30,1.7,65.0,sailing,0,0,1 -452613023,Panagiotis Samilidis,GRE,male,1993-08-09,1.87,91.0,aquatics,0,0,0 -264595645,Panayiota Tsinopoulou,GRE,female,1990-10-16,1.65,54.0,athletics,0,0,0 -750775977,Panayiota Vlahaki,GRE,female,1991-04-03,1.67,50.0,athletics,0,0,0 -19012939,Pancho Paskov,BUL,male,1994-05-09,1.78,77.0,fencing,0,0,0 -86712158,Pandelela Rinong Pamg,MAS,female,1993-03-02,1.61,52.0,aquatics,0,1,0 -350338417,Panipak Wongpattanakit,THA,female,1997-08-08,1.73,49.0,taekwondo,0,0,1 -911324763,Paola Espinosa,MEX,female,1986-07-31,1.56,48.0,aquatics,0,0,0 -124063612,Paola Munoz,CHI,female,1986-04-13,1.64,54.0,cycling,0,0,0 -322724107,Paola Ogechi Egonu,ITA,female,1998-12-18,1.9,70.0,volleyball,0,0,0 -11943738,Paola Perez,VEN,female,1991-04-05,1.62,54.0,aquatics,0,0,0 -146805157,Paola Perez,ECU,female,1989-12-21,1.45,43.0,athletics,0,0,0 -340714642,Paolo Lorenzi,ITA,male,1981-12-15,1.84,77.0,tennis,0,0,0 -882385123,Paolo Nicolai,ITA,male,1988-08-06,2.04,100.0,volleyball,0,1,0 -654810825,Paolo Pizzo,ITA,male,1983-04-04,1.8,70.0,fencing,0,1,0 -388627308,Paolo Yurivilca,PER,male,1996-04-23,1.69,62.0,athletics,0,0,0 -443908875,Pap D. Jonga,GAM,male,1997-07-01,,,aquatics,0,0,0 -442404063,Par Gerell,SWE,male,1982-06-23,1.74,68.0,table tennis,0,0,0 -832744866,Paraskevi Papahristou,GRE,female,1989-04-17,1.7,53.0,athletics,0,0,0 -363332921,Pardeep Mor,IND,male,1992-06-03,1.76,67.0,hockey,0,0,0 -954374812,Pardon Ndhlovu,ZIM,male,1987-08-23,1.58,54.0,athletics,0,0,0 -593995753,Paris Henken,USA,female,1995-12-22,1.68,63.0,sailing,0,0,0 -318649476,Parvenn Rana,IND,male,1992-10-12,,,wrestling,0,0,0 -851811760,Parviz Baghirov,AZE,male,1994-02-10,1.77,,boxing,0,0,0 -390284488,Pascal Gregor,DEN,male,1994-02-18,1.89,82.0,football,0,0,0 -609470264,Pascal Lussier,CAN,male,1991-09-13,1.9,90.0,rowing,0,0,0 -671651436,Pascal Martinot-Lagarde,FRA,male,1991-09-22,1.89,85.0,athletics,0,0,0 -663249321,Pascal Plamondon,CAN,male,1992-12-12,1.71,85.0,weightlifting,0,0,0 -323658987,Pasquale Sottile,ITA,male,1979-08-17,1.86,73.0,volleyball,0,1,0 -52489905,Pat McCormack,GBR,male,1995-06-08,1.82,,boxing,0,0,0 -975309523,Pat McCutcheon,AUS,male,1987-06-24,1.87,105.0,rugby sevens,0,0,0 -705420033,Patience Okon George,NGR,female,1991-11-25,1.69,63.0,athletics,0,0,0 -522216516,Patimat Abakarova,AZE,female,1994-10-23,1.65,49.0,taekwondo,0,0,1 -244723566,Patricia Alejandra Bermudez,ARG,female,1987-02-05,1.5,48.0,wrestling,0,0,0 -539946489,Patricia Castro Ortega,ESP,female,1992-08-06,1.78,66.0,aquatics,0,0,0 -775629274,Patricia Elorza,ESP,female,1984-04-08,1.8,78.0,handball,0,0,0 -919589860,Patricia Freitas,BRA,female,1990-03-10,1.73,59.0,sailing,0,0,0 -505669658,Patricia Garcia,ESP,female,1989-12-02,1.63,64.0,rugby sevens,0,0,0 -295886630,Patricia Herrera Fernandez,ESP,female,1993-02-09,1.63,59.0,aquatics,0,0,0 -618669054,Patricia Mamona,POR,female,1988-11-21,1.67,60.0,athletics,0,0,0 -81353559,Patricia Obee,CAN,female,1991-10-31,1.65,60.0,rowing,0,1,0 -784355437,Patricia Sarrapio,ESP,female,1982-11-16,1.65,56.0,athletics,0,0,0 -899859792,Patricia Taea,COK,female,1993-05-25,1.7,67.0,athletics,0,0,0 -912531799,Patricia Vizitiu,ROU,female,1988-10-15,1.75,68.0,handball,0,0,0 -463070123,Patricio Garino,ARG,male,1993-05-17,1.96,96.0,basketball,0,0,0 -703130921,Patrick Barnes,IRL,male,1987-04-09,1.63,,boxing,0,0,0 -581538049,Patrick Constable,AUS,male,1995-07-15,1.83,95.0,cycling,0,0,0 -732337980,Patrick Dogue,GER,male,1992-03-09,1.97,81.0,modern pentathlon,0,0,0 -588164500,Patrick Groetzki,GER,male,1989-07-04,1.9,83.0,handball,0,0,0 -258619134,Patrick Hausding,GER,male,1989-03-09,1.8,77.0,aquatics,0,0,1 -883872117,Patrick Huston,GBR,male,1996-01-05,1.83,84.0,archery,0,0,0 -319404194,Patrick Lourenco,BRA,male,1993-07-02,1.64,,boxing,0,0,0 -949167573,Patrick Reed,USA,male,1990-08-05,1.83,90.0,golf,0,0,0 -799162684,Patrick Tiernan,AUS,male,1994-09-11,1.83,83.0,athletics,0,0,0 -765058141,Patrick Wiencek,GER,male,1989-03-22,2.0,110.0,handball,0,0,1 -764251988,Patrick van der Heijden,BRA,male,1992-09-19,1.8,70.0,hockey,0,0,0 -121079592,Patrik Kittel,SWE,male,1976-06-10,1.86,79.0,equestrian,0,0,0 -745764315,Patrik Tybor,SVK,male,1987-09-16,1.86,89.0,cycling,0,0,0 -81021268,Patrycja Piechowiak,POL,female,1992-09-01,1.62,68.0,weightlifting,0,0,0 -393072060,Patrycja Wyciszkiewicz,POL,female,1994-01-08,1.73,58.0,athletics,0,0,0 -263761754,Patryk Dobek,POL,male,1994-02-13,1.87,75.0,athletics,0,0,0 -597321452,Patty Mills,AUS,male,1988-08-11,1.83,85.0,basketball,0,0,0 -899162102,Pau Gasol,ESP,male,1980-07-06,2.15,115.0,basketball,0,0,1 -766117830,Pau Quemada,ESP,male,1983-09-04,1.73,69.0,hockey,0,0,0 -416399703,Pau Tonnesen,ESP,male,1992-10-24,1.85,77.0,athletics,0,0,0 -701588939,Pau Vela Maggi,ESP,male,1986-05-31,1.9,86.0,rowing,0,0,0 -133432446,Paul Adams,AUS,male,1992-06-04,1.85,98.0,shooting,0,0,0 -780137117,Paul Bennett,GBR,male,1988-12-16,2.07,100.0,rowing,1,0,0 -424996653,Paul Biedermann,GER,male,1986-08-07,1.93,97.0,aquatics,0,0,0 -577741234,Paul Drinkhall,GBR,male,1990-01-16,1.76,80.0,table tennis,0,0,0 -454655220,Paul Drux,GER,male,1995-02-07,1.92,106.0,handball,0,0,1 -364360958,Paul Estermann,SUI,male,1963-06-24,1.8,78.0,equestrian,0,0,0 -209482391,Paul George,USA,male,1990-05-02,2.03,99.0,basketball,1,0,0 -918519534,Paul Gleghorne,IRL,male,1987-04-11,1.87,82.0,hockey,0,0,0 -534134295,Paul Kibikai,GAB,male,1991-04-04,1.8,80.0,judo,0,0,0 -498755923,Paul Kipkemoi Chelimo,USA,male,1990-10-27,1.8,57.0,athletics,0,1,0 -357755328,Paul Kipngetich Tanui,KEN,male,1990-12-22,1.68,47.0,athletics,0,1,0 -29049380,Paul Kohlhoff,GER,male,1995-06-26,1.87,83.0,sailing,0,0,0 -367795663,Paul O'Donovan,IRL,male,1994-04-19,1.77,70.0,rowing,0,1,0 -674380639,Paul Omba Biongolo,FRA,male,1995-12-28,1.89,,boxing,0,0,0 -350939465,Paul Pollock,IRL,male,1986-06-25,1.77,57.0,athletics,0,0,0 -696711515,Paul Sieber,AUT,male,1993-02-12,1.77,70.0,rowing,0,0,0 -221321947,Paul Snow-Hansen,NZL,male,1990-09-03,1.74,64.0,sailing,0,0,0 -407521099,Paula Cristina Goncalves,BRA,female,1990-08-11,1.76,68.0,tennis,0,0,0 -134532053,Paula Ishibashi,BRA,female,1985-02-14,1.56,58.0,rugby sevens,0,0,0 -927721952,Paula Kania,POL,female,1992-11-06,1.73,63.0,tennis,0,0,0 -820732854,Paula Leiton Arrones,ESP,female,2000-04-27,1.87,98.0,aquatics,0,0,0 -990523633,Paula Lynn Obanana,USA,female,1985-03-19,1.61,58.0,badminton,0,0,0 -55960503,Paula Medin,ESP,female,1984-06-17,1.71,66.0,rugby sevens,0,0,0 -300343611,Paula Pareto,ARG,female,1986-01-16,1.5,48.0,judo,1,0,0 -794648133,Paula Reto,RSA,female,1990-05-03,1.7,62.0,golf,0,0,0 -870585003,Paula Ungureanu,ROU,female,1980-03-30,1.81,75.0,handball,0,0,0 -92938402,Paula Yamila Nizetich,ARG,female,1989-01-27,1.81,74.0,volleyball,0,0,0 -612802155,Paula-Claudia Todoran,ROU,female,1985-06-09,1.64,48.0,athletics,0,0,0 -250731306,Paulina Buziak,POL,female,1986-12-16,1.7,48.0,athletics,0,0,0 -102706397,Paulina Guba,POL,female,1991-05-14,1.83,104.0,athletics,0,0,0 -8892570,Paulina Schmiedel,GER,female,1993-05-29,1.75,61.0,aquatics,0,0,0 -483710937,Pauline Biscarat,FRA,female,1989-05-08,1.57,53.0,rugby sevens,0,0,0 -401150367,Pauline Ferrand Prevot,FRA,female,1992-02-10,1.64,52.0,cycling,0,0,0 -161474093,Pauline Hammarlund,SWE,female,1994-05-07,1.72,65.0,football,0,1,0 -780010350,Pauline Pousse,FRA,female,1987-09-17,1.84,84.0,athletics,0,0,0 -685006892,Pauline Schaefer,GER,female,1997-01-04,1.62,55.0,gymnastics,0,0,0 -934414617,Paulius Jankunas,LTU,male,1984-04-29,2.05,105.0,basketball,0,0,0 -208751523,Paulo Amotun,ROT,male,1992-01-01,1.7,61.0,athletics,0,0,0 -311057526,Paulo Batista,BRA,male,1993-01-27,1.85,90.0,hockey,0,0,0 -121145247,Paulo Bernardo Reichardt,PAR,male,1960-08-08,1.98,105.0,shooting,0,0,0 -168215468,Paulo Henrique,POR,male,1996-10-23,1.8,75.0,football,0,0,0 -185993510,Paulo Roberto Paula,BRA,male,1979-07-08,1.7,55.0,athletics,0,0,0 -538800855,Paulo Salemi,BRA,male,1993-08-08,1.91,94.0,aquatics,0,0,0 -495685388,Pauls Pujats,LAT,male,1991-08-06,1.87,83.0,athletics,0,0,0 -138975413,Pavel Bareisha,BLR,male,1991-02-16,1.93,118.0,athletics,0,0,0 -290600382,Pavel Chihuan,PER,male,1986-01-19,1.7,70.0,athletics,0,0,0 -88762996,Pavel Eigel,RUS,male,1990-03-01,1.78,81.0,canoe,0,0,0 -647042868,Pavel Ilyashenko,KAZ,male,1990-06-23,1.81,76.0,modern pentathlon,0,0,0 -266152637,Pavel Janecek,CZE,male,1994-04-07,1.95,87.0,aquatics,0,0,0 -286853003,Pavel Kastramin,BLR,male,1991-07-12,1.76,,boxing,0,0,0 -693513512,Pavel Kelemen,CZE,male,1991-05-28,1.85,83.0,cycling,0,0,0 -436853301,Pavel Khadasevich,BLR,male,1993-07-16,1.73,84.0,weightlifting,0,0,0 -99074928,Pavel Kochetkov,RUS,male,1986-03-07,1.84,72.0,cycling,0,0,0 -223572860,Pavel Maslak,CZE,male,1991-02-21,1.76,66.0,athletics,0,0,0 -455949717,Pavel Petrikov,CZE,male,1986-06-20,1.7,64.0,judo,0,0,0 -321729482,Pavel Sankovich,BLR,male,1990-06-29,1.82,77.0,aquatics,0,0,0 -663676373,Pavel Sozykin,RUS,male,1987-12-25,,,sailing,0,0,0 -654016001,Pavel Sukhov,RUS,male,1988-05-07,1.79,75.0,fencing,0,0,0 -941704131,Pavle Kostov,CRO,male,1987-09-28,1.8,82.0,sailing,0,0,0 -122873337,Pavlo Altukhov,UKR,male,1995-12-23,1.85,90.0,canoe,0,0,0 -124577739,Pavlo Korostylov,UKR,male,1997-11-05,1.86,100.0,shooting,0,0,0 -476028194,Pavlo Matsuyev,UKR,male,1990-11-05,1.81,72.0,sailing,0,0,0 -713017392,Pavlo Tymoshchenko,UKR,male,1986-10-13,1.92,78.0,modern pentathlon,0,1,0 -62996678,Pavlos Kagialis,GRE,male,1984-07-14,1.84,74.0,sailing,0,0,1 -18058430,Pavlos Kontides,CYP,male,1990-02-11,1.83,82.0,sailing,0,0,0 -517302167,Pavol Kopp,SVK,male,1978-12-27,1.83,74.0,shooting,0,0,0 -253678456,Pawel Fajdek,POL,male,1989-06-04,1.86,126.0,athletics,0,0,0 -196109685,Pawel Juraszek,POL,male,1994-10-08,,,aquatics,0,0,0 -491762709,Pawel Kaczmarek,POL,male,1995-09-08,1.83,85.0,canoe,0,0,0 -165395706,Pawel Kolodzinski,POL,male,1988-01-07,1.9,83.0,sailing,0,0,0 -943548615,Pawel Korzeniowski,POL,male,1985-07-09,1.92,86.0,aquatics,0,0,0 -301937277,Pawel Spisak,POL,male,1981-09-29,1.76,68.0,equestrian,0,0,0 -908561919,Pawel Wiesiolek,POL,male,1991-08-13,1.9,90.0,athletics,0,0,0 -147490715,Pawel Wojciechowski,POL,male,1989-06-06,1.9,85.0,athletics,0,0,0 -983705869,Pawel Zatorski,POL,male,1990-06-21,1.84,73.0,volleyball,0,0,0 -656447088,Peace Uko,NGR,female,1995-12-26,1.61,58.0,athletics,0,0,0 -34305365,Peamwilai Laopeam,THA,female,1983-10-20,1.63,,boxing,0,0,0 -260021417,Peder Fredricson,SWE,male,1972-01-30,1.89,80.0,equestrian,0,1,0 -361452199,Pedro Daniel Gomez,MEX,male,1990-12-31,1.78,70.0,athletics,0,0,0 -373434726,Pedro Francisco Ceballos Fuentes,VEN,male,1990-09-08,1.8,86.0,wrestling,0,0,0 -750408155,Pedro Ibarra,ARG,male,1985-09-11,1.74,75.0,hockey,1,0,0 -790093790,Pedro Isidro,POR,male,1985-07-17,1.75,58.0,athletics,0,0,0 -191275004,Pedro Luiz de Oliveira,BRA,male,1992-02-17,1.8,83.0,athletics,0,0,0 -337351687,Pedro Martins,POR,male,1990-02-14,1.77,70.0,badminton,0,0,0 -322923312,Pedro Miguel Pinotes,ANG,male,1989-09-30,1.92,85.0,aquatics,0,0,0 -863329559,Pedro P. Pichardo,CUB,male,1993-06-30,1.83,68.0,athletics,0,0,0 -85634845,Pedro Pascual,USA,male,1996-03-15,1.86,69.0,sailing,0,0,0 -696910008,Pedro Rangel,MEX,male,1988-09-16,1.92,85.0,volleyball,0,0,0 -173848012,Pedro Solberg,BRA,male,1986-03-27,1.94,90.0,volleyball,0,0,0 -66114756,Pedro Tavares de Almeida,BRA,male,1993-12-11,1.76,78.0,equestrian,0,0,0 -299349112,Pedro Veniss,BRA,male,1983-01-06,1.81,66.0,equestrian,0,0,0 -595693599,Pedro da Silva,BRA,male,1993-04-12,1.76,69.0,canoe,0,0,0 -202596543,Pedrya Seymour,BAH,female,1995-05-29,,,athletics,0,0,0 -45726783,Peer Borsky,SUI,male,1990-11-05,1.92,80.0,fencing,0,0,0 -703096164,Peeter Olesk,EST,male,1993-04-22,1.79,70.0,shooting,0,0,0 -553824405,Pei-Wun Lin,TPE,female,1999-11-25,1.74,61.0,aquatics,0,0,0 -678886243,Peimeng Zhang,CHN,male,1987-03-13,1.86,78.0,athletics,0,0,0 -374248899,Peina Chen,CHN,female,1989-06-19,1.72,63.0,sailing,0,1,0 -773795384,Penelope Leprevost,FRA,female,1980-08-01,1.75,55.0,equestrian,1,0,0 -703818710,Peng Han,CHN,female,1989-12-20,1.65,60.0,football,0,0,0 -709692617,Peng Soon Chan,MAS,male,1988-04-27,1.75,68.0,badminton,0,1,0 -983734556,Peng Tang,HKG,male,1981-02-04,1.78,77.0,table tennis,0,0,0 -431074718,Peng Zhou,CHN,male,1989-10-11,2.06,102.0,basketball,0,0,0 -239797947,Penny Oleksiak,CAN,female,2000-06-13,1.86,68.0,aquatics,1,1,2 -56646013,Penny Taylor,AUS,female,1981-05-24,1.85,72.0,basketball,0,0,0 -319525295,Pernilla Lindberg,SWE,female,1986-07-13,1.65,60.0,golf,0,0,0 -489476635,Pernille Blume,DEN,female,1994-05-14,1.7,58.0,aquatics,1,0,1 -653763497,Perrine Clauzel,FRA,female,1994-04-05,1.55,47.0,cycling,0,0,0 -847560874,Perry Baker,USA,male,1986-06-29,1.86,81.0,rugby sevens,0,0,0 -607325017,Perseus Karlstrom,SWE,male,1990-05-02,1.84,73.0,athletics,0,0,0 -831774748,Persis William-Mensah,GHA,female,1996-06-15,1.74,52.0,athletics,0,0,0 -923556859,Peruth Chemutai,UGA,female,1999-07-10,,,athletics,0,0,0 -71344147,Petar Cupac,CRO,male,1980-02-01,1.82,77.0,sailing,0,0,0 -325195799,Petar Gorsa,CRO,male,1988-01-11,1.8,82.0,shooting,0,0,0 -336214928,Petar Tomasevic,FRA,male,1989-01-02,1.92,100.0,aquatics,0,0,0 -78269978,Pete Reed,GBR,male,1981-07-27,1.97,100.0,rowing,1,0,0 -495318551,Peter Bacsi,HUN,male,1983-05-15,1.75,82.0,wrestling,0,0,0 -819872199,Peter Bernek,HUN,male,1992-04-13,1.93,83.0,aquatics,0,0,0 -624757765,Peter Bol,AUS,male,1994-02-22,1.77,63.0,athletics,0,0,0 -59945226,Peter Burling,NZL,male,1991-01-01,1.86,82.0,sailing,1,0,0 -136312420,Peter Caruth,IRL,male,1988-06-04,1.73,73.0,hockey,0,0,0 -934977346,Peter Chambers,GBR,male,1990-03-14,1.86,72.0,rowing,0,0,0 -817847784,Peter Elisa Henry,COK,male,1990-08-14,1.7,82.0,sailing,0,0,0 -496549000,Peter Gelle,SVK,male,1984-08-23,1.83,82.0,canoe,0,0,0 -846408678,Peter Holoda,HUN,male,1996-01-09,1.96,90.0,aquatics,0,0,0 -596350083,Peter Joppich,GER,male,1982-12-21,1.76,68.0,fencing,0,0,0 -856913107,Peter Kauzer,SLO,male,1983-09-08,1.77,70.0,canoe,0,1,0 -968123142,Peter Lambert,GBR,male,1986-12-03,1.9,90.0,rowing,0,0,0 -703243129,Peter Lombard Ii,GUM,male,1976-05-24,1.7,73.0,cycling,0,0,0 -713355622,Peter Matthews,JAM,male,1989-11-13,1.88,84.0,athletics,0,1,0 -246570363,Peter Molnar,HUN,male,1986-02-16,1.88,88.0,canoe,0,0,0 -788615502,Peter Mullenberg,NED,male,1987-12-30,1.83,,boxing,0,0,0 -730243242,Peter Mungai Warui,KEN,male,1981-04-22,,,boxing,0,0,0 -208033661,Peter Nagy,HUN,male,1986-01-16,1.92,159.0,weightlifting,0,0,0 -945986909,Peter Sagan,SVK,male,1990-01-26,1.82,79.0,cycling,0,0,0 -183289884,Peter Sidi,HUN,male,1978-09-11,1.76,86.0,shooting,0,0,0 -4878555,Peter Skantar,SVK,male,1982-07-20,1.82,78.0,canoe,1,0,0 -690076235,Peter Somfai,HUN,male,1980-04-02,1.88,85.0,fencing,0,0,1 -966741128,Peter Taylor,NZL,male,1984-01-03,1.89,70.0,rowing,0,0,0 -289227297,Peter Wiersum,NED,male,1984-11-01,1.73,55.0,rowing,0,0,1 -338590323,Peter van Schie,NED,male,1988-03-03,2.0,98.0,rowing,0,0,0 -219934113,Peterson dos Santos,BRA,male,1991-03-31,1.81,70.0,athletics,0,0,0 -70412036,Petit David Minkoumba,CMR,male,1989-02-27,1.71,92.0,weightlifting,0,0,0 -878015006,Petr Asayonak,BLR,male,1993-02-27,1.65,84.0,weightlifting,0,0,0 -874838042,Petr Frydrych,CZE,male,1988-01-13,2.0,100.0,athletics,0,0,0 -211340599,Petr Khamukov,RUS,male,1991-07-15,1.72,,boxing,0,0,0 -799782526,Petr Koukal,CZE,male,1985-12-14,1.92,92.0,badminton,0,0,0 -166411555,Petr Svoboda,CZE,male,1984-10-10,1.95,90.0,athletics,0,0,0 -402795962,Petr Vakoc,CZE,male,1992-07-11,1.8,70.0,cycling,0,0,0 -20631920,Petra Fontanive,SUI,female,1988-10-10,1.7,59.0,athletics,0,0,0 -6323208,Petra Kvitova,CZE,female,1990-03-08,1.81,68.0,tennis,0,0,1 -705078758,Petra Lovas,HUN,female,1980-07-04,1.58,48.0,table tennis,0,0,0 -395735676,Petra Maarit Olli,FIN,female,1994-06-05,1.63,64.0,wrestling,0,0,0 -326772928,Petra Zublasing,ITA,female,1989-06-30,1.64,55.0,shooting,0,0,0 -956175465,Petrea Webster,NZL,female,1988-03-30,1.65,56.0,hockey,0,0,0 -921402374,Petrissa Solja,GER,female,1994-03-11,1.68,67.0,table tennis,0,1,0 -780238765,Petro Pakhnyuk,AZE,male,1991-11-26,1.73,64.0,gymnastics,0,0,0 -797449371,Petter Menning,SWE,male,1987-08-08,1.87,92.0,canoe,0,0,0 -556102017,Pezhman Ghalehnoei,IRI,male,1992-01-29,1.9,96.0,athletics,0,0,0 -975709612,Phannapa Harnsujin,THA,female,1997-09-14,1.72,57.0,taekwondo,0,0,0 -905495882,Phara Anacharsis,FRA,female,1983-12-17,1.77,58.0,athletics,0,0,0 -966511165,Phelan Hill,GBR,male,1979-07-21,1.72,55.0,rowing,1,0,0 -729088638,Phetetso Monese,LES,male,1984-09-22,,,cycling,0,0,0 -11938440,Phil Burgess,GBR,male,1988-07-01,1.8,92.0,rugby sevens,0,1,0 -514082441,Philadelphia Orlando,KEN,female,1990-02-18,1.54,72.0,rugby sevens,0,0,0 -805140825,Philip Dalhausser,USA,male,1980-01-26,2.06,92.0,volleyball,0,0,0 -492969583,Philip Heintz,GER,male,1991-02-21,1.94,85.0,aquatics,0,0,0 -229176554,Philip Hindes,GBR,male,1992-09-22,1.78,82.0,cycling,1,0,0 -289790809,Philip Milanov,BEL,male,1991-07-06,1.98,110.0,athletics,0,0,0 -286704073,Philip Snyman,RSA,male,1987-03-26,1.88,95.0,rugby sevens,0,0,1 -194978733,Philip Stenmalm,SWE,male,1992-03-03,2.0,103.0,handball,0,0,0 -615802244,Philipine van Aanholt,ARU,female,1992-05-26,1.71,65.0,sailing,0,0,0 -544522799,Philipp Buhl,GER,male,1989-12-19,1.87,85.0,sailing,0,0,0 -915966505,Philipp Kohlschreiber,GER,male,1983-10-16,1.8,70.0,tennis,0,0,0 -377804310,Philipp Max,GER,male,1993-09-30,1.77,76.0,football,0,1,0 -989265999,Philipp Pflieger,GER,male,1987-07-16,1.88,70.0,athletics,0,0,0 -417212497,Philipp Wende,GER,male,1985-07-04,1.99,90.0,rowing,1,0,0 -858470126,Philipp Wolf,GER,male,1992-08-15,1.96,96.0,aquatics,0,0,0 -220899684,Philippe Gagne,CAN,male,1997-10-23,1.8,68.0,aquatics,0,0,0 -897247005,Philippe Gilbert,BEL,male,1982-07-05,1.79,72.0,cycling,0,0,0 -274768032,Philippe Rozier,FRA,male,1963-02-05,1.73,63.0,equestrian,1,0,0 -595488187,Phillip Chew,USA,male,1994-05-16,1.73,90.0,badminton,0,0,0 -355129780,Phillip Dutton,USA,male,1963-09-13,1.68,68.0,equestrian,0,0,1 -330465212,Phillip Kipyeko,UGA,male,1995-01-01,,,athletics,0,0,0 -113831851,Phumlani Ntshangase,RSA,male,1994-12-24,1.76,71.0,football,0,0,0 -417884111,Phumlile Ndzinisa,SWZ,female,1992-08-21,1.7,57.0,athletics,0,0,0 -784201731,Phuoc Hoang,VIE,male,1993-03-24,1.8,75.0,aquatics,0,0,0 -524378907,Phuoc Hung Pham,VIE,male,1988-06-10,1.62,55.0,gymnastics,0,0,0 -74346826,Phupu Lamu Khatri,NEP,female,1996-10-05,1.63,63.0,judo,0,0,0 -864167150,Phuttharaksa Neegree,THA,female,1974-02-25,1.66,67.0,rowing,0,0,0 -888940150,Phylicia George,CAN,female,1987-11-16,1.78,65.0,athletics,0,0,0 -221313812,Phyllis Francis,USA,female,1992-05-04,1.81,71.0,athletics,1,0,0 -775856261,Pia-Sophie Oldhafer,GER,female,1992-07-01,1.66,55.0,hockey,0,0,1 -196591431,Pier Paolo Petroni,ITA,male,1987-03-30,1.8,70.0,modern pentathlon,0,0,0 -193264881,Piero Codia,ITA,male,1989-10-22,1.9,80.0,aquatics,0,0,0 -93324634,Pierpaolo Frattini,ITA,male,1984-02-23,1.92,92.0,rowing,0,0,0 -652989457,Pierre Duprat,FRA,male,1989-11-26,1.75,73.0,judo,0,0,0 -424639600,Pierre Gilles Lakafia,FRA,male,1987-03-12,1.83,95.0,rugby sevens,0,0,0 -381048812,Pierre Houin,FRA,male,1994-04-15,1.82,74.0,rowing,1,0,0 -760941047,Pierre Le Corre,FRA,male,1990-02-03,1.76,65.0,triathlon,0,0,0 -273264631,Pierre Plihon,FRA,male,1989-10-29,1.84,130.0,archery,0,0,0 -546837114,Pierre Pujol,FRA,male,1984-07-13,1.86,90.0,volleyball,0,0,0 -188712292,Pierre Volla,FRA,male,1981-07-16,1.71,69.0,equestrian,0,0,0 -637459176,Pierre le Coq,FRA,male,1989-01-17,1.86,72.0,sailing,0,0,1 -703056678,Pierre-Ambroise Bosse,FRA,male,1992-05-11,1.85,68.0,athletics,0,0,0 -565117926,Pierre-Hugues Herbert,FRA,male,1991-03-18,1.89,79.0,tennis,0,0,0 -8420504,Pieter Braun,NED,male,1993-01-21,1.87,85.0,athletics,0,0,0 -635258187,Pieter Bulling,NZL,male,1993-03-02,1.79,77.0,cycling,0,0,0 -185824997,Pieter Timmers,BEL,male,1988-01-21,2.0,89.0,aquatics,0,1,0 -712300678,Pieter-Jan Hannes,BEL,male,1992-10-30,1.86,75.0,athletics,0,0,0 -381556932,Pieter-Jan Postma,NED,male,1982-01-10,1.89,99.0,sailing,0,0,0 -556354189,Pietro Figlioli,ITA,male,1984-05-29,1.91,98.0,aquatics,0,0,1 -312015477,Pietro Roman,ITA,male,1989-09-20,1.78,72.0,equestrian,0,0,0 -359623981,Pietro Ruta,ITA,male,1987-08-06,1.85,70.0,rowing,0,0,0 -965100500,Pietro Zucchetti,ITA,male,1981-01-25,1.88,82.0,sailing,0,0,0 -538317694,Pilar Lucrecia Cordon,ESP,female,1973-03-04,1.75,58.0,equestrian,0,0,0 -229833243,Pilar Romang,ARG,female,1992-07-09,1.6,50.0,hockey,0,0,0 -258972749,Pilar Shimizu,GUM,female,1996-05-27,1.68,83.0,aquatics,0,0,0 -417310154,Pim-On Klaisuban,THA,female,1992-07-07,1.67,75.0,shooting,0,0,0 -430131884,Pimsiri Sirikaew,THA,female,1990-04-25,1.52,57.0,weightlifting,0,1,0 -864763299,Ping Li,QAT,male,1986-05-18,1.78,65.0,table tennis,0,0,0 -859561876,Ping Yao,CHN,female,1993-03-19,1.6,58.0,cycling,0,0,0 -138538135,Pingan Shen,CHN,male,1994-04-20,1.82,73.0,cycling,0,0,0 -381746437,Piotr Daniluk,POL,male,1982-01-15,1.75,72.0,shooting,0,0,0 -838910156,Piotr Juszczak,POL,male,1988-07-03,1.97,94.0,rowing,0,0,0 -489240868,Piotr Kantor,POL,male,1992-05-03,2.0,90.0,volleyball,0,0,0 -156639283,Piotr Lisek,POL,male,1992-08-16,1.94,92.0,athletics,0,0,0 -193728095,Piotr Malachowski,POL,male,1983-06-07,1.93,130.0,athletics,0,1,0 -630205806,Piotr Myszka,POL,male,1981-07-25,1.86,74.0,sailing,0,0,0 -798661107,Piotr Nowakowski,POL,male,1987-12-18,2.05,90.0,volleyball,0,0,0 -61020520,Piotr Pazinski,POL,male,1987-08-07,1.87,80.0,taekwondo,0,0,0 -978409862,Piotr Szczepanski,POL,male,1988-07-31,1.8,76.0,canoe,0,0,0 -205004197,Piotr Wyszomirski,POL,male,1988-01-06,1.95,95.0,handball,0,0,0 -780018325,Pippa Funnell,GBR,female,1968-10-07,1.68,68.0,equestrian,0,0,0 -660600598,Pippa Hayward,NZL,female,1990-05-23,1.76,69.0,hockey,0,0,0 -930203213,Pirmammad Aliyev,KAZ,male,1997-11-02,1.7,63.0,gymnastics,0,0,0 -789056524,Pirmin Blaak,NED,male,1988-03-08,1.88,88.0,hockey,0,0,0 -988729820,Pita Nikolas Taufatofua,TGA,male,1983-11-05,1.91,100.0,taekwondo,0,0,0 -825607253,Pite,POR,male,1994-08-22,1.86,80.0,football,0,0,0 -624453875,Po Heung Lin,HKG,female,1985-05-02,1.64,58.0,fencing,0,0,0 -249071689,Pol Arias Dourdet,AND,male,1996-08-08,1.75,80.0,aquatics,0,0,0 -799207392,Pol Moya,AND,male,1996-12-09,1.78,62.0,athletics,0,0,0 -172536875,Pol Pla,ESP,male,1993-02-18,1.77,75.0,rugby sevens,0,0,0 -704580585,Polat Kemboi Arikan,TUR,male,1990-12-12,1.75,59.0,athletics,0,0,0 -974099683,Poliana,BRA,female,1991-02-06,1.72,60.0,football,0,0,0 -39584129,Poliana Okimoto,BRA,female,1983-03-08,1.65,52.0,aquatics,0,0,1 -343525700,Polina Kuznetsova,RUS,female,1987-06-10,1.68,60.0,handball,1,0,0 -543469512,Polina Mikhailova,RUS,female,1986-08-31,1.68,60.0,table tennis,0,0,0 -593041020,Polina Repina,KAZ,female,1990-06-29,1.7,50.0,athletics,0,0,0 -931143770,Polly Powrie,NZL,female,1987-12-09,1.73,70.0,sailing,0,1,0 -663848389,Polly Swann,GBR,female,1988-06-05,1.85,76.0,rowing,0,1,0 -171878949,Polona Batagelj,SLO,female,1989-06-07,1.73,53.0,cycling,0,0,0 -983536887,Polona Hercog,SLO,female,1991-01-20,1.85,70.0,tennis,0,0,0 -254645346,Poonam Rani,IND,female,1993-02-08,1.52,53.0,hockey,0,0,0 -704169798,Poovamma Raju Machettira,IND,female,1990-06-05,1.74,58.0,athletics,0,0,0 -678194406,Popole Misenga,ROT,male,1992-02-25,1.8,90.0,judo,0,0,0 -327752493,Popoola Saliu,NGR,male,1994-08-07,1.7,,football,0,0,1 -319595440,Pornanong Phatlum,THA,female,1989-12-04,1.6,55.0,golf,0,0,0 -169038848,Porntip Buranaprasertsuk,THA,female,1991-10-24,1.65,61.0,badminton,0,0,0 -508827063,Portia Woodman,NZL,female,1991-07-12,1.69,74.0,rugby sevens,0,1,0 -487961386,Pourya Norouziyan,IRI,male,1992-01-22,1.76,63.0,shooting,0,0,0 -373005438,Prakash Nanjappa,IND,male,1976-02-29,1.75,85.0,shooting,0,0,0 -126137216,Praneel Naidu,FIJ,male,1995-01-29,1.76,66.0,football,0,0,0 -339114990,Prarthana G. Thombare,IND,female,1994-06-18,1.65,58.0,tennis,0,0,0 -980803991,Praveen Jordan,INA,male,1993-04-26,1.83,88.0,badminton,0,0,0 -56351006,Predrag Filipovic,SRB,male,1978-10-05,1.82,72.0,athletics,0,0,0 -701283758,Predrag JOKIC,MNE,male,1983-02-03,1.88,102.0,aquatics,0,0,0 -670821875,Preeti Dubey,IND,female,1998-06-13,1.66,53.0,hockey,0,0,0 -534519239,Prenam Pesse,TOG,female,1997-12-31,,,athletics,0,0,0 -990896999,Primoz Roglic,SLO,male,1989-10-29,1.77,66.0,cycling,0,0,0 -11988746,Priscilla Frederick,ANT,female,1989-02-14,1.83,70.0,athletics,0,0,0 -575956679,Priscilla Gneto,FRA,female,1991-08-03,1.64,52.0,judo,0,0,0 -230616729,Priscilla Stevaux Carnaval,BRA,female,1993-12-02,1.56,58.0,cycling,0,0,0 -376160174,Przemyslaw Krajewski,POL,male,1987-01-20,1.84,87.0,handball,0,0,0 -324476209,Przemyslaw Wacha,POL,male,1981-01-31,1.82,81.0,badminton,0,0,0 -319585560,Pui Yin Yip,HKG,female,1987-08-06,1.64,61.0,badminton,0,0,0 -654021545,Puttita Supajirakul,THA,female,1996-03-29,1.84,72.0,badminton,0,0,0 -513769796,Qais Ashfaq,GBR,male,1993-03-10,1.7,,boxing,0,0,0 -598446702,Qendrim Guri,KOS,male,1993-11-27,1.75,70.0,cycling,0,0,0 -321163659,Qi Zhou,CHN,male,1996-01-16,2.17,95.0,basketball,0,0,0 -17611557,Qian Chen,CHN,female,1987-01-14,1.63,54.0,modern pentathlon,0,0,0 -441490855,Qian Li,POL,female,1986-07-30,1.6,54.0,table tennis,0,0,0 -497917027,Qian Li,CHN,female,1990-06-06,1.8,,boxing,0,0,1 -889734754,Qian Ren,CHN,female,2001-02-20,1.62,49.0,aquatics,1,0,0 -517073047,Qian Yu,CHN,female,1992-03-25,1.66,55.0,hockey,0,0,0 -596026558,Qiang Li,CHN,male,1989-01-04,1.86,84.0,canoe,0,0,0 -725478524,Qiang Meng,CHN,male,1987-07-03,1.86,122.0,wrestling,0,0,0 -521823425,Qiang Pan,CHN,male,1985-01-21,1.85,85.0,shooting,0,0,0 -58421484,Qiang Wang,CHN,female,1992-01-14,1.72,60.0,tennis,0,0,0 -67441417,Qiangbing Li,AUT,female,1985-04-30,1.66,49.0,table tennis,0,0,0 -212028981,Qianxun Hu,CHN,male,1987-09-18,1.78,,boxing,0,0,0 -657089417,Qibin Zhang,CHN,male,1994-06-23,1.85,85.0,aquatics,0,0,0 -143319125,Qifeng Pu,CHN,male,1986-01-03,1.84,80.0,shooting,0,0,0 -114459374,Qinan Zhu,CHN,male,1984-11-15,1.83,75.0,shooting,0,0,0 -509966707,Qing Ma,CHN,female,1992-08-24,1.74,68.0,canoe,0,0,0 -673843844,Qingling Song,CHN,female,1986-07-22,1.74,69.0,hockey,0,0,0 -119828579,Qingquan Long,CHN,male,1990-12-03,1.5,56.0,weightlifting,1,0,0 -461528297,Qiong Wu,CHN,female,1989-04-28,1.67,59.0,hockey,0,0,0 -171553885,Qiqi Yuan,CHN,female,1995-10-26,1.55,50.0,athletics,0,0,0 -677460608,Qiuxia Cui,CHN,female,1990-09-11,1.66,63.0,hockey,0,0,0 -207837009,Qiuyue Wei,CHN,female,1988-09-26,1.82,65.0,volleyball,1,0,0 -35437978,Quadri Aruna,NGR,male,1988-08-09,1.8,81.0,table tennis,0,0,0 -612840820,Quedjau Nhabali,UKR,male,1990-07-08,1.87,90.0,judo,0,0,0 -385666002,Quentin Rew,NZL,male,1984-07-16,1.74,60.0,athletics,0,0,0 -629107636,Quinta Steenbergen,NED,female,1985-04-02,1.89,75.0,volleyball,0,0,0 -607332373,Quoc Cuong Tran,VIE,male,1974-07-27,1.7,68.0,shooting,0,0,0 -910962257,R. Mohan Kumar,IND,male,1996-12-14,,,athletics,0,0,0 -443066075,R.m. Sumeda Ranasinghe,SRI,male,1991-02-10,1.82,82.0,athletics,0,0,0 -172012231,Rababe Arafi,MAR,female,1991-01-12,1.67,52.0,athletics,0,0,0 -465159345,Rabah Yousif,GBR,male,1986-12-11,1.86,74.0,athletics,0,0,0 -437311401,Rabia Guelec,GER,female,1994-06-05,1.75,62.0,taekwondo,0,0,0 -775207992,Race Imboden,USA,male,1993-04-17,1.86,74.0,fencing,0,0,1 -667595731,Rachael Adams,USA,female,1990-06-03,1.88,81.0,volleyball,0,0,1 -348723574,Rachael Lynch,AUS,female,1986-07-02,1.79,68.0,hockey,0,0,0 -519575300,Rachael Mbogo,KEN,female,1982-12-20,1.7,64.0,rugby sevens,0,0,0 -95839942,Rachel Cawthorn,GBR,female,1988-11-03,1.77,70.0,canoe,0,0,0 -728971691,Rachel Dawson,USA,female,1985-08-02,1.78,68.0,hockey,0,0,0 -305014449,Rachel Fattal,USA,female,1993-12-10,1.73,65.0,aquatics,1,0,0 -559172027,Rachel Jarry,AUS,female,1991-12-06,1.86,77.0,basketball,0,0,0 -676779702,Rachel Klamer,NED,female,1990-10-08,1.66,51.0,triathlon,0,0,0 -844090644,Rachel Neylan,AUS,female,1982-03-09,1.69,52.0,cycling,0,0,0 -935732603,Rachel Nicol,CAN,female,1993-02-16,1.6,61.0,aquatics,0,0,0 -646854550,Rachel Tallent,AUS,female,1993-02-20,1.67,54.0,athletics,0,0,0 -240097025,Rachele Bruni,ITA,female,1990-11-04,1.7,59.0,aquatics,0,1,0 -487277130,Rachid Ait-Atmane,ALG,male,1993-02-04,1.85,75.0,football,0,0,0 -284352083,Rachid Kisri,MAR,male,1975-08-02,1.79,60.0,athletics,0,0,0 -449827888,Rachid Sidibe,BUR,male,1990-12-02,1.77,119.0,judo,0,0,0 -644175866,Racquel Sheath,NZL,female,1994-11-27,1.65,57.0,cycling,0,0,0 -937396787,Radek Juska,CZE,male,1993-03-08,1.94,84.0,athletics,0,0,0 -627998847,Radek Stepanek,CZE,male,1978-11-27,1.86,78.0,tennis,0,0,1 -577467717,Radhouane Chebbi,TUN,male,1985-08-08,1.74,125.0,wrestling,0,0,0 -937210805,Radik Isaev,AZE,male,1989-09-26,2.0,93.0,taekwondo,1,0,0 -644415749,Radmila Petrovic,MNE,female,1988-04-19,1.75,60.0,handball,0,0,0 -81383453,Radomyos Matjiur,THA,male,1988-04-02,1.82,82.0,aquatics,0,0,0 -440186734,Radoslava Mavrodieva,BUL,female,1987-03-13,1.76,94.0,athletics,0,0,0 -850134713,Radoslaw Baran,POL,male,1989-11-05,1.77,97.0,wrestling,0,0,0 -581644071,Radoslaw Kawecki,POL,male,1991-08-16,1.86,77.0,aquatics,0,0,0 -997901130,Radu Albot,MDA,male,1989-11-11,1.75,70.0,tennis,0,0,0 -457575476,Rafael Alcantara,BRA,male,1993-02-12,1.74,65.0,football,1,0,0 -825109201,Rafael Andrade,BRA,male,1986-05-07,1.68,,gymnastics,0,0,0 -724962293,Rafael Antonio Lacayo Paladino,NCA,male,1998-11-19,1.78,59.0,shooting,0,0,0 -730338405,Rafael Buzacarini,BRA,male,1991-10-06,1.83,100.0,judo,0,0,0 -731749263,Rafael Cabrera Bello,ESP,male,1984-05-25,1.87,83.0,golf,0,0,0 -930296757,Rafael Capote,QAT,male,1987-10-05,1.98,106.0,handball,0,0,0 -632477118,Rafael Castillo,CUB,male,1993-08-12,2.02,87.0,taekwondo,0,0,0 -558313217,Rafael Hettsheimeir,BRA,male,1986-06-16,2.08,120.0,basketball,0,0,0 -125913109,Rafael Luz,BRA,male,1992-02-11,1.88,95.0,basketball,0,0,0 -845065634,Rafael Nadal,ESP,male,1986-06-03,1.85,79.0,tennis,1,0,0 -691523372,Rafael Quintero,PUR,male,1994-07-24,1.8,68.0,aquatics,0,0,0 -501691381,Rafael Silva,BRA,male,1987-05-11,2.03,160.0,judo,0,0,1 -361799297,Rafaela Silva,BRA,female,1992-04-24,1.69,57.0,judo,1,0,0 -98299217,Rafaelle,BRA,female,1991-06-18,1.75,67.0,football,0,0,0 -155018393,Rafal Augustyn,POL,male,1984-05-14,1.8,82.0,athletics,0,0,0 -178643168,Rafal Buszek,POL,male,1987-04-28,1.94,81.0,volleyball,0,0,0 -290922761,Rafal Fedaczynski,POL,male,1980-12-03,1.68,58.0,athletics,0,0,0 -888503746,Rafal Majka,POL,male,1989-09-12,1.73,61.0,cycling,0,0,1 -740945281,Rafal Omelko,POL,male,1989-01-16,1.94,82.0,athletics,0,0,0 -986457519,Rafal Rosolski,POL,male,1991-05-27,1.88,86.0,canoe,0,0,0 -650690707,Rafal Sarnecki,POL,male,1990-01-08,1.75,81.0,cycling,0,0,0 -397809896,Rafith Rodriguez,COL,male,1989-06-01,1.9,75.0,athletics,0,0,0 -607055937,Ragab Abdalla,EGY,male,1991-03-04,1.7,93.0,weightlifting,0,0,0 -546687693,Raghunath Vokkaliga,IND,male,1988-11-01,1.79,89.0,hockey,0,0,0 -913614600,Ragna Agerup,NOR,female,1995-06-22,,,sailing,0,0,0 -470122397,Rahel Fseha Gebresilassie,ETH,female,1995-11-03,1.6,55.0,aquatics,0,0,0 -215186405,Raheleh Asemani,BEL,female,1989-06-21,1.71,59.0,taekwondo,0,0,0 -932824514,Rahma Ben Ali,TUN,female,1993-09-15,1.59,55.0,taekwondo,0,0,0 -680456945,Raiber Jose Rodriguez Orozco,VEN,male,1990-12-28,1.68,69.0,wrestling,0,0,0 -296185766,Raijieli Daveua,FIJ,female,1992-05-30,,69.0,rugby sevens,0,0,0 -711937461,Raissa Nasser,CMR,female,1994-08-19,1.73,73.0,volleyball,0,0,0 -608420333,Raiza Goulao-Henrique,BRA,female,1991-02-28,1.68,56.0,cycling,0,0,0 -425162621,Rajani Etimarpu,IND,female,1990-06-09,1.72,63.0,hockey,0,0,0 -818043080,Rajeev Ram,USA,male,1984-03-18,,,tennis,0,1,0 -465151965,Rajiv Ouseph,GBR,male,1986-08-30,1.91,,badminton,0,0,0 -364210839,Ralf Buchheim,GER,male,1983-10-10,1.83,72.0,shooting,0,0,0 -907089617,Ralph Goveia Assafrao,ZAM,male,1996-03-08,1.87,77.0,aquatics,0,0,0 -717986475,Raluca Olaru,ROU,female,1989-03-03,1.74,63.0,tennis,0,0,0 -168597162,Ramadan Darwish,EGY,male,1988-01-29,1.88,100.0,judo,0,0,0 -781913285,Ramandeep Singh,IND,male,1993-04-01,1.79,70.0,hockey,0,0,0 -186916471,Rami Anis,ROT,male,1991-03-18,1.78,78.0,aquatics,0,0,0 -554313909,Rami Antero Hietaniemi,FIN,male,1982-12-28,1.8,92.0,wrestling,0,0,0 -448967973,Ramil Guliyev,TUR,male,1990-05-29,1.86,80.0,athletics,0,0,0 -171315396,Ramiro Quintana,ARG,male,1977-03-07,1.72,70.0,equestrian,0,0,0 -411617493,Ramon Gittens,BAR,male,1987-07-20,1.8,79.0,athletics,0,0,0 -700638959,Ramon Pileta,HON,male,1977-03-20,1.85,130.0,judo,0,0,0 -115217179,Ramona Papaioannou,CYP,female,1989-06-15,1.7,63.0,athletics,0,0,0 -605985287,Ramu Tokashiki,JPN,female,1991-06-11,1.91,85.0,basketball,0,0,0 -655303301,Ramunas Navardauskas,LTU,male,1988-01-30,1.9,78.0,cycling,0,0,0 -479803559,Ran Sui,CHN,male,1992-06-25,1.93,96.0,basketball,0,0,0 -495175841,Rana Nakano,JPN,female,1997-09-10,1.57,43.0,gymnastics,0,0,0 -177719798,Randy Leru,CUB,male,1995-11-07,1.73,72.0,gymnastics,0,0,0 -512106785,Rani,IND,female,1994-12-04,1.6,60.0,hockey,0,0,0 -957169237,Ranokhon Amanova,UZB,female,1994-03-08,1.74,62.0,aquatics,0,0,0 -332379601,Ranomi Kromowidjojo,NED,female,1990-08-20,1.8,72.0,aquatics,0,0,0 -738350082,Raphael Gagne,CAN,male,1987-07-16,1.73,67.0,cycling,0,0,0 -160405615,Raphael Marcel Holzdeppe,GER,male,1989-09-28,1.83,78.0,athletics,0,0,0 -470376574,Raphael Stacchiotti,LUX,male,1992-03-09,1.83,80.0,aquatics,0,0,0 -57685762,Raquel Fernandes,BRA,female,1991-03-21,1.6,56.0,football,0,0,0 -953499881,Raquel Gonzalez,ESP,female,1989-11-16,1.68,58.0,athletics,0,0,0 -254376988,Raquel Kochhann,BRA,female,1992-10-06,1.72,70.0,rugby sevens,0,0,0 -930359048,Rasa Drazdauskaite,LTU,female,1981-03-20,1.73,58.0,athletics,0,0,0 -718297229,Rashid Hamad,QAT,male,1987-10-18,1.68,85.0,shooting,0,0,0 -15159824,Rashid Yunusmetov,KAZ,male,1979-07-09,1.8,100.0,shooting,0,0,0 -223728241,Rasmus Christian Quaade,DEN,male,1990-01-07,1.87,77.0,cycling,0,0,1 -258311001,Rasmus Magi,EST,male,1992-05-04,1.87,77.0,athletics,0,0,0 -950792939,Rasmus Quist,DEN,male,1980-04-05,1.73,72.0,rowing,0,0,0 -86534695,Rasul Chunayev,AZE,male,1991-01-07,1.71,66.0,wrestling,0,0,1 -553576191,Ratchanok Intanon,THA,female,1995-02-05,1.69,59.0,badminton,0,0,0 -682664911,Ratu Nakalevu,FIJ,male,1994-03-07,1.7,73.0,football,0,0,0 -722317723,Ratu Waranaivalu,FIJ,male,1995-09-16,1.75,72.0,football,0,0,0 -595425086,Raul Curiel,MEX,male,1995-12-06,1.77,,boxing,0,0,0 -50353432,Raul Hernandez Hidalgo,CUB,male,1992-09-22,1.9,71.0,rowing,0,0,0 -76349078,Raul Lopez,MEX,male,1993-02-22,1.84,74.0,football,0,0,0 -776415123,Raul Machacuay,PER,male,1983-02-18,1.65,57.0,athletics,0,0,0 -720271360,Raul Must,EST,male,1987-11-09,1.76,70.0,badminton,0,0,0 -728492983,Raul Pacheco,PER,male,1979-04-26,1.67,55.0,athletics,0,0,0 -788112409,Raulzinho Neto,BRA,male,1992-05-19,1.92,82.0,basketball,0,0,0 -874212245,Raven Saunders,USA,female,1996-05-15,1.66,108.0,athletics,0,0,0 -412234699,Ravinder Khatri,IND,male,1992-05-15,1.68,85.0,wrestling,0,0,0 -717317078,Ray Bassil,LIB,female,1988-10-20,1.75,65.0,shooting,0,0,0 -920376731,Rayderley Miguel Zapata,ESP,male,1993-05-26,1.69,71.0,gymnastics,0,0,0 -479953072,Raymond Kibet,KEN,male,1996-02-04,1.85,64.0,athletics,0,0,0 -781144193,Raymond Ovinou,PNG,male,1984-09-06,1.69,68.0,judo,0,0,0 -677348878,Rayssa Costa,BRA,female,1991-03-16,1.76,63.0,fencing,0,0,0 -372384369,Rayton Nduku Okwiri,KEN,male,1986-03-26,1.8,,boxing,0,0,0 -707114327,Rebeca Andrade,BRA,female,1999-05-08,1.45,45.0,gymnastics,0,0,0 -35093364,Rebeca Quinteros Ortiz,ESA,female,1997-08-28,,,aquatics,0,0,0 -130438348,Rebecca Downie,GBR,female,1992-01-24,1.56,54.0,gymnastics,0,0,0 -698057200,Rebecca Gallantree,GBR,female,1984-08-19,1.66,61.0,aquatics,0,0,0 -109265734,Rebecca Henderson,AUS,female,1991-09-27,1.58,49.0,cycling,0,0,0 -437939980,Rebecca Heyliger,BER,female,1992-11-24,1.61,54.0,aquatics,0,0,0 -708254383,Rebecca Howard,CAN,female,1979-05-09,1.62,70.0,equestrian,0,0,0 -360632507,Rebecca James,GBR,female,1991-11-29,1.71,66.0,cycling,0,2,0 -564979166,Rebecca Ndolo Muambo,CMR,female,1985-07-16,1.52,48.0,wrestling,0,0,0 -711142045,Rebecca Quinn,CAN,female,1995-08-11,1.76,66.0,football,0,0,1 -253747465,Rebecca Rolls,NZL,female,1975-08-22,1.78,72.0,football,0,0,0 -108904189,Rebecca Scown,NZL,female,1983-08-10,1.78,70.0,rowing,0,1,0 -140526328,Rebecca Tavo,FIJ,female,1983-03-23,1.72,75.0,rugby sevens,0,0,0 -890871290,Rebeka Koha,LAT,female,1998-05-19,1.57,52.0,weightlifting,0,0,0 -20518421,Rebekah Stott,NZL,female,1993-06-17,1.72,63.0,football,0,0,0 -841170860,Rebekah Tiler,GBR,female,1999-01-13,1.63,69.0,weightlifting,0,0,0 -98483508,Rebekka Haase,GER,female,1993-01-02,1.7,58.0,athletics,0,0,0 -795341579,Rebii Simon,GBR,female,1996-07-06,1.75,70.0,canoe,0,0,0 -409979850,Rechael Tonjor,NGR,female,1991-10-14,1.68,57.0,aquatics,0,0,0 -874150353,Reda Benbaziz,ALG,male,1993-09-05,1.85,,boxing,0,0,0 -873696755,Redouane Cherifi,ALG,male,1993-02-22,1.8,70.0,football,0,0,0 -684887323,Reem Mansour,EGY,female,1993-12-20,1.69,76.0,archery,0,0,0 -415954257,Reem Mohamed Hussein Elsayed kassem,EGY,female,1995-10-01,1.75,75.0,aquatics,0,0,0 -825382404,Refiloe Jane,RSA,female,1992-08-04,1.59,55.0,football,0,0,0 -437714118,Regan Gough,NZL,male,1996-10-06,1.83,71.0,cycling,0,0,0 -578826400,Regan Lamble,AUS,female,1991-10-14,1.74,55.0,athletics,0,0,0 -820273693,Regan Ware,NZL,male,1994-08-07,1.84,93.0,rugby sevens,0,0,0 -530301493,Regine Tugade,GUM,female,1998-01-28,1.65,50.0,athletics,0,0,0 -503502071,Rei Higuchi,JPN,male,1996-01-28,1.63,63.0,wrestling,0,1,0 -487797823,Reid Coolsaet,CAN,male,1979-07-29,1.73,62.0,athletics,0,0,0 -634606811,Reidis Ramos,CUB,male,1996-07-21,1.72,76.0,athletics,0,0,0 -102628929,Reiko Takeda,JPN,female,1984-12-14,1.51,48.0,equestrian,0,0,0 -728475978,Rein Taaramae,EST,male,1987-04-24,1.85,75.0,cycling,0,0,0 -710574826,Reina-Flor Okori,GEQ,female,1980-05-02,1.63,56.0,athletics,0,0,0 -705591992,Reinder Nummerdor,NED,male,1976-09-10,1.94,87.0,volleyball,0,0,0 -863462189,Reineris Salas Perez,CUB,male,1987-03-17,1.8,86.0,wrestling,0,0,0 -315497786,Reinier Estpinan,CUB,male,1982-12-05,1.7,72.0,shooting,0,0,0 -133025640,Reinier Rojas Cohimbra,CUB,male,1986-07-31,1.9,78.0,volleyball,0,0,0 -189944782,Reinier Torres,CUB,male,1990-02-15,1.82,85.0,canoe,0,0,0 -19704992,Rejoice Kapfumvuti,ZIM,female,1991-11-18,1.6,53.0,football,0,0,0 -165032675,Reka Gyorgy,HUN,female,1996-05-25,1.76,63.0,aquatics,0,0,0 -263751358,Reka-Luca Jani,HUN,female,1991-07-31,1.6,57.0,tennis,0,0,0 -776117067,Remi Garsau,FRA,male,1984-07-19,1.9,82.0,aquatics,0,0,0 -746576007,Remi Saudadier,FRA,male,1986-03-20,1.98,100.0,aquatics,0,0,0 -545506950,Remigijus Kancys,LTU,male,1987-07-17,1.73,62.0,athletics,0,0,0 -228952671,Renaldas Seibutis,LTU,male,1985-07-23,1.96,83.0,basketball,0,0,0 -390436911,Renat Saidov,RUS,male,1988-09-27,2.1,101.0,judo,0,0,0 -456145597,Renata Tobai Sike,HUN,female,1978-07-10,1.8,67.0,shooting,0,0,0 -736324919,Renato Augusto,BRA,male,1988-02-08,1.86,86.0,football,1,0,0 -897045693,Renato Portella,BRA,male,1962-12-05,1.65,74.0,shooting,0,0,0 -466194153,Renato Rezende,BRA,male,1991-02-28,1.71,72.0,cycling,0,0,0 -59908931,Renaud Lavillenie,FRA,male,1986-09-18,1.77,71.0,athletics,0,1,0 -394581018,Rene Enders,GER,male,1987-02-13,1.65,77.0,cycling,0,0,0 -608905335,Rene Holten,DEN,male,1988-11-28,1.88,92.0,canoe,0,0,0 -873459751,Rene Lopez,COL,male,1964-04-05,1.68,70.0,equestrian,0,0,0 -94618006,Rene Philippe Kouassi,CIV,male,1979-12-14,1.9,83.0,archery,0,0,0 -861585492,Rene Pranz,AUT,male,1985-09-04,1.84,79.0,fencing,0,0,0 -57264316,Rene Tebbel,UKR,male,1969-02-12,1.82,84.0,equestrian,0,0,0 -400301052,Rene Toft Hansen,DEN,male,1984-11-01,2.0,105.0,handball,1,0,0 -109399064,Renee Eykens,BEL,female,1996-06-08,1.7,54.0,athletics,0,0,0 -73182018,Renelle Lamote,FRA,female,1993-12-26,1.65,59.0,athletics,0,0,0 -18503067,Reneta Kamberova,BUL,female,1990-09-12,1.7,53.0,gymnastics,0,0,1 -499299966,Renick James,BIZ,male,1987-08-21,1.8,91.0,judo,0,0,0 -119864566,Renjith Maheswary,IND,male,1986-01-30,1.84,79.0,athletics,0,0,0 -563285077,Renny Quow,TTO,male,1987-08-25,1.6,77.0,athletics,0,0,0 -154797267,Renuka Yadav,IND,female,1994-07-18,1.59,53.0,hockey,0,0,0 -642982452,Renxue Zhu,CHN,male,1991-04-06,1.7,50.0,athletics,0,0,0 -859598308,Renzo Agresta,BRA,male,1985-06-27,1.81,76.0,fencing,0,0,0 -188249064,Renzo Leon Garcia,PER,male,1990-08-14,1.86,71.0,rowing,0,0,0 -217093800,Renzo Tjon-A-Joe,SUR,male,1995-07-08,1.9,78.0,aquatics,0,0,0 -63732121,Repo Malepe,RSA,male,1997-02-18,1.79,65.0,football,0,0,0 -867912323,Reshmie Shari Oogink,NED,female,1989-10-26,1.79,72.0,taekwondo,0,0,0 -280064758,Revazi Nadareishvili,GEO,male,1991-06-21,1.76,98.0,wrestling,0,0,0 -248931578,Rexford Tullius,ISV,male,1987-03-10,,,aquatics,0,0,0 -671136449,Reyare Thomas,TTO,female,1987-11-23,1.69,59.0,athletics,0,0,0 -826747874,Reynier Mena,CUB,male,1996-11-21,1.74,79.0,athletics,0,0,0 -686527217,Reza Ghasemi,IRI,male,1987-07-24,1.82,69.0,athletics,0,0,0 -137914262,Reza Mohammad Ali Yazdani,IRI,male,1984-08-25,1.72,100.0,wrestling,0,0,0 -522982610,Rhian Wilkinson,CAN,female,1982-05-12,1.66,69.0,football,0,0,1 -317198923,Rhydian Cowley,AUS,male,1991-01-04,1.81,65.0,athletics,0,0,0 -264189277,Rhys Grant,AUS,male,1987-02-06,1.88,88.0,rowing,0,0,0 -768753135,Rhys Howden,AUS,male,1987-04-02,1.89,84.0,aquatics,0,0,0 -546440532,Ria Percival,NZL,female,1989-12-07,1.62,57.0,football,0,0,0 -558739071,Riau Ega Agatha,INA,male,1991-10-25,1.75,70.0,archery,0,0,0 -238394812,Ricard Alarcon Tevar,ESP,male,1991-08-18,1.88,110.0,aquatics,0,0,0 -898620184,Ricardas Berankis,LTU,male,1990-06-21,1.75,74.0,tennis,0,0,0 -56369474,Ricardas Nekriosius,LTU,male,1986-09-12,1.84,88.0,canoe,0,0,0 -458070483,Ricardo Esgaio,POR,male,1993-05-16,1.75,80.0,football,0,0,0 -975400260,Ricardo Gouveia,POR,male,1991-08-06,1.75,88.0,golf,0,0,0 -279428980,Ricardo Lucarelli,BRA,male,1992-02-14,1.95,79.0,volleyball,1,0,0 -372426764,Ricardo Ramos,MEX,male,1985-12-05,1.72,56.0,athletics,0,0,0 -385094917,Ricardo Ribas,POR,male,1977-10-08,1.74,63.0,athletics,0,0,0 -923708870,Ricardo Santos,BRA,male,1980-05-08,1.85,73.0,sailing,0,0,0 -711718943,Ricardo Soto,CHI,male,1999-10-22,1.83,88.0,archery,0,0,0 -308968107,Ricardo Vargas Jacobo,MEX,male,1997-11-21,1.8,79.0,aquatics,0,0,0 -820785891,Ricardo de Souza,BRA,male,1994-09-21,1.75,70.0,athletics,0,0,0 -281848184,Riccardo Mazzetti,ITA,male,1984-05-02,1.81,77.0,shooting,0,0,0 -96823281,Riccardo de Luca,ITA,male,1986-03-22,1.87,80.0,modern pentathlon,0,0,0 -558439036,Richard Bohus,HUN,male,1993-04-09,1.85,80.0,aquatics,0,0,0 -509225287,Richard Chambers,GBR,male,1985-06-10,1.83,74.0,rowing,0,0,0 -385235592,Richard Hildreth,CAN,male,1984-06-03,1.83,80.0,hockey,0,0,0 -817106482,Richard Hounslow,GBR,male,1981-12-19,1.83,78.0,canoe,0,1,0 -715219943,Richard John Edward Patterson,NZL,male,1983-04-30,1.67,84.0,weightlifting,0,0,0 -180069208,Richard Kilty,GBR,male,1989-09-02,1.82,82.0,athletics,0,0,0 -232429024,Richard Kruse,GBR,male,1983-07-30,1.9,84.0,fencing,0,0,0 -947093935,Richard Merjan,LIB,male,1988-11-10,1.6,57.0,canoe,0,0,0 -158446186,Richard Murray,RSA,male,1989-01-04,1.8,70.0,triathlon,0,0,0 -539128451,Richard Nagy,SVK,male,1993-03-09,1.88,80.0,aquatics,0,0,0 -289690027,Richard Ringer,GER,male,1989-02-27,1.82,62.0,athletics,0,0,0 -16165612,Richard Schmidt,GER,male,1987-05-23,1.73,76.0,rowing,0,1,0 -434798426,Richard Thompson,TTO,male,1985-07-06,1.88,80.0,athletics,0,0,0 -45434968,Richard Varga,SVK,male,1989-01-28,1.88,65.0,triathlon,0,0,0 -50122257,Richard Vargas,VEN,male,1994-12-28,1.75,58.0,athletics,0,0,0 -545591237,Richard Weinberger,CAN,male,1990-06-07,1.84,72.0,aquatics,0,0,0 -582543706,Richardson Hitchins,HAI,male,1997-09-26,,,boxing,0,0,0 -153689574,Richelle Stephens,USA,female,1996-07-22,1.68,65.0,rugby sevens,0,0,0 -972776916,Richer Perez,CUB,male,1986-02-20,1.63,58.0,athletics,0,0,0 -8241548,Richie Campbell,AUS,male,1987-09-18,1.93,99.0,aquatics,0,0,0 -145755728,Richie Porte,AUS,male,1985-01-30,1.72,62.0,cycling,0,0,0 -553041631,Richson Simeon,MHL,male,1997-10-05,1.55,78.0,athletics,0,0,0 -182264140,Rick Yves Confiance,SEY,male,1994-05-24,1.65,62.0,weightlifting,0,0,0 -52657161,Rick van der Ven,NED,male,1991-04-14,1.81,76.0,archery,0,0,0 -896354114,Rickie Fowler,USA,male,1988-12-13,1.76,68.0,golf,0,0,0 -664274714,Ricky Robertson,USA,male,1990-09-19,1.81,74.0,athletics,0,0,0 -184879090,Ricky Rubio,ESP,male,1990-10-21,1.93,82.0,basketball,0,0,1 -29620677,Rico Freimuth,GER,male,1988-03-14,1.96,95.0,athletics,0,0,0 -814315679,Rie Kaneto,JPN,female,1988-09-08,1.75,64.0,aquatics,1,0,0 -766598389,Rie Matsubara,JPN,female,1993-10-21,1.67,48.0,gymnastics,0,0,0 -155731349,Rieko Ioane,NZL,male,1997-03-18,1.9,105.0,rugby sevens,0,0,0 -989632205,Rigoberto Uran Uran,COL,male,1987-01-26,1.71,63.0,cycling,0,0,0 -304856558,Riheb Hammami,TUN,female,1989-06-04,1.7,68.0,sailing,0,0,0 -765122395,Rikako Ikee,JPN,female,2000-07-04,1.7,58.0,aquatics,0,0,0 -137154946,Riki Harakawa,JPN,male,1993-08-18,1.75,72.0,football,0,0,0 -62001472,Rikke Moller Pedersen,DEN,female,1989-01-09,1.74,61.0,aquatics,0,0,1 -485889936,Riley Fitzsimmons,AUS,male,1996-07-27,1.92,93.0,canoe,0,0,0 -646643525,Rima Kashafutdinova,KAZ,female,1995-07-24,1.6,48.0,athletics,0,0,0 -968504345,Rio Watari,JPN,female,1991-09-19,1.63,69.0,wrestling,0,0,0 -162020781,Risako Kawai,JPN,female,1994-11-21,1.6,61.0,wrestling,1,0,0 -518813071,Risako Mitsui,JPN,female,1993-09-23,1.68,56.0,aquatics,0,0,2 -54528524,Rishod Sobirov,UZB,male,1986-09-11,1.68,66.0,judo,0,0,1 -13338257,Ristananna Tracey,JAM,female,1992-05-09,1.78,66.0,athletics,0,0,0 -875138025,Riste Pandev,MKD,male,1994-01-25,1.78,68.0,athletics,0,0,0 -169332497,Risto Matas,EST,male,1984-04-30,1.89,94.0,athletics,0,0,0 -226090486,Rita Keszthelyi,HUN,female,1991-12-10,1.78,67.0,aquatics,0,0,0 -977877235,Rita Recsei,HUN,female,1996-01-30,1.68,54.0,athletics,0,0,0 -427379114,Rita Zeqiri,KOS,female,1995-12-08,1.65,55.0,aquatics,0,0,0 -463062176,Rivaldo Coetzee,RSA,male,1996-10-16,1.83,73.0,football,0,0,0 -950284932,Riwei Wang,CHN,male,1993-03-21,1.82,85.0,canoe,0,0,0 -536544725,Riza Kayaalp,TUR,male,1989-10-10,1.82,130.0,wrestling,0,1,0 -899335116,Rizlen Zouak,MAR,female,1986-05-14,1.64,63.0,judo,0,0,0 -578088761,Ro Dakuwaqa,FIJ,male,1994-02-14,1.9,105.0,rugby sevens,1,0,0 -854121144,Rob Mullett,GBR,male,1987-07-31,1.78,64.0,athletics,0,0,0 -753125163,Robbert Kemperman,NED,male,1990-06-24,1.86,76.0,hockey,0,0,0 -57185186,Robbie Renwick,GBR,male,1988-07-21,1.84,80.0,aquatics,0,1,0 -955978928,Robby Andrews,USA,male,1991-03-29,1.78,65.0,athletics,0,0,0 -58169707,Robeilys Peinado,VEN,female,1997-11-26,,,athletics,0,0,0 -921399403,Robeisy Ramirez,CUB,male,1993-12-20,1.62,,boxing,1,0,0 -651154983,Robel Kiros Habte,ETH,male,1992-04-13,1.76,81.0,aquatics,0,0,0 -371852297,Robenilson de Jesus,BRA,male,1987-09-24,1.66,,boxing,0,0,0 -484252219,Robert Baran,POL,male,1992-07-03,1.86,103.0,wrestling,0,0,0 -793414933,Robert Bauer,GER,male,1995-04-09,1.83,76.0,football,0,1,0 -136649712,Robert Elder,FIJ,male,1981-04-25,1.94,115.0,archery,0,0,0 -453661639,Robert Farah,COL,male,1987-01-20,1.93,92.0,tennis,0,0,0 -65516021,Robert Fuchs,POL,male,1991-06-12,2.09,105.0,rowing,0,0,0 -69277111,Robert Gardos,AUT,male,1979-01-16,1.78,72.0,table tennis,0,0,0 -588439110,Robert Glinta,ROU,male,1997-04-18,1.85,72.0,aquatics,0,0,0 -957342784,Robert Grabarz,GBR,male,1987-10-03,1.91,81.0,athletics,0,0,0 -748479187,Robert Harting,GER,male,1984-10-18,2.01,126.0,athletics,0,0,0 -126498342,Robert Heffernan,IRL,male,1978-02-28,1.71,58.0,athletics,0,0,0 -401609569,Robert Hering,GER,male,1990-06-14,1.8,78.0,athletics,0,0,0 -740457326,Robert Luecken,NED,male,1985-04-30,1.99,95.0,rowing,0,0,1 -99198444,Robert Manson,NZL,male,1989-10-11,1.88,89.0,rowing,0,0,0 -651788735,Robert Mateusiak,POL,male,1976-01-13,1.69,65.0,badminton,0,0,0 -650448560,Robert Meeuwsen,NED,male,1988-03-21,2.07,94.0,volleyball,0,0,1 -222647716,Robert Micael Gibson,CAN,male,1986-02-02,1.95,101.0,rowing,0,0,0 -653323381,Robert Mike,HUN,male,1984-05-08,1.76,80.0,canoe,0,0,0 -892281190,Robert Munn,USA,male,1990-07-26,1.94,97.0,rowing,0,0,0 -595627689,Robert Paez,VEN,male,1994-06-01,1.74,72.0,aquatics,0,0,0 -455331641,Robert Renner,SLO,male,1994-03-08,1.83,75.0,athletics,0,0,0 -503602363,Robert Scheidt,BRA,male,1973-04-15,1.88,76.0,sailing,0,0,0 -720405492,Robert Skov,DEN,male,1996-05-20,1.83,78.0,football,0,0,0 -853386777,Robert Sobera,POL,male,1991-01-19,1.91,86.0,athletics,0,0,0 -532564803,Robert Timothy David Smith,USA,male,1987-01-30,1.83,127.0,wrestling,0,0,0 -360028984,Robert Tvorogal,LTU,male,1994-10-05,1.67,65.0,gymnastics,0,0,0 -859612210,Robert Urbanek,POL,male,1987-04-29,2.0,122.0,athletics,0,0,0 -959745173,Robert Zbogar,SLO,male,1989-03-06,1.83,72.0,aquatics,0,0,0 -810837214,Robert van der Horst,NED,male,1984-10-17,1.79,74.0,hockey,0,0,0 -146983428,Roberta Bianconi,ITA,female,1989-07-08,1.76,76.0,aquatics,0,1,0 -755354493,Roberta Vinci,ITA,female,1983-02-18,1.64,54.0,tennis,0,0,0 -954509397,Robertas Javtokas,LTU,male,1980-03-20,2.1,117.0,basketball,0,0,0 -722113054,Roberti Kobliashvili,GEO,male,1993-12-06,1.8,85.0,wrestling,0,0,0 -979738461,Roberto Acuna,ARG,male,1990-09-14,2.08,109.0,basketball,0,0,0 -758461623,Roberto Bautista Agut,ESP,male,1988-04-14,1.83,76.0,tennis,0,0,0 -21769735,Roberto Janet,CUB,male,1986-08-29,1.87,106.0,athletics,0,0,0 -340569096,Roberto Maehler,BRA,male,1985-01-25,1.83,82.0,canoe,0,0,0 -555000042,Roberto Sawyers,CRC,male,1986-10-17,1.82,111.0,athletics,0,0,0 -509328493,Roberto Schmits,BRA,male,1969-02-04,1.87,90.0,shooting,0,0,0 -930615320,Roberto Skyers,CUB,male,1991-11-12,1.83,83.0,athletics,0,0,0 -819268120,Robin Erewa,GER,male,1991-06-24,1.84,80.0,athletics,0,0,0 -577679695,Robin Haase,NED,male,1987-04-06,1.9,77.0,tennis,0,0,0 -982057422,Robin Middleton,AUS,male,1985-02-08,1.88,85.0,badminton,0,0,0 -444302683,Robin Neumann,NED,female,1997-12-12,1.73,63.0,aquatics,0,0,0 -932757600,Robin Pacek,SWE,male,1991-03-17,1.81,81.0,judo,0,0,0 -860901297,Robin Prendes,USA,male,1988-12-13,1.86,72.0,rowing,0,0,0 -205241654,Robin Quaison,SWE,male,1993-10-09,1.82,78.0,football,0,0,0 -569702101,Robin Ramaekers,BEL,male,1994-10-26,1.8,78.0,archery,0,0,0 -196394925,Robin Seidl,AUT,male,1990-01-21,1.9,80.0,volleyball,0,0,0 -771525167,Robin Vanderbemden,BEL,male,1994-02-10,1.83,74.0,athletics,0,0,0 -583096662,Robin de Kruijf,NED,female,1991-05-05,1.92,80.0,volleyball,0,0,0 -938744484,Robson Conceicao,BRA,male,1988-10-25,1.75,,boxing,1,0,0 -713083048,Robyn Moodaly,RSA,female,1994-06-16,1.62,49.0,football,0,0,0 -412115856,Roc Oliva,ESP,male,1989-07-18,1.8,78.0,hockey,0,0,0 -994481425,Rocco van Rooyen,RSA,male,1992-12-23,1.9,98.0,athletics,0,0,0 -369012326,Rocio Campigli,ARG,female,1994-08-06,1.73,70.0,handball,0,0,0 -374524255,Rocio Comba,ARG,female,1987-07-14,1.8,100.0,athletics,0,0,0 -164940768,Rocio Gutierrez,ESP,female,1985-07-20,1.62,61.0,hockey,0,0,0 -528331372,Rocio Sanchez,ARG,female,1988-08-02,1.57,54.0,hockey,0,0,0 -885588616,Rocio Ybarra,ESP,female,1984-12-26,1.59,54.0,hockey,0,0,0 -273991849,Rodman Teltull,PLW,male,1994-01-29,1.53,64.0,athletics,0,0,0 -374596998,Rodney Govinden,SEY,male,1984-09-13,1.79,84.0,sailing,0,0,0 -625240454,Rodolfo Cazaubon,MEX,male,1989-08-15,1.78,72.0,golf,0,0,0 -562268541,Rodolfo Lombardo Ontiveros Gomez,MEX,male,1983-11-09,1.85,87.0,volleyball,0,0,0 -519800008,Rodolfo Pizarro,MEX,male,1994-02-15,1.73,64.0,football,0,0,0 -480321541,Rodrick Kuku,COD,male,1986-04-06,,66.0,judo,0,0,0 -216037265,Rodrigo Caio,BRA,male,1993-08-17,1.82,70.0,football,1,0,0 -5451063,Rodrigo Diego,MEX,male,1996-12-02,1.66,69.0,aquatics,0,0,0 -699797697,Rodrigo Dourado,BRA,male,1994-06-17,1.86,80.0,football,1,0,0 -956734649,Rodrigo Etchart,ARG,male,1994-01-24,1.8,80.0,rugby sevens,0,0,0 -817730923,Rodrigo Faustino,BRA,male,1987-01-06,1.67,69.0,hockey,0,0,0 -539471357,Rodrigo Germade,ESP,male,1990-08-23,1.83,85.0,canoe,0,0,0 -666269394,Rodrigo Gonzalez,MEX,male,1989-12-14,1.8,69.0,triathlon,0,0,0 -259323134,Rodrigo Steimbach,BRA,male,1996-01-30,1.61,58.0,hockey,0,0,0 -482861402,Roel Braas,NED,male,1987-03-11,2.0,100.0,rowing,0,0,0 -992517041,Rogen Ladon,PHI,male,1993-11-10,1.65,,boxing,0,0,0 -105565134,Roger Beresford Hudson,RSA,male,1978-03-22,1.77,72.0,sailing,0,0,0 -9609361,Roger Kluge,GER,male,1986-02-05,1.91,85.0,cycling,0,0,0 -311874637,Roger Tahull Compte,ESP,male,1997-05-11,1.95,105.0,aquatics,0,0,0 -42178820,Roger Yves Bost,FRA,male,1965-10-21,1.76,80.0,equestrian,1,0,0 -620929414,Rogerio Dutra Silva,BRA,male,1984-02-05,1.8,73.0,tennis,0,0,0 -327425474,Rogier Hofman,NED,male,1986-09-05,1.84,79.0,hockey,0,0,0 -142935806,Rohan Bopanna,IND,male,1980-03-04,1.9,85.0,tennis,0,0,0 -571443859,Rohan Dennis,AUS,male,1990-05-28,1.82,72.0,cycling,0,0,0 -918866581,Roilya Ranaivosoa,MRI,female,1990-11-14,1.52,48.0,weightlifting,0,0,0 -777959030,Rok Draksic,SLO,male,1987-01-02,1.66,75.0,judo,0,0,0 -215465391,Rok Stipcevic,CRO,male,1986-05-20,1.86,85.0,basketball,0,0,0 -883712398,Roko Ukic,CRO,male,1984-12-05,1.95,88.0,basketball,0,0,0 -79712981,Rolando Palacios,HON,male,1987-05-03,1.89,89.0,athletics,0,0,0 -388656945,Rolando Saquipay,ECU,male,1979-07-21,1.65,61.0,athletics,0,0,0 -640287031,Rolands Strobinders,LAT,male,1992-04-14,1.9,103.0,athletics,0,0,0 -30879894,Rolf-Goran Bengtsson,SWE,male,1962-06-02,1.71,67.0,equestrian,0,0,0 -312873200,Romain Bardet,FRA,male,1990-11-09,1.84,64.0,cycling,0,0,0 -600322031,Romain Blary,FRA,male,1985-10-20,1.95,100.0,aquatics,0,0,0 -230439728,Romain Duguet,SUI,male,1980-10-07,1.76,68.0,equestrian,0,0,0 -64482420,Roman Anoshkin,RUS,male,1987-08-31,1.92,95.0,canoe,0,0,1 -699151957,Roman Bondaruk,UKR,male,1974-06-20,1.77,95.0,shooting,0,0,0 -729709593,Roman Fosti,EST,male,1983-06-06,1.81,69.0,athletics,0,0,0 -309267956,Roman Moustopoulos,GRE,male,1993-03-02,1.77,81.0,judo,0,0,0 -551938294,Roman Prodius,MDA,male,1981-04-12,1.82,70.0,athletics,0,0,0 -617922297,Roman Roeoesli,SUI,male,1993-09-22,1.91,87.0,rowing,0,0,0 -733665408,Roman Valiyev,KAZ,male,1984-03-27,1.9,76.0,athletics,0,0,0 -324597845,Roman Vlasov,RUS,male,1990-10-06,1.75,75.0,wrestling,1,0,0 -634337993,Romana Malacova,CZE,female,1987-05-15,1.64,57.0,athletics,0,0,0 -917720486,Romano Battisti,ITA,male,1986-08-21,1.9,91.0,rowing,0,0,0 -648586794,Romario Leitao,STP,male,1997-01-16,1.75,56.0,athletics,0,0,0 -109375657,Romell Quioto,HON,male,1991-08-09,1.8,86.0,football,0,0,0 -672358347,Rommel Pacheco,MEX,male,1986-07-12,1.67,63.0,aquatics,0,0,0 -154900117,Romuald Hausser,SUI,male,1988-04-16,1.85,72.0,sailing,0,0,0 -201879323,Romy Kasper,GER,female,1988-05-05,1.73,59.0,cycling,0,0,0 -312147622,Ron Atias,ISR,male,1995-04-19,1.7,58.0,taekwondo,0,0,0 -226253632,Ron Darmon,ISR,male,1992-10-30,1.75,65.0,triathlon,0,0,0 -224177837,Ronal Quispe,BOL,male,1988-03-05,1.64,57.0,athletics,0,0,0 -106685753,Ronald Forbes,CAY,male,1985-04-05,,,athletics,0,0,0 -379214136,Ronald Kwemoi,KEN,male,1995-09-19,1.72,58.0,athletics,0,0,0 -244164442,Ronald Musagala,UGA,male,1992-12-16,,,athletics,0,0,0 -335845060,Ronald Rauhe,GER,male,1981-10-03,1.79,82.0,canoe,0,0,1 -115983009,Ronald Serugo,UGA,male,1984-09-05,,,boxing,0,0,0 -745602669,Ronaldas Racinskas,LTU,male,1968-05-13,1.88,85.0,shooting,0,0,0 -972672762,Ronan Gormley,IRL,male,1983-04-21,1.74,74.0,hockey,0,0,0 -947165381,Rondel Sorrillo,TTO,male,1986-01-24,1.78,62.0,athletics,0,0,0 -209988250,Rong Shang,CHN,female,2000-02-12,1.7,45.0,gymnastics,0,0,0 -821012000,Rong Zhao,CHN,female,1991-08-02,1.71,59.0,football,0,0,0 -151465997,Roniel Iglesias,CUB,male,1988-08-14,1.82,,boxing,0,0,0 -243518732,Ronja Sturm,GER,female,1995-09-11,1.65,55.0,rowing,0,0,0 -644702031,Ronnie Ash,USA,male,1988-07-02,1.88,94.0,athletics,0,0,0 -948499068,Roope Kakko,FIN,male,1982-02-13,1.83,82.0,golf,0,0,0 -389320731,Rosa Chacha,ECU,female,1982-12-08,1.55,48.0,athletics,0,0,0 -836406601,Rosa Godoy,ARG,female,1982-03-19,1.6,47.0,athletics,0,0,0 -726865426,Rosa Keleku Lukusa,COD,female,1995-01-16,1.63,48.0,taekwondo,0,0,0 -564590281,Rosa Rodriguez,VEN,female,1986-07-02,1.78,88.0,athletics,0,0,0 -616812664,Rosane Sibele Budag,BRA,female,1973-08-27,1.74,66.0,shooting,0,0,0 -389253550,Rosane dos Reis Santos,BRA,female,1987-06-20,1.62,53.0,weightlifting,0,0,0 -828730273,Rosangela Santos,BRA,female,1990-12-20,1.66,64.0,athletics,0,0,0 -870380362,Rosannagh Maclennan,CAN,female,1988-08-27,1.58,54.0,gymnastics,1,0,0 -353182484,Rosaria Aiello,ITA,female,1989-05-12,1.72,74.0,aquatics,0,1,0 -686173584,Rose Chelimo,BRN,female,1989-07-12,,,athletics,0,0,0 -72544057,Rose Mary Almanza,CUB,female,1992-07-13,1.65,53.0,athletics,0,0,0 -843367304,Rose Nathike Lokonyen,ROT,female,1995-02-24,1.57,50.0,athletics,0,0,0 -506706014,Rose Stackpole,AUS,female,1995-05-25,1.65,55.0,aquatics,0,0,0 -400245389,Rose Thomas,FRA,female,1988-11-29,1.63,66.0,rugby sevens,0,0,0 -272141065,Rose Woo,CAN,female,2000-01-12,1.45,46.0,gymnastics,0,0,0 -287037985,Rosefelo Siosi,SOL,male,1996-08-23,,,athletics,0,0,0 -781979354,Roseline Filion,CAN,female,1987-07-03,1.54,53.0,aquatics,0,0,1 -860145199,Rosemary Quispe,BOL,female,1983-08-20,1.59,50.0,athletics,0,0,0 -779316418,Roser Tarrago Aymerich,ESP,female,1993-03-25,1.71,62.0,aquatics,0,0,0 -592903889,Rosie White,NZL,female,1993-06-06,1.65,63.0,football,0,0,0 -820935997,Rosko Specman,RSA,male,1989-04-28,1.74,81.0,rugby sevens,0,0,1 -264922632,Ross Millington,GBR,male,1989-09-19,1.73,58.0,athletics,0,0,0 -956717868,Ross Murdoch,GBR,male,1994-01-14,1.83,76.0,aquatics,0,0,0 -905413207,Rossella Fiamingo,ITA,female,1991-07-14,1.67,50.0,fencing,0,1,0 -761193618,Rossella Gregorio,ITA,female,1990-08-30,1.64,60.0,fencing,0,0,0 -301051027,Rostyslav Pevtsov,AZE,male,1987-04-15,1.87,74.0,triathlon,0,0,0 -977315432,Roukaya Moussa Mahamane,NIG,female,1997-01-13,1.63,47.0,aquatics,0,0,0 -325010773,Rovshan Bayramov,AZE,male,1987-05-07,1.6,59.0,wrestling,0,0,0 -449448572,Rowie Webster,AUS,female,1987-12-27,1.78,80.0,aquatics,0,0,0 -840917709,Roxana Cogianu,ROU,female,1986-09-21,1.8,72.0,rowing,0,0,1 -5862724,Roxana Gomez,CUB,female,1999-01-07,1.69,49.0,athletics,0,0,0 -506230382,Roxanne Barker,RSA,female,1991-05-06,1.8,76.0,football,0,0,0 -848420962,Roxroy Cato,JAM,male,1988-06-05,1.83,76.0,athletics,0,0,0 -734810648,Roy Krishna,FIJ,male,1987-08-30,1.7,70.0,football,0,0,0 -375955657,Roy Meyer,NED,male,1991-06-04,1.86,122.0,judo,0,0,0 -745464194,Roy Schmidt,GER,male,1991-09-30,1.79,77.0,athletics,0,0,0 -402214262,Rozaliya Nasretdinova,RUS,female,1997-02-10,1.8,66.0,aquatics,0,0,0 -75479348,Ruaridh McConnochie,GBR,male,1991-10-23,1.9,93.0,rugby sevens,0,1,0 -209686890,Ruben Aleksanyan,ARM,male,1990-03-14,1.8,152.0,weightlifting,0,0,0 -477986784,Ruben Limardo Gascon,VEN,male,1985-08-03,1.75,75.0,fencing,0,0,0 -411710210,Ruben Scheire,BEL,male,1991-12-06,1.75,62.0,cycling,0,0,0 -249844758,Ruben Voisard Rezola,ARG,male,1991-04-21,1.74,80.0,canoe,0,0,0 -179815659,Rubens Donizete Valeriano,BRA,male,1979-08-14,1.73,69.0,cycling,0,0,0 -527756567,Ruby Harrold,GBR,female,1996-06-04,1.62,56.0,gymnastics,0,0,0 -233580730,Ruby Tew,NZL,female,1994-03-07,1.81,76.0,rowing,0,0,0 -192700977,Ruby Tui,NZL,female,1991-12-13,1.77,71.0,rugby sevens,0,1,0 -659959985,Ruda Franco,BRA,male,1986-07-25,1.85,90.0,aquatics,0,0,0 -446404656,Ruddy Zang Milama,GAB,female,1987-06-06,1.56,53.0,athletics,0,0,0 -232202316,Rudi van Houts,NED,male,1984-01-16,1.77,64.0,cycling,0,0,0 -743075472,Rudo Neshamba,ZIM,female,1992-02-10,1.64,53.0,football,0,0,0 -371266772,Rudolf Knijnenburg,BOL,male,1982-05-18,1.76,82.0,shooting,0,0,0 -514482932,Rudy Fernandez,ESP,male,1985-04-04,1.96,84.0,basketball,0,0,1 -648750303,Rudy Gobert,FRA,male,1992-06-26,2.15,113.0,basketball,0,0,0 -220770340,Rudy Verhoeff,CAN,male,1989-06-24,1.98,88.0,volleyball,0,0,0 -143605979,Rudy Winkler,USA,male,1994-12-06,1.88,108.0,athletics,0,0,0 -334846524,Rufat Huseynov,AZE,male,1997-04-25,1.7,,boxing,0,0,0 -18555870,Ruggero Pertile,ITA,male,1974-08-08,1.65,55.0,athletics,0,0,0 -914411806,Ruggero Tita,ITA,male,1992-03-20,1.74,71.0,sailing,0,0,0 -990887420,Rui Alberto Faria da Costa,POR,male,1986-10-05,1.83,68.0,cycling,0,0,0 -513573507,Rui Braganca,POR,male,1991-12-26,1.8,60.0,taekwondo,0,0,0 -242415534,Rui Machida,JPN,female,1993-03-08,1.61,57.0,basketball,0,0,0 -490760543,Rui Pedro Silva,POR,male,1981-05-06,1.73,60.0,athletics,0,0,0 -27201624,Rui Xu,CHN,female,1995-06-06,1.7,63.0,wrestling,0,0,0 -3876003,Rui Zhang,CHN,female,1989-01-17,1.72,62.0,football,0,0,0 -32285535,Rumen Dimitrov,BUL,male,1986-09-19,1.92,80.0,athletics,0,0,0 -621812972,Runa Imai,JPN,female,2000-08-15,1.63,50.0,aquatics,0,0,0 -377313576,Rune Hermans,BEL,female,1999-05-09,1.59,48.0,gymnastics,0,0,0 -854755696,Ruolin Chen,CHN,female,1992-12-12,1.6,49.0,aquatics,1,0,0 -777015312,Ruoqi Hui,CHN,female,1991-03-04,1.92,78.0,volleyball,1,0,0 -111970158,Rupinder Pal Singh,IND,male,1990-11-11,1.93,93.0,hockey,0,0,0 -333696204,Rushana Nurjavova,TKM,female,1994-06-22,1.65,57.0,judo,0,0,0 -666630210,Rusheen McDonald,JAM,male,1992-08-17,1.75,81.0,athletics,0,1,0 -85111828,Rushlee Buchanan,NZL,female,1988-01-20,1.7,63.0,cycling,0,0,0 -95697909,Rushwal Samaai,RSA,male,1991-09-25,1.85,75.0,athletics,0,0,0 -718060123,Rusila Nagasau,FIJ,female,1987-08-04,1.75,80.0,rugby sevens,0,0,0 -338294011,Ruslan Dmytrenko,UKR,male,1986-03-22,1.8,67.0,athletics,0,0,0 -111143331,Ruslan Kurbanov,UZB,male,1993-02-10,1.83,65.0,athletics,0,0,0 -736588005,Ruslan Lunev,AZE,male,1989-07-25,1.83,80.0,shooting,0,0,0 -327649539,Ruslan Nurudinov,UZB,male,1991-11-24,1.83,105.0,weightlifting,1,0,0 -507662268,Ruslan Tsarev,KGZ,male,1991-07-16,1.7,66.0,wrestling,0,0,0 -362733210,Ruslan Zhaparov,KAZ,male,1996-05-27,1.98,80.0,taekwondo,0,0,0 -577577039,Ruslana Tsykhotska,UKR,female,1986-03-23,1.65,56.0,athletics,0,0,0 -267763098,Ruslans Nakonechnyi,LAT,male,1989-04-21,1.82,71.0,modern pentathlon,0,0,0 -497886017,Rustam Assakalov,UZB,male,1984-07-13,1.83,85.0,wrestling,0,0,0 -87660757,Rustam Djangabaev,UZB,male,1993-08-25,1.83,146.0,weightlifting,0,0,0 -453637233,Rustam Orujov,AZE,male,1991-10-04,1.8,73.0,judo,0,1,0 -822684944,Rustam Tulaganov,UZB,male,1991-10-08,1.86,,boxing,0,0,1 -423291068,Ruta Meilutyte,LTU,female,1997-03-19,1.76,75.0,aquatics,0,0,0 -103023896,Rutendo Joan Nyahora,ZIM,female,1988-11-11,1.55,45.0,athletics,0,0,0 -532873176,Rutendo Makore,ZIM,female,1992-09-30,1.65,68.0,football,0,0,0 -936775315,Rutger van Schaardenburg,NED,male,1987-10-08,1.88,80.0,sailing,0,0,0 -146985778,Ruth Beitia,ESP,female,1979-04-01,1.91,72.0,athletics,1,0,0 -126006667,Ruth Jebet,BRN,female,1996-11-17,1.62,51.0,athletics,1,0,0 -379466742,Ruth Marie Christelle Gbagbi,CIV,female,1994-02-07,1.76,65.0,taekwondo,0,0,1 -686937343,Ruth Sophia Spelmeyer,GER,female,1990-09-19,1.73,59.0,athletics,0,0,0 -532261640,Ruth Winder,USA,female,1993-07-09,1.63,57.0,cycling,0,0,0 -373628906,Ruvimbo Mutyavaviri,ZIM,female,1986-12-08,1.75,65.0,football,0,0,0 -103779726,Ruy Fonseca,BRA,male,1973-06-09,1.81,76.0,equestrian,0,0,0 -622244605,Ruyin Tan,CHN,female,1994-07-17,1.65,59.0,football,0,0,0 -334757159,Ryad Keniche,ALG,male,1993-04-30,1.85,75.0,football,0,0,0 -809153340,Ryan Archibald,NZL,male,1980-09-01,1.86,78.0,hockey,0,0,0 -96527612,Ryan Bailie,AUS,male,1990-07-15,1.77,61.0,triathlon,0,0,0 -205829488,Ryan Broekhoff,AUS,male,1990-08-23,2.01,93.0,basketball,0,0,0 -907618760,Ryan Carlyle,USA,female,1989-11-24,1.68,65.0,rugby sevens,0,0,0 -660446084,Ryan Cochrane,CAN,male,1988-10-29,1.92,80.0,aquatics,0,0,0 -330882245,Ryan Cochrane,CAN,male,1983-07-24,1.78,78.0,canoe,0,0,0 -613943202,Ryan Crouser,USA,male,1992-12-18,2.01,124.0,athletics,1,0,0 -313062867,Ryan Fisher,AUS,male,1991-04-05,1.73,64.0,triathlon,0,0,0 -739862351,Ryan Fox,NZL,male,1987-01-22,1.79,98.0,golf,0,0,0 -976252794,Ryan Gregson,AUS,male,1990-04-26,1.74,83.0,athletics,0,0,0 -788805861,Ryan Held,USA,male,1995-06-27,,,aquatics,1,0,0 -712200855,Ryan Lochte,USA,male,1984-08-03,1.88,88.0,aquatics,1,0,0 -237391684,Ryan Murphy,USA,male,1995-07-02,1.91,90.0,aquatics,3,0,0 -524278516,Ryan Patterson,RSA,male,1994-01-10,1.77,72.0,gymnastics,0,0,0 -882980527,Ryan Pini,PNG,male,1981-12-10,1.96,98.0,aquatics,0,0,0 -799465101,Ryan Seaton,IRL,male,1987-12-03,1.82,77.0,sailing,0,0,0 -164279204,Ryan Sissons,NZL,male,1988-06-24,1.75,62.0,triathlon,0,0,0 -764390994,Ryan Taylor,NZL,male,1980-03-02,1.75,73.0,shooting,0,0,0 -524440279,Ryan Tyack,AUS,male,1991-06-02,1.86,102.0,archery,0,0,1 -983386718,Rynardt van Rensburg,RSA,male,1992-03-23,1.85,73.0,athletics,0,0,0 -40870014,Ryohei Arai,JPN,male,1991-06-23,1.83,95.0,athletics,0,0,0 -166557498,Ryohei Kato,JPN,male,1993-09-09,1.62,54.0,gymnastics,1,0,0 -634681317,Ryosuke Irie,JPN,male,1990-01-24,1.78,64.0,aquatics,0,0,0 -67014071,Ryota Ohshima,JPN,male,1993-01-23,1.68,64.0,football,0,0,0 -549532344,Ryota Yamagata,JPN,male,1992-06-10,1.77,70.0,athletics,0,1,0 -105508843,Ryunosuke Haga,JPN,male,1991-04-28,1.86,100.0,judo,0,0,1 -716069085,Ryuzo Kitajima,JPN,male,1985-10-23,1.71,68.0,equestrian,0,0,0 -131783687,S.S.P. Chawrasia,IND,male,1978-05-15,1.6,67.0,golf,0,0,0 -184171388,Sa Rang Kim,KOR,male,1989-08-22,1.78,82.0,badminton,0,0,0 -329105644,Saad Luaibi,IRQ,male,1992-01-19,1.78,75.0,football,0,0,0 -155945642,Sabah Shariati,AZE,male,1989-01-01,1.92,130.0,wrestling,0,0,1 -80742707,Sabina Asenjo,ESP,female,1986-08-03,1.78,90.0,athletics,0,0,0 -110659347,Sabina Ashirbayeva,KAZ,female,1998-11-05,1.63,45.0,gymnastics,0,0,0 -775171391,Sabina Jacobsen,SWE,female,1989-03-24,1.8,75.0,handball,0,0,0 -971872432,Sabina Mikina,AZE,female,1987-10-24,1.68,51.0,fencing,0,0,0 -935009563,Sabina Veit,SLO,female,1985-12-02,1.67,59.0,athletics,0,0,0 -602319311,Sabine Kusterer,GER,female,1991-01-04,1.56,58.0,weightlifting,0,0,0 -634385368,Sabine Spitz,GER,female,1971-12-27,1.67,60.0,cycling,0,0,0 -665063441,Sabrina Ameghino,ARG,female,1980-07-06,1.69,64.0,canoe,0,0,0 -574404234,Sabrina D Angelo,CAN,female,1993-05-11,1.73,71.0,football,0,0,1 -726001944,Sabrina Delannoy,FRA,female,1986-05-18,1.71,62.0,football,0,0,0 -532268413,Sabrina Filzmoser,AUT,female,1980-06-12,1.73,57.0,judo,0,0,0 -103751592,Sabrina Hering,GER,female,1992-02-16,1.67,70.0,canoe,0,1,0 -832538099,Sabrina Jaquet,SUI,female,1987-06-21,1.69,60.0,badminton,0,0,0 -858092870,Sacha Valleau,FRA,male,1996-10-08,1.92,97.0,rugby sevens,0,0,0 -773178246,Sachi Mochida,JPN,female,1999-07-19,1.66,57.0,aquatics,0,0,0 -966020626,Sadiq Umar,NGR,male,1997-02-02,1.72,,football,0,0,1 -49083611,Sae Miyakawa,JPN,female,1999-09-10,1.45,34.0,gymnastics,0,0,0 -240262036,Saeed Almaktoum,UAE,male,1976-10-01,1.74,88.0,shooting,0,0,0 -752177504,Saehyuk Joo,KOR,male,1980-01-20,1.8,67.0,table tennis,0,0,0 -270527674,Saeid Mollaei,IRI,male,1992-01-05,1.76,82.0,judo,0,0,0 -795286396,Saeid Morad Abdvali,IRI,male,1989-11-04,1.7,80.0,wrestling,0,0,1 -436856323,Safa Saidani,TUN,female,1990-05-26,,,table tennis,0,0,0 -208668946,Safwan Khalil,AUS,male,1986-05-15,1.84,58.0,taekwondo,0,0,0 -329709652,Sage Watson,CAN,female,1994-06-20,1.8,57.0,athletics,0,0,0 -888210415,Sagi Muki,ISR,male,1992-05-17,1.8,73.0,judo,0,0,0 -585435438,Sahily Diago,CUB,female,1995-08-26,1.69,49.0,athletics,0,0,0 -74385345,Sahit Prizreni,AUS,male,1983-02-23,1.66,65.0,wrestling,0,0,0 -627433948,Saidi Juma Makula,TAN,male,1994-08-01,,54.0,athletics,0,0,0 -226370287,Saif Bin Futtais,UAE,male,1973-09-02,1.74,90.0,shooting,0,0,0 -863864577,Saily Viart,CUB,female,1995-09-10,1.7,94.0,athletics,0,0,0 -311986373,Saina Nehwal,IND,female,1990-03-17,1.65,65.0,badminton,0,0,0 -138238574,Saisai Zheng,CHN,female,1994-02-05,1.7,62.0,tennis,0,0,0 -872568098,Saiyidah Mohamed Rafa'ee,SIN,female,1988-04-20,1.73,68.0,rowing,0,0,0 -745752282,Saiyinjirigala,CHN,male,1989-12-14,1.75,73.0,judo,0,0,0 -528224389,Sajan Prakash Prakash,IND,male,1993-09-14,1.78,70.0,aquatics,0,0,0 -950063976,Sajjad Mardani,IRI,male,1988-07-01,1.98,96.0,taekwondo,0,0,0 -665801270,Sakiko Shimizu,JPN,female,1992-04-20,1.56,50.0,aquatics,0,0,0 -20596177,Sakina Karchaoui,FRA,female,1996-01-26,1.6,55.0,football,0,0,0 -686699609,Sakiyo Asano,JPN,female,1987-05-26,1.64,60.0,hockey,0,0,0 -249879747,Sakshi Malik,IND,female,1992-09-03,1.62,58.0,wrestling,0,0,1 -530076969,Sakura Noshitani,JPN,female,1997-09-29,1.68,48.0,gymnastics,0,0,0 -265460437,Sakura Tsukagoshi,JPN,female,1991-04-13,1.68,60.0,cycling,0,0,0 -612549397,Salem Eid Yaqoob,BRN,male,1996-03-01,1.75,65.0,athletics,0,0,0 -294737301,Salim Keddar,ALG,male,1993-11-13,1.76,65.0,athletics,0,0,0 -966097460,Salima Elouali Alami,MAR,female,1983-12-29,1.79,58.0,athletics,0,0,0 -189291344,Sally Conway,GBR,female,1987-02-01,1.67,70.0,judo,0,0,1 -102051307,Sally Kehoe,AUS,female,1986-09-25,1.72,75.0,rowing,0,0,0 -808105365,Sally Rutherford,NZL,female,1981-06-05,1.65,65.0,hockey,0,0,0 -11169898,Sally Yee,FIJ,female,2001-04-10,,84.0,table tennis,0,0,0 -354208577,Salma Negmeldin,EGY,female,1996-03-05,1.6,49.0,aquatics,0,0,0 -667737647,Salome Kora,SUI,female,1994-06-08,1.73,66.0,athletics,0,0,0 -646360332,Salome Nyirarukundo,RWA,female,1997-12-20,,,athletics,0,0,0 -975877653,Salome Pazhava,GEO,female,1997-09-03,1.68,47.0,gymnastics,0,0,0 -603595723,Salvador,POR,male,1991-11-11,1.68,70.0,football,0,0,0 -203392098,Salvador Piera,ESP,male,1991-05-18,1.83,83.0,hockey,0,0,0 -462840223,Salvatore Rossini,ITA,male,1986-07-13,1.85,82.0,volleyball,0,1,0 -842038551,Salwa Eid Naser,BRN,female,1998-05-23,1.68,54.0,athletics,0,0,0 -206513748,Salwan Jasim Abbood Abbood,IRQ,male,1991-09-26,1.8,104.0,weightlifting,0,0,0 -326897448,Sam Cross,GBR,male,1992-08-26,1.91,103.0,rugby sevens,0,1,0 -789125587,Sam Crouser,USA,male,1991-12-31,1.99,104.0,athletics,0,0,0 -850239984,Sam Dickson,NZL,male,1989-10-28,1.93,101.0,rugby sevens,0,0,0 -660006484,Sam Dommer,USA,male,1991-09-04,1.88,91.0,rowing,0,0,0 -898696395,Sam Dorman,USA,male,1991-08-30,1.76,77.0,aquatics,0,1,0 -23788700,Sam Griffiths,AUS,male,1972-05-27,1.71,73.0,equestrian,0,0,1 -826848760,Sam Groth,AUS,male,1987-10-19,,,tennis,0,0,0 -835488067,Sam Kendricks,USA,male,1992-09-07,1.86,79.0,athletics,0,0,1 -785341118,Sam McEntee,AUS,male,1992-02-03,1.91,67.0,athletics,0,0,0 -294472857,Sam Meech,NZL,male,1991-04-04,1.83,81.0,sailing,0,0,1 -115220097,Sam Townsend,GBR,male,1985-11-26,1.99,102.0,rowing,0,0,0 -541927313,Sam Ward,GBR,male,1990-12-24,1.78,75.0,hockey,0,0,0 -479073328,Sam Webster,NZL,male,1991-07-16,1.83,80.0,cycling,0,1,0 -419064340,Sam Welsford,AUS,male,1996-01-19,1.79,82.0,cycling,0,1,0 -55429092,Sam Willoughby,AUS,male,1991-08-15,1.73,87.0,cycling,0,0,0 -600127558,Saman Ahmed Tahmasebi,AZE,male,1985-07-26,1.8,85.0,wrestling,0,0,0 -643857228,Samantha Arevalo,ECU,female,1994-09-30,1.71,60.0,aquatics,0,0,0 -144477923,Samantha Birch,AUS,female,1981-06-06,1.61,,equestrian,0,0,0 -750356413,Samantha Charlton,NZL,female,1991-12-07,1.74,66.0,hockey,0,0,0 -932711733,Samantha Kassman,PNG,female,1984-01-23,1.65,68.0,taekwondo,0,0,0 -494749900,Samantha Kerr,AUS,female,1993-09-10,1.67,63.0,football,0,0,0 -890674126,Samantha Mewis,USA,female,1992-10-09,1.83,74.0,football,0,0,0 -103473949,Samantha Murray,GBR,female,1989-09-25,1.74,60.0,modern pentathlon,0,0,0 -244735360,Samantha Quek,GBR,female,1988-10-18,1.69,62.0,hockey,1,0,0 -917830379,Samantha Roberts,ANT,female,2000-04-21,1.72,64.0,aquatics,0,0,0 -580514299,Samantha Stosur,AUS,female,1984-03-30,1.72,68.0,tennis,0,0,0 -431344021,Samar Amer Ibrahim Hamza,EGY,female,1995-04-04,1.7,75.0,wrestling,0,0,0 -261741263,Sami Hill,USA,female,1992-06-08,1.83,89.0,aquatics,1,0,0 -813927295,Samia Ahmed,EGY,female,1996-01-20,1.7,57.0,aquatics,0,0,0 -869328062,Samir Ait Said,FRA,male,1989-11-01,1.67,66.0,gymnastics,0,0,0 -556027153,Samira Amirova,UZB,female,1998-04-02,1.62,47.0,gymnastics,0,0,0 -108428818,Samira Ouass,MAR,female,1992-04-22,1.62,74.0,weightlifting,0,0,0 -969554371,Samira Rocha,BRA,female,1989-01-26,1.7,64.0,handball,0,0,0 -65132330,Samisoni Viriviri,FIJ,male,1988-04-25,1.86,88.0,rugby sevens,1,0,0 -496725946,Samkelisiwe Zulu,ZIM,female,1990-04-14,1.64,50.0,football,0,0,0 -712300954,Samson Samuel Opuakpo Forcados,NGR,male,1986-04-24,1.83,81.0,aquatics,0,0,0 -120558748,Samuel Albrecht,BRA,male,1981-09-02,1.8,71.0,sailing,0,0,0 -992495912,Samuel Carmona Heredia,ESP,male,1996-05-28,1.62,,boxing,0,0,0 -513575285,Samuel Cordova,MEX,male,1989-03-13,2.0,89.0,volleyball,0,0,0 -443125316,Samuel Gaze,NZL,male,1995-12-12,1.89,79.0,cycling,0,0,0 -422579271,Samuel Ireri Gathimba,KEN,male,1987-10-26,1.65,57.0,athletics,0,0,0 -619793183,Samuel Mikulak,USA,male,1992-10-13,1.67,63.0,gymnastics,0,0,0 -828892813,Samuel Ojserkis,USA,male,1990-03-24,1.73,55.0,rowing,0,0,0 -322626973,Samuel Oliech,KEN,male,1993-12-15,1.85,92.0,rugby sevens,0,0,0 -203872353,Samuel Schachter,CAN,male,1990-05-08,1.95,80.0,volleyball,0,0,0 -538227014,Samuel Walker,GBR,male,1995-05-07,1.86,74.0,table tennis,0,0,0 -184337666,Samuela Nabenia,FIJ,male,1995-02-09,1.78,76.0,football,0,0,0 -554608436,Samuil Donkov,BUL,male,1983-06-20,1.7,68.0,shooting,0,0,0 -232045954,Samuli Piippo,FIN,male,1980-01-01,1.81,75.0,archery,0,0,0 -82346325,Samy Abdel Razek,EGY,male,1980-04-10,1.7,60.0,shooting,0,0,0 -110457904,San Naing San Naing,MYA,male,1991-03-05,1.7,57.0,athletics,0,0,0 -311616634,San Yu Htwe,MYA,female,1986-10-14,1.67,57.0,archery,0,0,0 -760631015,Sanaa Koubaa,GER,female,1985-01-06,1.7,56.0,athletics,0,0,0 -936764018,Sanae Motokawa,JPN,female,1992-04-02,1.75,65.0,basketball,0,0,0 -193482094,Sanah Mollo,RSA,female,1987-01-30,1.59,64.0,football,0,0,0 -303543549,Sanchai Ratiwatana,THA,male,1982-01-23,1.75,70.0,tennis,0,0,0 -656865751,Sanda Belgyan,ROU,female,1992-12-17,1.65,53.0,athletics,0,0,0 -740295602,Sandeep Kumar,IND,male,1986-05-01,,,athletics,0,0,0 -394939837,Sandeep Tomar,IND,male,1991-04-02,1.68,61.0,wrestling,0,0,0 -597658041,Sander Baart,NED,male,1988-04-30,1.78,76.0,hockey,0,0,0 -287509063,Sander de Wijn,NED,male,1990-05-02,1.83,78.0,hockey,0,0,0 -550264358,Sandi Morris,USA,female,1992-07-08,1.73,62.0,athletics,0,1,0 -603435489,Sandie Clair,FRA,female,1988-04-01,1.6,60.0,cycling,0,0,0 -317595891,Sandie Toletti,FRA,female,1995-07-13,1.69,58.0,football,0,0,0 -952416738,Sandor Racz,HUN,male,1986-09-14,1.78,68.0,athletics,0,0,0 -183435388,Sandor Totka,HUN,male,1994-07-27,1.88,85.0,canoe,0,0,0 -161387205,Sandra Aguilar,ESP,female,1992-08-09,1.68,51.0,gymnastics,0,1,0 -994885002,Sandra Arenas,COL,female,1993-09-17,1.6,50.0,athletics,0,0,0 -750628038,Sandra Auffarth,GER,female,1986-12-27,1.7,57.0,equestrian,0,1,0 -530853976,Sandra Eriksson,FIN,female,1989-06-04,1.63,47.0,athletics,0,0,0 -530454853,Sandra Gal,GER,female,1985-05-09,1.83,70.0,golf,0,0,0 -202752942,Sandra Galvis,COL,female,1986-06-28,1.6,46.0,athletics,0,0,0 -112616108,Sandra Gomis,FRA,female,1983-11-21,1.65,53.0,athletics,0,0,0 -857025783,Sandra Lemos,COL,female,1989-01-01,1.7,100.0,athletics,0,0,0 -211016698,Sandra Perkovic,CRO,female,1990-06-21,1.83,85.0,athletics,1,0,0 -999653955,Sandra Sepulveda,COL,female,1988-03-03,1.67,58.0,football,0,0,0 -705817505,Sandrine Gruda,FRA,female,1987-06-25,1.93,83.0,basketball,0,0,0 -359187609,Sandrine Mainville,CAN,female,1992-03-20,1.73,66.0,aquatics,0,0,1 -458205614,Sandro Aminashvili,GEO,male,1992-02-21,1.8,85.0,wrestling,0,0,0 -673884830,Sandro Bazadze,GEO,male,1993-07-29,1.93,88.0,fencing,0,0,0 -140258499,Sandro Sukno,CRO,male,1990-06-30,2.0,93.0,aquatics,0,1,0 -592791233,Sanghoon Park,KOR,male,1993-03-13,1.83,77.0,cycling,0,0,0 -87067072,Sanghyeok Woo,KOR,male,1996-04-23,1.87,70.0,athletics,0,0,0 -540684681,Sangmin Sim,KOR,male,1993-05-21,1.72,70.0,football,0,0,0 -15622309,Sangmyeong Ham,KOR,male,1995-11-10,1.68,,boxing,0,0,0 -652133768,Sangsu Lee,KOR,male,1990-08-13,1.8,69.0,table tennis,0,0,0 -437224347,Sangwook Lee,KOR,male,1985-10-14,1.7,60.0,gymnastics,0,0,0 -654419111,Sangyoung Park,KOR,male,1995-10-16,1.77,73.0,fencing,1,0,0 -980035227,Sania Mirza,IND,female,1986-11-15,1.66,57.0,tennis,0,0,0 -23025751,Sanita Puspure,IRL,female,1981-12-21,1.8,72.0,rowing,0,0,0 -385623198,Sanna Solberg,NOR,female,1990-06-16,1.78,,handball,0,0,1 -813586699,Sanne Verhagen,NED,female,1992-08-24,1.53,57.0,judo,0,0,0 -366953580,Sanne Wevers,NED,female,1991-09-17,1.56,46.0,gymnastics,1,0,0 -747452521,Sanne van Olphen,NED,female,1989-03-13,1.77,68.0,handball,0,0,0 -131711159,Sanni Utriainen,FIN,female,1991-02-05,1.69,72.0,athletics,0,0,0 -658712925,Santa Pakenyte,LTU,female,1990-12-11,1.86,132.0,judo,0,0,0 -438444460,Santiago Alvarez,ARG,male,1994-01-17,1.88,93.0,rugby sevens,0,0,0 -88117385,Santiago Ascacibar,ARG,male,1997-02-25,1.74,77.0,football,0,0,0 -466362039,Santiago Enrique Grillo Diez,COL,male,1987-05-27,1.85,74.0,sailing,0,0,0 -86887082,Santiago Gonzalez,MEX,male,1983-02-24,1.91,91.0,tennis,0,0,0 -455876725,Santiago Grassi,ARG,male,1996-09-25,1.87,82.0,aquatics,0,0,0 -921054925,Santiago Lange,ARG,male,1961-09-22,1.84,73.0,sailing,1,0,0 -929112768,Santiago Ramirez Morales,COL,male,1994-07-20,1.8,82.0,cycling,0,0,0 -213886728,Santino Kenyi,SSD,male,1993-08-14,1.8,75.0,athletics,0,0,0 -475115369,Santisouk Inthavong,LAO,male,1999-09-02,1.7,60.0,aquatics,0,0,0 -156085164,Santo Condorelli,CAN,male,1995-01-17,1.88,88.0,aquatics,0,0,0 -754215814,Saori Kimura,JPN,female,1986-08-19,1.85,65.0,volleyball,0,0,0 -43036553,Saori Nagamine,JPN,female,1993-07-05,1.66,60.0,archery,0,0,0 -631663441,Saori Sakoda,JPN,female,1987-12-18,1.75,63.0,volleyball,0,0,0 -39484933,Saori Yoshida,JPN,female,1982-10-05,1.57,55.0,wrestling,0,1,0 -988318457,Saoussen Boudiaf,FRA,female,1993-12-31,1.73,69.0,fencing,0,0,0 -451333617,Sapana Sapana,IND,female,1988-01-02,,,athletics,0,0,0 -361564419,Sapsiree Taerattanachai,THA,female,1992-04-18,1.7,59.0,badminton,0,0,0 -635644066,Sara Ahmed,EGY,female,1998-01-01,1.55,68.0,weightlifting,0,0,1 -424013884,Sara Algotsson Ostholt,SWE,female,1974-12-08,1.63,56.0,equestrian,0,0,0 -871531053,Sara Bertolasi,ITA,female,1988-04-29,1.79,68.0,rowing,0,0,0 -657896616,Sara Carmo,POR,female,1986-10-12,1.79,69.0,sailing,0,0,0 -576383527,Sara Cholnoky,HUN,female,1988-11-03,1.65,64.0,sailing,0,0,0 -716078374,Sara Daebritz,GER,female,1995-02-15,1.71,59.0,football,1,0,0 -807403357,Sara Dosho,JPN,female,1994-10-17,1.59,69.0,wrestling,1,0,0 -54068810,Sara Errani,ITA,female,1987-04-29,1.64,58.0,tennis,0,0,0 -809844335,Sara Franceschi,ITA,female,1999-02-01,1.78,61.0,aquatics,0,0,0 -628076788,Sara Gambetta,GER,female,1993-02-18,1.84,89.0,athletics,0,0,0 -851213557,Sara Kolak,CRO,female,1995-06-22,1.7,74.0,athletics,1,0,0 -824809383,Sara Krnjic,SRB,female,1991-07-15,1.93,83.0,basketball,0,0,1 -718107651,Sara Lopez Ravetllat,ESP,female,1992-11-29,1.65,66.0,sailing,0,0,0 -213440914,Sara Louise Treacy,IRL,female,1989-06-22,1.68,58.0,athletics,0,0,0 -21017895,Sara Moreira,POR,female,1985-10-17,1.66,50.0,athletics,0,0,0 -686409294,Sara Mustonen,SWE,female,1981-02-08,1.61,58.0,cycling,0,0,0 -517852202,Sara Pastrana,HON,female,1999-03-12,1.7,55.0,aquatics,0,0,0 -764316889,Sara Ramadhani,TAN,female,1987-12-30,,45.0,athletics,0,0,0 -939579347,Sara Sgarzi,ITA,female,1986-05-27,1.75,63.0,aquatics,0,0,0 -796713465,Sara Slott Petersen,DEN,female,1987-04-09,1.71,57.0,athletics,0,1,0 -968095020,Sara Tan,SIN,female,1990-08-06,1.71,71.0,sailing,0,0,0 -666449730,Sara Vilic,AUT,female,1992-03-29,1.7,55.0,triathlon,0,0,0 -32160007,Sarah Atcho,SUI,female,1995-06-01,1.8,63.0,athletics,0,0,0 -216183238,Sarah Attar,KSA,female,1992-08-27,1.65,52.0,athletics,0,0,0 -717386326,Sarah Banting,AUS,female,1993-11-09,,,rowing,0,0,0 -578952502,Sarah Barrow,GBR,female,1988-10-22,1.6,53.0,aquatics,0,0,0 -920425475,Sarah Bouhaddi,FRA,female,1986-10-17,1.75,68.0,football,0,0,0 -307552411,Sarah Bro,DEN,female,1996-03-04,1.77,64.0,aquatics,0,0,0 -955540321,Sarah Elizabeth Robles,USA,female,1988-08-01,1.78,143.0,weightlifting,0,0,1 -291926816,Sarah Goss,NZL,female,1992-12-09,1.76,73.0,rugby sevens,0,1,0 -489042953,Sarah Gregorius,NZL,female,1987-08-06,1.58,52.0,football,0,0,0 -828720993,Sarah Guyot,FRA,female,1991-04-16,1.76,69.0,canoe,0,0,0 -773163998,Sarah Hammer,USA,female,1983-08-18,1.71,65.0,cycling,0,2,0 -245433771,Sarah Hornung,SUI,female,1996-04-18,1.51,43.0,shooting,0,0,0 -135997067,Sarah Kohler,GER,female,1994-06-20,1.79,67.0,aquatics,0,0,0 -335518971,Sarah Lahti,SWE,female,1995-02-18,1.77,54.0,athletics,0,0,0 -842091888,Sarah Menezes,BRA,female,1990-03-26,1.54,48.0,judo,0,0,0 -218628942,Sarah Michel,FRA,female,1989-01-10,1.8,65.0,basketball,0,0,0 -886890449,Sarah Myriam Mazouz,GAB,female,1987-04-29,1.77,77.0,judo,0,0,0 -601579213,Sarah Nikitin,BRA,female,1988-12-27,1.7,75.0,archery,0,0,0 -950602968,Sarah Ourahmoune,FRA,female,1982-01-21,1.57,,boxing,0,1,0 -809483271,Sarah Pavan,CAN,female,1986-08-16,1.96,70.0,volleyball,0,0,0 -176016249,Sarah Scherer,USA,female,1991-02-12,1.71,62.0,shooting,0,0,0 -350191738,Sarah Sjostrom,SWE,female,1993-08-17,1.82,76.0,aquatics,1,1,1 -464482319,Sarah Steyaert,FRA,female,1986-11-27,1.75,67.0,sailing,0,0,0 -952559484,Sarah Troel,FRA,female,1986-07-12,1.75,71.0,canoe,0,0,0 -360246098,Sarah True,USA,female,1981-11-27,1.71,60.0,triathlon,0,0,0 -204834878,Sarah-Anne Brault,CAN,female,1989-12-01,1.71,65.0,triathlon,0,0,0 -579905792,Saraswati Bhattarai,NEP,female,1994-03-08,1.63,46.0,athletics,0,0,0 -509184238,Sarat Sumpradit,THA,male,1994-04-17,1.81,93.0,weightlifting,0,0,0 -487278690,Sardar Singh,IND,male,1986-07-15,1.76,80.0,hockey,0,0,0 -32650602,Sardorbek Dusmurotov,UZB,male,1993-03-13,1.68,110.0,weightlifting,0,0,0 -662999016,Sargis Martirosjan,AUT,male,1986-09-14,1.79,104.0,weightlifting,0,0,0 -122086540,Sarolta Kovacs,HUN,female,1991-03-12,1.67,59.0,modern pentathlon,0,0,0 -929534823,Sarra Besbes,TUN,female,1989-02-05,1.75,62.0,fencing,0,0,0 -357200340,Sasa Cado,SRB,female,1989-07-13,1.78,72.0,basketball,0,0,1 -513283860,Sasa MISIC,MNE,male,1987-03-27,1.98,109.0,aquatics,0,0,0 -672574541,Sascha Klein,GER,male,1985-09-12,1.73,71.0,aquatics,0,0,0 -586159759,Sascia Kraus,SUI,female,1993-03-03,1.77,65.0,aquatics,0,0,0 -543039018,Sashalee Forbes,JAM,female,1996-05-10,1.6,55.0,athletics,0,1,0 -626916015,Saskia Bartusiak,GER,female,1982-09-09,1.7,60.0,football,1,0,0 -447095096,Saskia Clark,GBR,female,1979-08-23,1.76,68.0,sailing,1,0,0 -757107967,Saskia Loretta van Erven Garcia,COL,female,1987-08-29,1.75,64.0,fencing,0,0,0 -819917798,Saskia Tidey,IRL,female,1993-06-11,1.83,70.0,sailing,0,0,0 -120862414,Saso Taljat,SLO,male,1989-09-22,1.74,73.0,canoe,0,0,0 -857233175,Sathish Kumar Sivalingam,IND,male,1992-06-23,1.75,77.0,weightlifting,0,0,0 -753187729,Satomi Kubokura,JPN,female,1982-04-27,1.6,52.0,athletics,0,0,0 -631296742,Satomi Suzuki,JPN,female,1991-01-29,1.68,62.0,aquatics,0,0,0 -186482270,Satoru Sasaki,JPN,male,1985-10-16,1.71,56.0,athletics,0,0,0 -780249197,Sattawat Pongnairat,USA,male,1990-05-08,1.81,70.0,badminton,0,0,0 -160287556,Satu Makela-Nummela,FIN,female,1970-10-26,1.69,63.0,shooting,0,0,0 -458458780,Saturday Erimuya,NGR,male,1998-01-10,1.71,,football,0,0,1 -742022352,Saud Alzaabi,UAE,male,1988-08-07,,,athletics,0,0,0 -800437586,Saud Habib,IOA,male,1979-10-04,1.77,85.0,shooting,0,0,0 -530860018,Saul Craviotto,ESP,male,1984-11-03,1.92,98.0,canoe,1,0,1 -538588992,Saul Gutierrez Macedo,MEX,male,1992-12-28,1.91,68.0,taekwondo,0,0,0 -627929661,Saula Waqa,FIJ,male,1995-10-12,1.96,96.0,football,0,0,0 -818121664,Saulius Ritter,LTU,male,1988-08-23,2.02,110.0,rowing,0,1,0 -227050501,Sava Randelovic,SRB,male,1993-07-17,1.93,98.0,aquatics,1,0,0 -649115184,Savannah Marshall,GBR,female,1991-05-19,1.81,,boxing,0,0,0 -692221197,Savenaca Rawaca,FIJ,male,1991-08-20,1.89,105.0,rugby sevens,1,0,0 -684153467,Saviour Godwin,NGR,male,1996-08-22,1.72,71.0,football,0,0,0 -202363602,Savita Savita,IND,female,1990-07-11,1.71,59.0,hockey,0,0,0 -309381381,Savitree Amitrapai,THA,female,1988-11-19,1.64,56.0,badminton,0,0,0 -194183434,Sawan Serasinghe,AUS,male,1994-02-21,1.78,79.0,badminton,0,0,0 -436141773,Saylom Ardee,THA,male,1986-07-07,1.75,,boxing,0,0,0 -185315649,Sayuri Sugimoto,JPN,female,1996-01-25,1.67,48.0,gymnastics,0,0,0 -488272303,Saziye Ivegin Uner,TUR,female,1982-02-08,1.8,69.0,basketball,0,0,0 -752901474,Scarleth Elizabeth Mercado Lopez,NCA,female,1996-08-09,1.52,53.0,weightlifting,0,0,0 -783419907,Scott Bowden,AUS,male,1995-04-04,1.75,65.0,cycling,0,0,0 -815590991,Scott Curry,NZL,male,1988-05-17,1.93,100.0,rugby sevens,0,0,0 -733105712,Scott Durant,GBR,male,1988-02-12,1.96,96.0,rowing,1,0,0 -128810768,Scott Evans,IRL,male,1987-09-26,1.8,78.0,badminton,0,0,0 -758417011,Scott Hend,AUS,male,1973-08-15,1.83,82.0,golf,0,0,0 -153971498,Scott Keach,AUS,male,1965-04-21,1.85,80.0,equestrian,0,0,0 -744441795,Scott Morgan,CAN,male,1989-06-20,1.6,62.0,gymnastics,0,0,0 -880417665,Scott Tupper,CAN,male,1986-12-16,1.79,79.0,hockey,0,0,0 -652324517,Scott Westcott,AUS,male,1975-09-25,1.79,64.0,athletics,0,0,0 -964785333,Se Gwang Ri,PRK,male,1985-01-21,1.55,54.0,gymnastics,1,0,0 -715580856,Seabelo Senatla,RSA,male,1993-02-10,1.77,82.0,rugby sevens,0,0,1 -947393781,Seamus Power,IRL,male,1987-03-04,1.92,90.0,golf,0,0,0 -78197579,Sean Furey,USA,male,1982-08-31,1.88,90.0,athletics,0,0,0 -599859960,Sean Michael Gunn,ZIM,male,1993-12-23,1.81,84.0,aquatics,0,0,0 -462481375,Sean Ryan,USA,male,1992-08-13,1.91,77.0,aquatics,0,0,0 -915415519,Sean Safo-Antwi,GHA,male,1990-10-31,1.71,69.0,athletics,0,0,0 -644711592,Seavmey Sorn,CAM,female,1995-09-14,1.83,73.0,taekwondo,0,0,0 -188093060,Sebastiaan Verschuren,NED,male,1988-10-07,1.92,84.0,aquatics,0,0,0 -746518634,Sebastian Brendel,GER,male,1988-03-12,1.92,92.0,canoe,2,0,0 -782106653,Sebastian Fleischer,DEN,male,1993-12-26,1.79,74.0,sailing,0,0,0 -947656351,Sebastian Kerk,GER,male,1994-04-17,1.84,76.0,football,0,0,0 -821529588,Sebastian Kuntschik,AUT,male,1988-09-23,1.82,79.0,shooting,0,0,0 -987203474,Sebastian Martos,ESP,male,1989-06-20,1.78,63.0,athletics,0,0,0 -409782771,Sebastian Morales,COL,male,1994-08-22,1.71,65.0,aquatics,0,0,0 -857597893,Sebastian Perez,COL,male,1993-03-29,1.77,69.0,football,0,0,0 -806780265,Sebastian Rodger,GBR,male,1991-06-29,1.8,70.0,athletics,0,0,0 -502044930,Sebastian Rossi,ARG,male,1992-02-12,1.75,77.0,canoe,0,0,0 -481454192,Sebastian Seidl,GER,male,1990-07-12,1.74,70.0,judo,0,0,0 -62067112,Sebastian Simonet,ARG,male,1986-05-12,1.89,93.0,handball,0,0,0 -526844987,Sebastian Sole,ARG,male,1991-06-12,2.0,94.0,volleyball,0,0,0 -628729080,Sebastian Starke Hedlund,SWE,male,1995-04-05,1.85,77.0,football,0,0,0 -948050872,Sebastian Villa,COL,male,1992-02-21,1.64,59.0,aquatics,0,0,0 -50163222,Sebastien Combot,FRA,male,1987-02-09,1.69,64.0,canoe,0,0,0 -980137222,Sebastien Daniel Rousseau,RSA,male,1990-09-10,1.9,86.0,aquatics,0,0,0 -266769451,Sebastien Dockier,BEL,male,1989-12-28,1.75,74.0,hockey,0,1,0 -394906544,Sebastien Jouve,FRA,male,1982-12-08,1.86,90.0,canoe,0,0,0 -696726152,Sebastien Martiny,FRA,male,1985-02-27,1.72,65.0,gymnastics,0,0,0 -421109661,Sebastien Reichenbach,SUI,male,1989-05-28,1.78,65.0,cycling,0,0,0 -755469189,Sebastien Schneiter,SUI,male,1995-09-24,1.76,72.0,sailing,0,0,0 -555941174,Sebnem Nezahat Kimyacioglu,TUR,female,1983-06-14,1.82,80.0,basketball,0,0,0 -471386171,Seda Tutkhalian,RUS,female,1999-07-15,1.42,35.0,gymnastics,0,1,0 -661458776,Seema Punia,IND,female,1983-07-27,1.82,92.0,athletics,0,0,0 -477722646,Segun Toriola,NGR,male,1974-09-18,1.72,70.0,table tennis,0,0,0 -64323819,Segundo Jami,ECU,male,1986-05-12,1.65,54.0,athletics,0,0,0 -365917008,Seham Elsawalhy,EGY,female,1991-04-14,1.75,65.0,taekwondo,0,0,0 -833886361,Sehyeon An,KOR,female,1995-10-14,1.68,56.0,aquatics,0,0,0 -386696464,Sei Muroya,JPN,male,1994-04-05,1.74,65.0,football,0,0,0 -881914341,Seiichiro Nakagawa,JPN,male,1979-06-07,1.74,78.0,cycling,0,0,0 -450765459,Seimone Augustus,USA,female,1984-04-30,1.83,74.0,basketball,1,0,0 -961376949,Seito Yamamoto,JPN,male,1992-03-11,1.81,70.0,athletics,0,0,0 -879945924,Seiya Adachi,JPN,male,1995-06-24,1.72,67.0,aquatics,0,0,0 -758020443,Seiyoung Kim,KOR,female,1993-01-21,1.63,68.0,golf,0,0,0 -548391655,Sekou Kaba,CAN,male,1990-08-25,1.88,88.0,athletics,0,0,0 -438328823,Selcuk Cebi,TUR,male,1982-06-03,1.69,76.0,wrestling,0,0,0 -23409142,Selcuk Eker,TUR,male,1991-06-12,1.69,,boxing,0,0,0 -187017492,Selena Piek,NED,female,1991-09-30,1.66,59.0,badminton,0,0,0 -121821838,Selim Yasar,TUR,male,1990-02-20,1.79,86.0,wrestling,0,1,0 -429028627,Selin Oruz,GER,female,1997-02-05,1.72,60.0,hockey,0,0,1 -280240800,Selina Buchel,SUI,female,1991-07-26,1.67,57.0,athletics,0,0,0 -837915273,Selina Gschwandtner,GER,female,1994-05-18,1.66,64.0,shooting,0,0,0 -387125204,Selma Kajan,AUS,female,1991-07-30,1.69,52.0,athletics,0,0,0 -275237162,Semen Makovich,RUS,male,1995-07-13,1.8,76.0,aquatics,0,0,0 -9122032,Semi Kunatani,FIJ,male,1990-10-27,1.92,98.0,rugby sevens,1,0,0 -35226441,Semoy Hackett,TTO,female,1988-11-27,1.73,61.0,athletics,0,0,0 -206262070,Sen Qiao,CHN,male,1990-05-14,1.97,85.0,taekwondo,0,0,0 -806870975,Sena Takano,JPN,female,1998-03-01,1.65,65.0,sailing,0,0,0 -743927516,Senbere Teferi,ETH,female,1995-05-03,,,athletics,0,0,0 -652894591,Senna Deriks,BEL,female,2000-12-30,1.54,46.0,gymnastics,0,0,0 -846363781,Seona Hwang,KOR,female,1989-09-16,1.63,55.0,fencing,0,0,0 -961455216,Seongeun Hwang,KOR,female,1993-02-28,1.65,55.0,shooting,0,0,0 -221532533,Seongyeon Kim,KOR,female,1991-04-16,1.75,70.0,judo,0,0,0 -4661115,Seoyeong Kim,KOR,female,1994-03-17,1.63,51.0,aquatics,0,0,0 -655555498,Seppe van Holsbeke,BEL,male,1988-01-19,1.98,93.0,fencing,0,0,0 -252015329,Ser-Od Bat-Ochir,MGL,male,1981-10-07,1.69,61.0,athletics,0,0,0 -86771577,Seref Osmanoglu,TUR,male,1989-01-02,1.83,75.0,athletics,0,0,0 -310865074,Seren Bundy-Davies,GBR,female,1994-12-30,1.75,63.0,athletics,0,0,0 -24929950,Serena Ortolani,ITA,female,1987-01-07,1.87,63.0,volleyball,0,0,0 -379931177,Serena Williams,USA,female,1981-09-26,1.78,72.0,tennis,0,0,0 -297370521,Serge Gnabry,GER,male,1995-07-14,1.73,80.0,football,0,1,0 -270678554,Serge Michel,GER,male,1988-09-10,1.81,,boxing,0,0,0 -612124451,Serge Pauwels,BEL,male,1983-11-21,1.77,64.0,cycling,0,0,0 -151596328,Sergei Chernetski,RUS,male,1990-04-09,1.76,63.0,cycling,0,0,0 -333027986,Sergei Komissarov,RUS,male,1987-12-03,1.84,81.0,sailing,0,0,0 -594962081,Sergey Grankin,RUS,male,1985-01-21,1.95,96.0,volleyball,0,0,0 -549185484,Sergey Kamenskiy,RUS,male,1987-10-07,1.78,73.0,shooting,0,1,0 -478548021,Sergey Khodos,RUS,male,1986-07-14,1.97,87.0,fencing,0,0,0 -645801054,Sergey Richter,ISR,male,1989-04-23,1.8,83.0,shooting,0,0,0 -536871163,Sergey Semenov,RUS,male,1995-08-10,1.87,130.0,wrestling,0,0,1 -428021542,Sergey Tetyukhin,RUS,male,1975-09-23,1.97,89.0,volleyball,0,0,0 -890081492,Serghei Cechir,MDA,male,1990-10-15,1.72,69.0,weightlifting,0,0,0 -832276457,Serghei Marghiev,MDA,male,1992-11-06,1.95,97.0,athletics,0,0,0 -137541951,Serghei Tarnovschi,MDA,male,1997-06-24,1.78,80.0,canoe,0,0,1 -825721013,Serghei Tvetcov,ROU,male,1988-12-29,1.78,70.0,cycling,0,0,0 -240732198,Sergi Enrique,ESP,male,1987-09-22,1.74,69.0,hockey,0,0,0 -804903425,Sergii Frolov,UKR,male,1992-04-14,1.74,74.0,aquatics,0,0,0 -774141587,Sergii Tokarnytskyi,KAZ,male,1993-02-01,1.88,102.0,canoe,0,0,0 -954264527,Sergio Alvarez Moya,ESP,male,1985-01-07,1.74,74.0,equestrian,0,0,0 -558359788,Sergio Dutra Santos,BRA,male,1975-10-15,1.84,78.0,volleyball,1,0,0 -790285042,Sergio Fernandez,ESP,male,1993-04-01,1.86,78.0,athletics,0,0,0 -952347029,Sergio Garcia,ESP,male,1980-01-09,1.76,80.0,golf,0,0,0 -157650129,Sergio Llull,ESP,male,1987-11-15,1.9,91.0,basketball,0,0,1 -848761398,Sergio Luis Henao Montoya,COL,male,1987-12-10,1.7,61.0,cycling,0,0,0 -390699178,Sergio Oliveira,POR,male,1992-06-02,1.8,80.0,football,0,0,0 -783188500,Sergio Pessoa,CAN,male,1988-09-03,1.87,60.0,judo,0,0,0 -259358477,Sergio Reynaldo Gonzalez Bayard,CUB,male,1990-06-20,1.95,99.0,volleyball,0,0,0 -156131114,Sergio Rodriguez,ESP,male,1986-06-12,1.91,80.0,basketball,0,0,1 -534656783,Sergio Sasaki,BRA,male,1992-03-31,1.61,64.0,gymnastics,0,0,0 -92473676,Sergio Vieira,POR,male,1976-02-20,1.74,60.0,athletics,0,0,0 -413925924,Sergiu Oleinic,POR,male,1985-12-25,1.65,68.0,judo,0,0,0 -642221222,Sergiu Toma,UAE,male,1987-01-29,1.8,81.0,judo,0,0,1 -993364632,Serguey Torres,CUB,male,1987-01-20,1.75,74.0,canoe,0,0,0 -638712579,Serhiy Budza,UKR,male,1984-12-06,1.8,75.0,athletics,0,0,0 -76496713,Serhiy Kulish,UKR,male,1993-04-17,1.76,64.0,shooting,0,1,0 -612592716,Serhiy Smelyk,UKR,male,1987-04-19,1.78,72.0,athletics,0,0,0 -544720324,Serik Mirbekov,UZB,male,1988-06-09,1.76,83.0,canoe,0,0,0 -983390495,Servet Tazegul,TUR,male,1988-09-26,1.76,68.0,taekwondo,0,0,0 -631502285,Setareki Hughes,FIJ,male,1995-06-08,1.75,73.0,football,0,0,0 -13474372,Seth Weil,USA,male,1987-03-09,1.99,97.0,rowing,0,0,0 -526516331,Seul-Ki Ahn,KOR,female,1992-05-29,1.61,46.0,athletics,0,0,0 -860133860,Seulchan Lee,KOR,male,1993-08-15,1.72,65.0,football,0,0,0 -732031439,Seung Chan Shin,KOR,female,1994-12-06,1.73,70.0,badminton,0,0,1 -714388590,Seunga Park,KOR,female,1991-04-16,1.68,61.0,hockey,0,0,0 -863636948,Seunghwa Jung,KOR,male,1981-03-27,1.8,80.0,fencing,0,0,0 -598701842,Seunghyun Jung,KOR,male,1994-04-03,1.88,74.0,football,0,0,0 -215880405,Seunghyun Yun,KOR,male,1994-06-01,1.93,75.0,athletics,0,0,0 -299216431,Seungsu Lee,KOR,male,1990-07-20,1.78,81.0,judo,0,0,0 -319207842,Seungwoo Han,KOR,male,1983-07-03,1.73,86.0,shooting,0,0,0 -950256788,Seungwoo Ryu,KOR,male,1993-12-17,1.72,67.0,football,0,0,0 -739050983,Seungyun Lee,KOR,male,1995-04-18,1.74,80.0,archery,1,0,0 -663165360,Severiano van Ass,NED,male,1992-04-10,1.78,70.0,hockey,0,0,0 -243524329,Severo Jesus Jurado Lopez,ESP,male,1988-09-09,1.72,71.0,equestrian,0,0,0 -615481668,Seye Ogunlewe,NGR,male,1991-08-30,1.89,84.0,athletics,0,0,0 -862783506,Seyed Mohammad Mousavi Eraghi,IRI,male,1987-08-22,2.03,86.0,volleyball,0,0,0 -817723961,Shadae Lawrence,JAM,female,1995-12-31,1.73,84.0,athletics,0,0,0 -781173388,Shadrack Kipchirchir,USA,male,1989-02-22,1.73,53.0,athletics,0,0,0 -753177730,Shahar Zubari,ISR,male,1986-09-01,1.77,68.0,sailing,0,0,0 -188476296,Shahram Mahmoudi,IRI,male,1988-07-20,1.98,95.0,volleyball,0,0,0 -630214839,Shaimaa Haridy,EGY,female,1991-01-01,1.6,124.0,weightlifting,0,0,0 -926666572,Shakhboz Kholmurzaev,UZB,male,1996-02-26,1.88,78.0,rowing,0,0,0 -294616056,Shakhobidin Zoirov,UZB,male,1993-03-03,1.69,,boxing,1,0,0 -295291162,Shakhram Giyasov,UZB,male,1993-07-07,1.74,,boxing,0,1,0 -107756226,Shakhzodbek Sabirov,UZB,male,1993-05-29,1.83,81.0,judo,0,0,0 -907513797,Shakira Baker,NZL,female,1992-01-04,1.72,89.0,rugby sevens,0,1,0 -682806875,Shakur Stevenson,USA,male,1997-06-28,1.73,,boxing,0,1,0 -591949071,Shalane Flanagan,USA,female,1981-07-08,1.66,47.0,athletics,0,0,0 -289631929,Shallon Olsen,CAN,female,2000-07-10,1.58,52.0,gymnastics,0,0,0 -551301673,Shamoli Ray,BAN,female,1994-04-05,,,archery,0,0,0 -722411313,Shane O'Donoghue,IRL,male,1992-11-24,1.83,80.0,hockey,0,0,0 -827053441,Shane Rose,AUS,male,1973-04-24,1.81,80.0,equestrian,0,0,1 -123371543,Shane Ryan,IRL,male,1994-01-27,1.98,89.0,aquatics,0,0,0 -304415448,Shaneel Naidu,FIJ,male,1995-03-28,1.8,77.0,football,0,0,0 -690199324,Shani Bloch,ISR,female,1979-03-06,1.62,59.0,cycling,0,0,0 -838944632,Shanice Craft,GER,female,1993-05-15,1.85,93.0,athletics,0,0,0 -672509994,Shanieka Thomas,JAM,female,1992-02-02,1.81,66.0,athletics,0,0,0 -368129090,Shanne Braspennincx,NED,female,1991-05-18,1.72,78.0,cycling,0,0,0 -19644871,Shannon Izar,FRA,female,1993-05-08,1.72,68.0,rugby sevens,0,0,0 -861802437,Shannon McCurley,IRL,female,1992-04-26,1.63,65.0,cycling,0,0,0 -364894953,Shannon Parry,AUS,female,1989-10-27,1.7,70.0,rugby sevens,1,0,0 -165793657,Shannon Rowbury,USA,female,1984-09-19,1.66,52.0,athletics,0,0,0 -297515398,Shanshan Feng,CHN,female,1989-08-05,1.72,85.0,golf,0,0,1 -70444302,Shanshan Li,CHN,female,1987-03-03,1.78,62.0,basketball,0,0,0 -124085107,Shanshan Liu,CHN,female,1992-03-16,1.69,65.0,football,0,0,0 -620111487,Shanshan Wang,CHN,female,1990-01-27,1.69,57.0,football,0,0,0 -843106594,Shaoqing Hua,CHN,female,1994-02-12,1.65,50.0,athletics,0,0,0 -353324453,Shaquania Dorsett,BAH,female,1997-09-16,,,athletics,0,0,0 -625323278,Shara Proctor,GBR,female,1988-09-16,1.73,60.0,athletics,0,0,0 -170273489,Shara Venegas,PUR,female,1992-09-18,1.73,68.0,volleyball,0,0,0 -198250006,Sharath Kamal Achanta,IND,male,1982-07-12,1.86,85.0,table tennis,0,0,0 -492459833,Sharif Sharifov,AZE,male,1988-11-11,1.8,97.0,wrestling,0,0,1 -501621761,Sharni Williams,AUS,female,1988-03-02,1.67,79.0,rugby sevens,1,0,0 -729778002,Sharolyn Scott,CRC,female,1983-10-27,1.68,63.0,athletics,0,0,0 -179932407,Sharon Acevedo,COL,female,1993-03-05,1.7,61.0,rugby sevens,0,0,0 -509787602,Sharon Firisua,SOL,female,1993-12-15,,,athletics,0,0,0 -370330019,Sharon van rouwendaal,NED,female,1993-09-09,1.73,65.0,aquatics,1,0,0 -371532634,Shaun Keeling,RSA,male,1987-01-21,1.96,94.0,rowing,0,1,0 -810913943,Shaun Kirkham,NZL,male,1992-07-24,1.91,96.0,rowing,0,0,0 -110430107,Shaunae Miller,BAH,female,1994-04-15,1.85,69.0,athletics,1,0,0 -891682431,Shavez Hart,BAH,male,1992-09-06,,,athletics,0,0,0 -421736590,Shawn Dingilius-Wallace,PLW,male,1994-07-26,1.84,93.0,aquatics,0,0,0 -239879514,Shawnacy Barber,CAN,male,1994-05-27,1.87,82.0,athletics,0,0,0 -906208615,Shay Neal,NZL,male,1990-06-04,1.76,75.0,hockey,0,0,0 -988651056,Shea McALEESE,NZL,male,1984-08-07,1.77,82.0,hockey,0,0,0 -798829173,Shehzana Anwar,KEN,female,1989-08-21,1.6,55.0,archery,0,0,0 -70622338,Sheikh Ali Al Thani,QAT,male,1982-09-01,1.94,73.0,equestrian,0,0,0 -323305837,Sheila Chajira,KEN,female,1993-12-20,1.65,73.0,rugby sevens,0,0,0 -148122474,Sheila Makoto,ZIM,female,1990-01-14,1.58,59.0,football,0,0,0 -994348153,Sheilla Castro de Paula Blassioli,BRA,female,1983-07-01,1.85,65.0,volleyball,0,0,0 -3895186,Shelayna Oskan-Clarke,GBR,female,1990-01-20,1.72,64.0,athletics,0,0,0 -586628246,Shelbi Vaughan,USA,female,1994-08-24,1.83,127.0,athletics,0,0,0 -362859708,Shelby Houlihan,USA,female,1993-02-08,1.61,53.0,athletics,0,0,0 -963097425,Shelina Zadorsky,CAN,female,1992-10-24,1.72,67.0,football,0,0,1 -425110382,Shelley Marie Watts,AUS,female,1987-08-10,1.64,,boxing,0,0,0 -80192943,Shelly Francis,USA,female,1958-12-16,1.58,65.0,equestrian,0,0,0 -422183350,Shelly-Ann Fraser-Pryce,JAM,female,1986-12-27,1.52,57.0,athletics,0,1,1 -256335420,Sheng Lei,CHN,male,1984-03-07,1.93,77.0,fencing,0,0,0 -830512513,Sheng Mu Lee,TPE,male,1986-10-03,1.79,71.0,badminton,0,0,0 -551099828,Shengbo Zhao,CHN,male,1982-04-14,1.75,81.0,shooting,0,0,0 -693828504,Shengfeng Bi,CHN,male,1989-01-28,1.83,87.0,wrestling,0,0,0 -94758945,Sheniqua Ferguson,BAH,female,1989-11-24,1.71,57.0,athletics,0,0,0 -451530720,Sherali Juraev,UZB,male,1986-12-13,1.82,90.0,judo,0,0,0 -225168499,Shericka Jackson,JAM,female,1994-07-16,1.73,61.0,athletics,0,1,1 -352645279,Sherine Elzeiny,EGY,female,1991-02-23,1.6,50.0,gymnastics,0,0,0 -781234683,Sherko Kareem,IRQ,male,1996-05-25,1.74,70.0,football,0,0,0 -739754145,Shermaine Williams,JAM,female,1990-02-04,1.76,65.0,athletics,0,0,0 -162371447,Shih-Chia Lin,TPE,female,1993-05-20,1.65,63.0,archery,0,0,1 -316407203,Shih-Chieh Chen,TPE,male,1989-11-27,1.9,152.0,weightlifting,0,0,0 -638278169,Shih-Feng Huang,TPE,male,1992-03-02,1.78,88.0,athletics,0,0,0 -719261791,Shiho Nishioka,JPN,female,1989-02-23,1.69,60.0,fencing,0,0,0 -2696635,Shiho Oyama,JPN,female,1977-05-25,1.68,62.0,golf,0,0,0 -450566696,Shijia Wang,CHN,female,1993-08-25,1.74,63.0,aquatics,0,0,0 -139152565,Shijie Qieyang,CHN,female,1990-11-11,1.6,45.0,athletics,0,0,0 -48818974,Shimaa Hashad,EGY,female,1981-04-21,1.66,57.0,shooting,0,0,0 -702145103,Shingo Katayama,JPN,male,1973-01-31,1.71,72.0,golf,0,0,0 -814685422,Shinnosuke Nakatani,JPN,male,1996-03-24,1.84,79.0,football,0,0,0 -213190977,Shinobu Ota,JPN,male,1993-12-28,1.65,65.0,wrestling,0,1,0 -169399991,Shinri Shioura,JPN,male,1991-11-26,1.88,90.0,aquatics,0,0,0 -224547831,Shinya Yajima,JPN,male,1994-01-18,1.71,67.0,football,0,0,0 -979127201,Shinzo Koroki,JPN,male,1986-07-31,1.75,72.0,football,0,0,0 -109446999,Shira Rishony,ISR,female,1991-02-21,1.5,48.0,judo,0,0,0 -229032173,Shirin Akter,BAN,female,1994-10-12,,,athletics,0,0,0 -200540934,Shitaye Eshete,BRN,female,1990-05-21,1.64,51.0,athletics,0,0,0 -652257369,Shiva Thapa,IND,male,1993-12-08,1.69,,boxing,0,0,0 -476256445,Shivani Shivani,IND,female,1997-09-27,1.63,60.0,aquatics,0,0,0 -720527022,Shiwe Nogwanya,RSA,female,1994-03-07,1.68,61.0,football,0,0,0 -207795938,Shiwen Liu,CHN,female,1991-04-12,1.6,54.0,table tennis,1,0,0 -538468544,Shiwen Ye,CHN,female,1996-03-01,1.72,71.0,aquatics,0,0,0 -36387534,Shiying Liu,CHN,female,1993-09-24,1.79,76.0,athletics,0,0,0 -880751102,Shlomi Haimy,ISR,male,1989-06-19,1.67,58.0,cycling,0,0,0 -522662655,Shmagi Bolkvadze,GEO,male,1994-07-26,1.7,66.0,wrestling,0,0,1 -629603163,Sho Kawamoto,JPN,male,1993-03-01,1.75,68.0,athletics,0,0,0 -964540128,Sho Sakai,JPN,male,1992-08-22,1.71,58.0,aquatics,0,0,0 -189386940,Sho Sasaki,JPN,male,1982-06-30,1.72,74.0,badminton,0,0,0 -691508623,Shohei Iwamoto,JPN,male,1989-08-23,1.78,68.0,modern pentathlon,0,0,0 -784767531,Shohei Ono,JPN,male,1992-02-03,1.7,73.0,judo,1,0,0 -474547184,Shohei Toyoshima,JPN,male,1989-01-09,1.75,87.0,rugby sevens,0,0,0 -976460404,Shona McCALLIN,GBR,female,1992-05-18,1.65,69.0,hockey,1,0,0 -920840087,Shona Thorburn,CAN,female,1982-08-07,1.75,70.0,basketball,0,0,0 -119951308,Shota Hazui,JPN,male,1986-09-30,1.77,77.0,aquatics,0,0,0 -592468462,Shota Iizuka,JPN,male,1991-06-25,1.85,80.0,athletics,0,1,0 -765026590,Shota Sasaki,JPN,male,1993-01-10,1.65,64.0,canoe,0,0,0 -752540261,Shoya Nakajima,JPN,male,1994-08-23,1.64,64.0,football,0,0,0 -778116508,Shu-Ching Hsu,TPE,female,1991-05-09,1.6,53.0,weightlifting,1,0,0 -129298920,Shuai Peng,CHN,female,1986-01-08,1.73,65.0,tennis,0,0,0 -179157979,Shuai Zhang,CHN,female,1989-01-21,1.77,66.0,tennis,0,0,0 -990347414,Shuai Zhao,CHN,male,1995-08-15,1.88,63.0,taekwondo,1,0,0 -72738698,Shuang Wang,CHN,female,1995-01-23,1.65,59.0,football,0,0,0 -843868379,Shudi Deng,CHN,male,1991-09-10,1.63,58.0,gymnastics,0,0,1 -293576739,Shuijiao Wu,CHN,female,1991-06-19,1.61,53.0,athletics,0,0,0 -972669681,Shun Wang,CHN,male,1994-02-11,1.91,81.0,aquatics,0,0,1 -285629214,Shun Xie Teo,SIN,female,1988-09-30,1.68,63.0,shooting,0,0,0 -833912254,Shuo Cao,CHN,male,1991-10-08,1.8,77.0,athletics,0,0,0 -389167708,Shuyin Zheng,CHN,female,1994-05-01,1.88,75.0,taekwondo,1,0,0 -126862439,Si Mohamed Ketbi,BEL,male,1997-12-27,1.8,62.0,taekwondo,0,0,0 -676275478,Siarhei Kalamoyets,BLR,male,1989-08-11,1.92,110.0,athletics,0,0,0 -512714002,Sibusiso Matsenjwa,SWZ,male,1988-05-02,1.8,82.0,athletics,0,0,0 -433307287,Sibusiso Nzima,RSA,male,1986-11-23,1.64,57.0,athletics,0,0,0 -873507500,Sid Ali Boudina,ALG,male,1990-05-07,1.78,73.0,rowing,0,0,0 -171735366,Siddikur Rahman,BAN,male,1984-11-20,1.65,69.0,golf,0,0,0 -696400766,Sideris Tasiadis,GER,male,1990-05-07,1.79,79.0,canoe,0,0,0 -997380920,Sidni Hoxha,ALB,male,1992-06-01,1.93,87.0,aquatics,0,0,0 -649779074,Sifan Hassan,NED,female,1993-01-01,1.7,49.0,athletics,0,0,0 -219470376,Signe Marie Fidge Store,NOR,female,1995-08-23,,,wrestling,0,0,0 -794983348,Siham Hilali,MAR,female,1986-05-02,1.6,48.0,athletics,0,0,0 -299706553,Sijing Huang,CHN,female,1996-01-08,1.9,82.0,basketball,0,0,0 -726164746,Siling Yi,CHN,female,1989-05-06,1.65,51.0,shooting,0,0,1 -486158932,Silvan Dillier,SUI,male,1990-08-03,1.83,73.0,cycling,0,0,0 -74936467,Silvano Chesani,ITA,male,1988-07-17,1.91,83.0,athletics,0,0,0 -348354377,Silvia Dominguez,ESP,female,1987-01-31,1.67,64.0,basketball,0,1,0 -84714321,Silvia Navarro,ESP,female,1979-03-20,1.69,62.0,handball,0,0,0 -523095646,Silvia Paredes,ECU,female,1983-01-23,1.64,57.0,athletics,0,0,0 -85492860,Silvia Sicouri,ITA,female,1987-09-27,1.72,65.0,sailing,0,0,0 -688147154,Silvia Valsecchi,ITA,female,1982-07-19,1.6,60.0,cycling,0,0,0 -978974427,Silvia Zennaro,ITA,female,1989-10-26,1.68,72.0,sailing,0,0,0 -593049053,Silvia di Pietro,ITA,female,1993-04-06,1.68,58.0,aquatics,0,0,0 -856209845,Silvija Popovic,SRB,female,1986-03-15,1.78,65.0,volleyball,0,0,0 -875603204,Silvio Fernandez,VEN,male,1979-01-09,1.9,80.0,fencing,0,0,0 -593561790,Silvio Heinevetter,GER,male,1984-10-21,1.94,99.0,handball,0,0,1 -506472821,Sime Fantela,CRO,male,1986-01-19,1.83,67.0,sailing,1,0,0 -578898700,Simeon Chamov,BUL,male,1990-12-24,1.8,,boxing,0,0,0 -160713644,Simione Tamanisau,FIJ,male,1982-06-05,1.87,88.0,football,0,0,0 -242744402,Simon Andreassen,DEN,male,1997-09-30,1.77,68.0,cycling,0,0,0 -511053729,Simon Child,NZL,male,1988-04-16,1.86,80.0,hockey,0,0,0 -910071832,Simon Clarke,AUS,male,1986-07-18,,,cycling,0,0,0 -948358366,Simon Gauzy,FRA,male,1994-10-25,1.82,77.0,table tennis,0,0,0 -585225700,Simon Geschke,GER,male,1986-03-13,1.7,64.0,cycling,0,0,0 -828900284,Simon Gougnard,BEL,male,1991-01-17,1.87,84.0,hockey,0,1,0 -32628932,Simon Mantell,GBR,male,1984-04-24,1.84,81.0,hockey,0,0,0 -618197194,Simon Martirosyan,ARM,male,1997-02-17,1.81,105.0,weightlifting,0,1,0 -894353536,Simon Niepmann,SUI,male,1985-08-02,1.8,71.0,rowing,1,0,0 -572516555,Simon Orchard,AUS,male,1986-07-09,1.86,82.0,hockey,0,0,0 -129979130,Simon Razgor,SLO,male,1985-09-18,1.83,82.0,handball,0,0,0 -37763336,Simon Schuerch,SUI,male,1990-12-02,1.86,75.0,rowing,1,0,0 -671775159,Simon Sjodin,SWE,male,1986-10-04,1.92,85.0,aquatics,0,0,0 -568958895,Simon Spilak,SLO,male,1986-06-23,1.77,68.0,cycling,0,0,0 -673967464,Simon Tibbling,SWE,male,1994-09-07,1.74,68.0,football,0,0,0 -674891863,Simon Wachira,KEN,male,1984-05-06,1.7,65.0,athletics,0,0,0 -137917095,Simon Werro,SUI,male,1989-12-09,1.78,74.0,canoe,0,0,0 -848550090,Simon Yacoub,PLE,male,1989-06-09,1.66,60.0,judo,0,0,0 -727296883,Simona Baumrtova,CZE,female,1991-08-24,1.76,66.0,aquatics,0,0,0 -422875239,Simona Castro,CHI,female,1989-01-11,1.6,54.0,gymnastics,0,0,0 -901255564,Simona Frapporti,ITA,female,1988-07-14,1.77,65.0,cycling,0,0,0 -617360976,Simona Gherman,ROU,female,1985-04-12,1.7,58.0,fencing,1,0,0 -626251390,Simona Krupeckaite,LTU,female,1982-12-13,1.7,70.0,cycling,0,0,0 -871511987,Simona Pop,ROU,female,1988-12-25,1.78,62.0,fencing,1,0,0 -481317414,Simonas Bilis,LTU,male,1993-11-11,1.98,100.0,aquatics,0,0,0 -770111957,Simone Biles,USA,female,1997-03-14,1.45,47.0,gymnastics,4,0,1 -718132328,Simone Buti,ITA,male,1983-09-19,2.06,100.0,volleyball,0,1,0 -303317190,Simone Christensen,DEN,female,1994-02-10,1.7,68.0,cycling,0,0,0 -218481152,Simone Consonni,ITA,male,1994-09-12,1.65,60.0,cycling,0,0,0 -322780969,Simone Facey,JAM,female,1985-05-07,1.62,53.0,athletics,0,1,0 -253019440,Simone Giannelli,ITA,male,1996-08-09,1.98,92.0,volleyball,0,1,0 -286644337,Simone Laudehr,GER,female,1986-07-12,1.74,56.0,football,0,0,0 -973414226,Simone Manuel,USA,female,1996-08-02,1.78,72.0,aquatics,2,2,0 -195420370,Simone Ruffini,ITA,male,1989-12-07,1.73,67.0,aquatics,0,0,0 -392271182,Simone Sabbioni,ITA,male,1996-10-03,1.85,79.0,aquatics,0,0,0 -828729175,Simone Venier,ITA,male,1984-08-26,1.96,94.0,rowing,0,0,0 -166532911,Simoya Campbell,JAM,female,1994-03-01,1.68,58.0,athletics,0,0,0 -203750302,Simplice Fotsala,CMR,male,1989-05-09,1.54,,boxing,0,0,0 -520685692,Simplice Ribouem,AUS,male,1982-12-05,1.73,93.0,weightlifting,0,0,0 -199944745,Sin Lam Sonia Lo,HKG,female,1992-12-18,1.66,59.0,sailing,0,0,0 -131138737,Sina Tkaltschewitsch,GER,female,1999-05-05,1.6,44.0,gymnastics,0,0,0 -703661440,Sinead Lynch,IRL,female,1976-09-30,1.72,57.0,rowing,0,0,0 -303411588,Sinphet Kruaithong,THA,male,1995-08-22,1.59,55.0,weightlifting,0,0,1 -434262676,Sinta Ozolina,LAT,female,1988-02-26,1.86,77.0,athletics,0,0,0 -313529097,Siobhan Haughey,HKG,female,1997-10-31,1.77,64.0,aquatics,0,0,0 -306151586,Siobhan-Marie O'Connor,GBR,female,1995-11-29,1.73,64.0,aquatics,0,1,0 -543220260,Sione Molia,NZL,male,1993-09-05,1.86,95.0,rugby sevens,0,0,0 -498918427,Siraba Dembele,FRA,female,1986-06-28,1.72,64.0,handball,0,1,0 -112175885,Siri Arun Budcharern,LAO,female,2002-01-12,1.66,63.0,aquatics,0,0,0 -541913883,Siripon Kaewduang-Ngam,THA,female,1994-07-27,1.67,50.0,sailing,0,0,0 -928639539,Siripuch Gulnoi,THA,female,1993-07-17,1.57,63.0,weightlifting,0,0,0 -274861509,Sirish Gurung,NEP,male,1998-08-11,1.58,65.0,aquatics,0,0,0 -100364594,Sisila Seavula,FIJ,female,1995-11-15,,,athletics,0,0,0 -983890439,Sitora Hamidova,UZB,female,1989-05-12,1.64,50.0,athletics,0,0,0 -374168059,Siueni Filimone,TGA,male,1994-08-19,1.73,69.0,athletics,0,0,0 -740126529,Siyao Shu,CHN,female,1992-09-12,1.67,52.0,gymnastics,0,0,0 -325216838,Sjef van den Berg,NED,male,1995-04-14,1.83,75.0,archery,0,0,0 -230251479,Sladana Perunovic,MNE,female,1984-03-26,1.7,58.0,athletics,0,0,0 -399380442,Slawomir Szmal,POL,male,1978-10-02,1.86,96.0,handball,0,0,0 -564349535,Sloane Stephens,USA,female,1993-03-20,1.73,64.0,tennis,0,0,0 -682518926,Slobodan Nikic,SRB,male,1983-01-25,1.97,106.0,aquatics,1,0,0 -466638845,Slobodan Soro,BRA,male,1978-12-23,1.96,100.0,aquatics,0,0,0 -655259381,Snjezana Pejcic,CRO,female,1982-07-13,1.7,75.0,shooting,0,0,0 -826022649,So Hee Lee,KOR,female,1994-06-14,1.71,67.0,badminton,0,0,0 -33975954,Sobhi Saied,TUN,male,1982-09-26,1.86,86.0,handball,0,0,0 -522422287,Sofia Andreeva,RUS,female,1998-08-02,1.78,64.0,aquatics,0,0,0 -615348545,Sofia Asoumanaki,GRE,female,1997-05-25,1.9,85.0,rowing,0,0,0 -863853337,Sofia Assefa,ETH,female,1987-11-14,1.67,52.0,athletics,0,0,0 -173744013,Sofia Bekatorou,GRE,female,1977-12-26,1.72,62.0,sailing,0,0,0 -236027852,Sofia Ennaoui,POL,female,1995-08-30,1.58,42.0,athletics,0,0,0 -149127920,Sofia Jakobsson,SWE,female,1990-04-23,1.74,64.0,football,0,1,0 -989432081,Sofia Lodi,ITA,female,1998-01-29,1.75,57.0,gymnastics,0,0,0 -669523830,Sofia Magdalena Mattsson,SWE,female,1989-11-11,1.64,56.0,wrestling,0,0,1 -419623115,Sofia Middleton,CHI,female,1993-03-12,1.68,60.0,sailing,0,0,0 -655552398,Sofia Paldanius,SWE,female,1979-03-16,1.85,75.0,canoe,0,0,0 -910335212,Sofia Polcanova,AUT,female,1994-09-03,1.81,69.0,table tennis,0,0,0 -955206552,Sofia Riga,GRE,female,1988-07-18,1.54,46.0,athletics,0,0,0 -322339558,Sofia Rito,URU,female,1985-11-02,1.55,52.0,weightlifting,0,0,0 -638320031,Sofia Sanchez,ARG,female,1989-08-23,1.67,57.0,aquatics,0,0,0 -459418489,Sofia Yfantidou,GRE,female,1985-01-10,1.62,54.0,athletics,0,0,0 -561413034,Sofian Bouvet,FRA,male,1989-06-02,1.73,63.0,sailing,0,0,0 -245506454,Sofiane Bendebka,ALG,male,1992-08-09,1.7,65.0,football,0,0,0 -496243437,Sofiane Guitone,FRA,male,1989-03-27,1.86,,rugby sevens,0,0,0 -147169766,Sofiane Oumiha,FRA,male,1994-12-23,1.74,,boxing,0,1,0 -342412966,Sofie Skoog,SWE,female,1990-06-07,1.81,65.0,athletics,0,0,0 -832285116,Sofya Velikaya,RUS,female,1985-06-08,1.75,70.0,fencing,1,1,0 -476545490,Sohrab Moradi,IRI,male,1988-09-22,1.7,94.0,weightlifting,1,0,0 -781502405,Sohsuke Takatani,JPN,male,1989-04-05,1.78,81.0,wrestling,0,0,0 -555728322,Sohui Kim,KOR,female,1994-01-29,1.64,50.0,taekwondo,1,0,0 -34827497,Sojeong Yu,KOR,female,1996-06-04,1.68,66.0,handball,0,0,0 -137006196,Sol Mi Kim,PRK,female,1990-11-20,1.55,48.0,judo,0,0,0 -833656877,Solangie Delgado,COL,female,1989-11-09,1.55,57.0,rugby sevens,0,0,0 -381575371,Solomon Bockarie,NED,male,1987-05-18,1.71,72.0,athletics,0,0,0 -504624644,Solonei da Silva,BRA,male,1982-04-18,1.72,65.0,athletics,0,0,0 -213271655,Sona Bernardova,CZE,female,1976-02-02,1.67,54.0,aquatics,0,0,0 -51596305,Sona Poghosyan,ARM,female,1998-06-29,1.58,73.0,weightlifting,0,0,0 -359534607,Sonchat Ratiwatana,THA,male,1982-01-23,1.75,71.0,tennis,0,0,0 -956809483,Sondre Nordstad Moen,NOR,male,1991-01-12,,,athletics,0,0,0 -592878786,Soner Demirtas,TUR,male,1991-06-25,1.7,74.0,wrestling,0,0,1 -939825627,Song Gao,CHN,female,1992-04-16,1.9,85.0,basketball,0,0,0 -614626867,Song Guk Kim,PRK,male,1985-10-05,1.67,75.0,shooting,0,0,1 -822430141,Song I Kim,PRK,female,1994-08-10,1.61,55.0,table tennis,0,0,1 -51012324,Song Yu,CHN,female,1986-08-06,1.82,128.0,judo,0,0,1 -388561246,Sonia Aktar,BAN,female,1997-07-15,,,aquatics,0,0,0 -504175005,Sonia Asselah,ALG,female,1991-08-20,1.76,78.0,judo,0,0,0 -161282037,Sonia Franquet,ESP,female,1980-07-03,1.73,60.0,shooting,0,0,0 -171624562,Sonia Malavisi,ITA,female,1994-10-31,1.72,66.0,athletics,0,0,0 -856090842,Sonia Samuels,GBR,female,1979-05-16,1.62,47.0,athletics,0,0,0 -195523024,Sonja Barjaktarovic,MNE,female,1986-09-11,1.8,74.0,handball,0,0,0 -765900580,Sonja Petrovic,SRB,female,1989-02-18,1.89,75.0,basketball,0,0,1 -835642046,Sonke Rothenberger,GER,male,1994-10-14,1.93,73.0,equestrian,1,0,0 -777662103,Sonny Bill Williams,NZL,male,1985-08-03,1.94,106.0,rugby sevens,0,0,0 -698806625,Sonny Webster,GBR,male,1994-03-10,1.78,94.0,weightlifting,0,0,0 -414404484,Sooji Jang,KOR,female,1987-11-21,1.64,57.0,hockey,0,0,0 -20275055,Sophie Ainsworth,GBR,female,1989-06-22,1.73,68.0,sailing,0,0,0 -346597272,Sophie Bray,GBR,female,1990-05-12,1.64,58.0,hockey,1,0,0 -661172330,Sophie Cocks,NZL,female,1994-07-25,1.72,62.0,hockey,0,0,0 -972385553,Sophie Giger,SUI,female,1995-12-21,1.68,54.0,aquatics,0,0,0 -857464664,Sophie Hansson,SWE,female,1998-08-02,1.86,73.0,aquatics,0,0,0 -426389518,Sophie Hitchon,GBR,female,1991-07-11,1.7,75.0,athletics,0,0,1 -123926137,Sophie Mackenzie,NZL,female,1992-03-31,1.72,57.0,rowing,0,0,0 -563907426,Sophie Scheder,GER,female,1997-01-07,1.67,56.0,gymnastics,0,0,1 -901973768,Sophie Schmidt,CAN,female,1988-06-28,1.72,68.0,football,0,0,1 -696322599,Sophie Souwer,NED,female,1987-06-29,1.85,77.0,rowing,0,0,0 -379316110,Sophie van Gestel,NED,female,1991-06-29,1.78,69.0,volleyball,0,0,0 -562244040,Sopita Tanasan,THA,female,1994-12-23,1.54,48.0,weightlifting,1,0,0 -433864898,Sopuruchi Dimgba,NGR,male,1993-03-29,1.68,68.0,football,0,0,0 -802550899,Soren Dahl,DEN,male,1993-07-15,1.93,86.0,aquatics,0,0,0 -602705126,Soren Kjeldsen,DEN,male,1975-05-17,1.7,72.0,golf,0,0,0 -510174906,Soren Opti,SUR,male,1997-05-16,,,badminton,0,0,0 -46541795,Soslan Daurov,BLR,male,1991-01-15,1.64,60.0,wrestling,0,0,0 -65014090,Soslan Ramonov,RUS,male,1991-01-01,1.7,65.0,wrestling,1,0,0 -303196371,Soso Tamarau,NGR,male,1984-05-16,1.67,97.0,wrestling,0,0,0 -724287824,Sosthene Moguenara,GER,female,1989-10-17,1.8,65.0,athletics,0,0,0 -992931470,Sotheara Chov,CAM,female,1983-10-10,1.55,48.0,wrestling,0,0,0 -370269631,Sotiria Neofytou,CYP,female,1998-04-23,1.65,50.0,aquatics,0,0,0 -388952499,Souad Ait Salem,ALG,female,1979-01-06,1.58,50.0,athletics,0,0,0 -767205811,Soufiane Elbakkali,MAR,male,1996-01-07,1.88,70.0,athletics,0,0,0 -64329847,Soufiane Haddi,MAR,male,1991-02-02,1.69,64.0,cycling,0,0,0 -856037002,Soufiyan Bouqantar,MAR,male,1993-08-30,1.73,54.0,athletics,0,0,0 -306264862,Soukphaxay Sithisane,LAO,male,1996-05-01,,,judo,0,0,0 -276345240,Soule Soilihi Athoumane,COM,male,1991-03-30,1.8,75.0,aquatics,0,0,0 -298080547,Souleymane Diop Cissokho,FRA,male,1991-07-04,1.79,,boxing,0,0,1 -764767080,Soumyajit Ghosh,IND,male,1993-05-10,1.68,69.0,table tennis,0,0,0 -830239226,Sovijja Pou,CAM,male,1995-07-18,1.69,62.0,aquatics,0,0,0 -269749454,Soyib Kurbonov,UZB,male,1988-02-03,1.86,100.0,judo,0,0,0 -11491281,Sparkle McKnight,TTO,female,1991-12-21,1.58,55.0,athletics,0,0,0 -132368991,Spela Perse,SLO,female,1996-08-04,1.58,50.0,aquatics,0,0,0 -55713015,Spela Ponomarenko Janic,SLO,female,1981-10-02,1.69,65.0,canoe,0,0,0 -755409303,Spencer Turrin,AUS,male,1991-08-29,1.9,92.0,rowing,0,0,0 -212024844,Spencer Wilton,GBR,male,1973-02-01,1.83,83.0,equestrian,0,1,0 -275435304,Spiros Gianniotis,GRE,male,1980-02-19,1.85,78.0,aquatics,0,1,0 -875845339,Spyridon Christos Giannaros,GRE,male,1992-05-12,1.83,72.0,rowing,0,0,0 -191166211,Srabani Nanda,IND,female,1991-05-07,1.64,54.0,athletics,0,0,0 -980722321,Srdjan Mrvaljevic,MNE,male,1984-05-16,1.86,85.0,judo,0,0,0 -999965015,Sreejesh Parattu,IND,male,1988-05-08,1.83,79.0,hockey,0,0,0 -213384743,Sri Wahyuni Agustiani,INA,female,1994-08-13,1.47,47.0,weightlifting,0,1,0 -311879753,Srikanth Kidambi,IND,male,1993-02-07,1.78,66.0,badminton,0,0,0 -79428759,Stacey Michelsen,NZL,female,1991-02-18,1.73,66.0,hockey,0,0,0 -64239196,Stacy Lewis,USA,female,1985-02-16,1.66,60.0,golf,0,0,0 -883031102,Stacy Otieno,KEN,female,1990-09-27,1.67,71.0,rugby sevens,0,0,0 -670857265,Stan Okoye,NGR,male,1991-04-10,1.98,,basketball,0,0,0 -217337729,Stanimira Petrova,BUL,female,1990-12-16,1.7,,boxing,0,0,0 -489359727,Stanislau Shcharbachenia,BLR,male,1985-03-05,2.01,104.0,rowing,0,0,0 -574714431,Stanley Amuzie,NGR,male,1996-02-28,1.71,,football,0,0,1 -948549107,Stanley Joseph,FRA,male,1991-10-24,1.81,66.0,athletics,0,0,0 -839709213,Stanley Kipleting Biwott,KEN,male,1986-04-21,1.52,61.0,athletics,0,0,0 -66299670,Stanly del Carmen,DOM,male,1995-09-20,1.63,62.0,athletics,0,0,0 -194610511,Stavroula Samara,GRE,female,1994-07-08,1.74,56.0,gymnastics,0,0,0 -760762237,Steele Johnson,USA,male,1996-06-16,1.88,79.0,aquatics,0,1,0 -588743622,Steeve Barry,FRA,male,1991-04-18,1.81,85.0,rugby sevens,0,0,0 -577610153,Stefan Bellore Sangala,CGO,female,1995-01-02,1.65,58.0,aquatics,0,0,0 -319413003,Stefan Bircevic,SRB,male,1989-12-13,2.1,104.0,basketball,0,1,0 -565822504,Stefan Brits,RSA,male,1992-01-19,1.83,72.0,athletics,0,0,0 -674224579,Stefan Denifl,AUT,male,1987-09-20,1.79,65.0,cycling,0,0,0 -308539287,Stefan Fegerl,AUT,male,1988-09-12,1.86,69.0,table tennis,0,0,0 -131551003,Stefan Hristov,BUL,male,1985-07-12,1.75,59.0,cycling,0,0,0 -971449557,Stefan Jovic,SRB,male,1990-11-03,1.96,94.0,basketball,0,1,0 -118661082,Stefan Kiraj,GER,male,1989-05-11,1.89,92.0,canoe,0,0,0 -921350998,Stefan Markovic,SRB,male,1988-04-25,1.99,96.0,basketball,0,1,0 -11369816,Stefan Mitrovic,SRB,male,1988-03-29,1.95,91.0,aquatics,1,0,0 -21362991,Stefan Nilsson,SWE,male,1990-08-12,1.86,89.0,shooting,0,0,0 -782498343,Stefana Veljkovic,SRB,female,1990-01-09,1.9,76.0,volleyball,0,1,0 -369495539,Stefani Stoeva,BUL,female,1995-09-23,1.74,64.0,badminton,0,0,0 -436598092,Stefania Pirozzi,ITA,female,1993-12-16,1.7,59.0,aquatics,0,0,0 -254807434,Stefanie Fee,USA,female,1990-03-11,1.68,63.0,hockey,0,0,0 -188282515,Stefanie Horn,ITA,female,1991-01-09,1.68,59.0,canoe,0,0,0 -392368733,Stefaniya Elfutina,RUS,female,1997-01-27,1.7,59.0,sailing,0,0,1 -857655408,Stefano Brecciaroli,ITA,male,1974-11-19,1.77,71.0,equestrian,0,0,0 -380056302,Stefano Giantorno,BRA,male,1991-09-27,1.7,73.0,rugby sevens,0,0,0 -137494922,Stefano Oppo,ITA,male,1994-09-12,1.87,70.0,rowing,0,0,0 -395614282,Stefano Peschiera,PER,male,1995-01-16,1.82,84.0,sailing,0,0,0 -145404810,Stefano Raffaele Marcia,RSA,male,1993-11-23,1.78,80.0,sailing,0,0,0 -25301799,Stefano Selva,SMR,male,1969-08-24,1.7,65.0,shooting,0,0,0 -965767592,Stefano Tempesti,ITA,male,1979-06-09,2.05,100.0,aquatics,0,0,1 -891446436,Stefano la Rosa,ITA,male,1985-09-28,1.7,54.0,athletics,0,0,0 -561352985,Stefanos Dimitriadis,GRE,male,1989-09-08,1.89,82.0,aquatics,0,0,0 -616655285,Stefanos Galanopoulos,GRE,male,1993-02-22,1.97,89.0,aquatics,0,0,0 -517441017,Stefanos Ntouskos,GRE,male,1997-03-29,1.86,73.0,rowing,0,0,0 -161034352,Stefany Castano,COL,female,1994-01-11,1.72,60.0,football,0,0,0 -457750300,Stefany Coronado,BOL,female,1996-09-16,1.73,54.0,athletics,0,0,0 -223169136,Stefany Hernandez,VEN,female,1991-06-15,1.65,65.0,cycling,0,0,1 -371287084,Steffen Deibler,GER,male,1987-07-10,1.86,81.0,aquatics,0,0,0 -675798889,Steffen Fath,GER,male,1990-04-04,1.99,97.0,handball,0,0,1 -520458964,Steffen Peters,USA,male,1964-09-18,1.73,68.0,equestrian,0,0,1 -318627222,Steffen Weinhold,GER,male,1986-07-19,1.91,94.0,handball,0,0,1 -733771935,Steffi Kriegerstein,GER,female,1992-11-03,1.78,70.0,canoe,0,1,0 -533594402,Stella Akakpo,FRA,female,1994-02-28,1.66,60.0,athletics,0,0,0 -305158735,Stella Chesang,UGA,female,1996-12-01,,,athletics,0,0,0 -995446610,Stepan Maryanyan,RUS,male,1991-09-21,1.65,59.0,wrestling,0,0,0 -957346968,Steph Catley,AUS,female,1994-01-26,1.71,60.0,football,0,0,0 -508727443,Stephan Feck,GER,male,1990-02-17,1.83,79.0,aquatics,0,0,0 -792396133,Stephan Krueger,GER,male,1988-11-29,1.87,86.0,rowing,0,0,0 -891602037,Stephan de Freitas Barcha,BRA,male,1989-10-27,,,equestrian,0,0,0 -352127171,Stephane Smith,BRA,male,1989-05-15,1.73,70.0,hockey,0,0,0 -305108118,Stephanie Au,HKG,female,1992-05-30,1.72,56.0,aquatics,0,0,0 -984866759,Stephanie Brieussel,FRA,female,1974-01-29,1.69,55.0,equestrian,0,0,0 -799391274,Stephanie Enright,PUR,female,1990-12-15,1.79,56.0,volleyball,0,0,0 -195104856,Stephanie Fotso Mogoung,CMR,female,1987-09-25,1.84,78.0,volleyball,0,0,0 -944264216,Stephanie Horner,CAN,female,1989-03-19,1.8,69.0,aquatics,0,0,0 -238315993,Stephanie Labbe,CAN,female,1986-10-10,1.78,63.0,football,0,0,1 -409287519,Stephanie Lovell,LCA,female,1995-09-08,1.6,62.0,sailing,0,0,0 -368669797,Stephanie Malherbe,RSA,female,1996-04-05,1.62,59.0,football,0,0,0 -129467500,Stephanie Meadow,IRL,female,1992-01-20,1.63,64.0,golf,0,0,0 -154640306,Stephanie Morton,AUS,female,1990-11-28,1.77,77.0,cycling,0,0,0 -932494249,Stephanie Pohl,GER,female,1987-10-21,1.6,51.0,cycling,0,0,0 -419421919,Stephanie Talbot,AUS,female,1994-06-15,1.86,87.0,basketball,0,0,0 -430731378,Stephanie Tirode,FRA,female,1975-05-01,1.65,78.0,shooting,0,0,0 -157050778,Stephanie Twell,GBR,female,1989-08-17,1.68,57.0,athletics,0,0,0 -422679366,Stephanie Vogt,LIE,female,1990-02-15,1.67,65.0,tennis,0,0,0 -459968412,Stephen Bird,AUS,male,1988-05-11,1.89,86.0,canoe,0,0,0 -254081121,Stephen Cummings,GBR,male,1981-03-19,1.9,75.0,cycling,0,0,0 -946544198,Stephen Jenness,NZL,male,1990-06-07,1.79,76.0,hockey,0,0,0 -203402435,Stephen Jones,NZL,male,1993-04-29,1.92,95.0,rowing,0,0,0 -156864780,Stephen Kasprzyk,USA,male,1982-02-14,2.01,103.0,rowing,0,0,0 -82771185,Stephen Kiprotich,UGA,male,1989-02-27,,,athletics,0,0,0 -641360615,Stephen Lambdin,USA,male,1988-03-09,1.88,108.0,taekwondo,0,0,0 -653237544,Stephen Milne,GBR,male,1994-04-29,1.86,76.0,aquatics,0,1,0 -144812081,Stephen Mokoka,RSA,male,1985-01-31,1.57,52.0,athletics,0,0,0 -730191367,Stephen Mozia,NGR,male,1993-08-16,1.91,124.0,athletics,0,0,0 -769819070,Stephen Newbold,BAH,male,1994-08-05,,,athletics,0,0,1 -791262365,Stephen Parez,FRA,male,1994-08-01,1.74,75.0,rugby sevens,0,0,0 -940693256,Stephenie Ann McPherson,JAM,female,1988-11-25,1.73,57.0,athletics,0,1,0 -445715201,Stevan Pletikosic,SRB,male,1972-03-14,1.84,81.0,shooting,0,0,0 -109758786,Steve Guerdat,SUI,male,1982-06-10,1.82,70.0,equestrian,0,0,0 -600255721,Steve Johnson,USA,male,1989-12-24,1.88,86.0,tennis,0,0,1 -226464933,Steve Morabito,SUI,male,1983-01-30,1.87,73.0,cycling,0,0,0 -226636545,Steven Burke,GBR,male,1988-03-04,1.83,78.0,cycling,1,0,0 -590851106,Steven Gardiner,BAH,male,1995-09-12,1.88,,athletics,0,0,1 -917340321,Steven Gerard Donnelly,IRL,male,1988-09-07,1.8,,boxing,0,0,0 -51098946,Steven Kruijswijk,NED,male,1987-06-07,1.77,64.0,cycling,0,0,0 -92940638,Steven Lopez,USA,male,1978-11-09,1.91,83.0,taekwondo,0,0,0 -59291100,Steven Marshall,CAN,male,1989-11-23,1.93,87.0,volleyball,0,0,0 -640785339,Steven Scott,GBR,male,1985-01-10,1.72,84.0,shooting,0,0,1 -308437509,Stewart Innes,GBR,male,1991-05-20,1.97,97.0,rowing,0,0,0 -634725148,Stian Skjerahaug,NOR,male,1992-03-08,1.71,66.0,gymnastics,0,0,0 -581997901,Stig-Andre Berge,NOR,male,1983-07-20,,,wrestling,0,0,1 -915123648,Stina Blackstenius,SWE,female,1996-02-05,1.73,72.0,football,0,1,0 -65486253,Stina Gardell,SWE,female,1990-03-28,1.72,70.0,aquatics,0,0,0 -854904743,Stina Troest,DEN,female,1994-01-17,1.7,59.0,athletics,0,0,0 -945304583,Stine Bredal Oftedal,NOR,female,1991-09-25,1.68,,handball,0,0,1 -602912762,Stine Nielsen,DEN,female,1991-02-09,1.63,79.0,shooting,0,0,0 -527651130,Stipe Zunic,CRO,male,1990-12-13,1.93,134.0,athletics,0,0,0 -499631914,Stsiapan Rahautsou,BLR,male,1986-05-29,1.74,58.0,athletics,0,0,0 -153456321,Stuart Dutamby,FRA,male,1994-04-24,1.76,74.0,athletics,0,0,0 -103790201,Stuart Farquhar,NZL,male,1982-03-15,1.87,98.0,athletics,0,0,0 -705154995,Stuart McNay,USA,male,1981-08-01,1.71,65.0,sailing,0,0,0 -85718684,Stuart Tinney,AUS,male,1964-12-07,1.8,71.0,equestrian,0,0,1 -571111419,Su Ji Kim,KOR,female,1987-07-11,1.87,68.0,volleyball,0,0,0 -54125373,Su Oh,AUS,female,1996-05-23,1.68,,golf,0,0,0 -464562333,Suad Natiq,IRQ,male,1994-03-19,1.85,78.0,football,0,0,0 -377406840,Subenrat Insaeng,THA,female,1994-02-10,1.82,115.0,athletics,0,0,0 -863916736,Sudha Singh,IND,female,1986-06-25,1.58,45.0,athletics,0,0,0 -676826208,Sudirman Hadi,INA,male,1996-03-09,1.71,76.0,athletics,0,0,0 -549566772,Sue Bird,USA,female,1980-10-16,1.75,66.0,basketball,1,0,0 -523057836,Suehiro Ishikawa,JPN,male,1979-09-27,1.69,56.0,athletics,0,0,0 -321796870,Sugoi Uriarte,ESP,male,1984-05-14,1.72,66.0,judo,0,0,0 -566595674,Suguru Osako,JPN,male,1991-05-23,1.7,52.0,athletics,0,0,0 -215796471,Suhrob Khodjaev,UZB,male,1993-05-21,1.85,120.0,athletics,0,0,0 -574040538,Sukanya Srisurat,THA,female,1995-05-03,1.55,57.0,weightlifting,1,0,0 -963404474,Sukhi Panesar,CAN,male,1993-12-26,1.8,75.0,hockey,0,0,0 -50906905,Sulaiman Hamad,KSA,male,1994-05-19,1.77,66.0,judo,0,0,0 -724836390,Suleyman Atli,TUR,male,1994-07-27,1.68,57.0,wrestling,0,0,0 -918686488,Sultan Duzelbayev,KAZ,male,1994-03-12,1.76,46.0,archery,0,0,0 -715405655,Sultan Haydar,TUR,female,1987-05-23,1.7,55.0,athletics,0,0,0 -548130768,Sultan Mubarak Al-Dawoodi,KSA,male,1977-06-16,1.92,86.0,athletics,0,0,0 -27825256,Sumeeth Reddy B.,IND,male,1991-09-26,1.82,64.0,badminton,0,0,0 -214849260,Sumin Choi,KOR,female,1990-01-09,1.77,64.0,handball,0,0,0 -43374597,Sumiya Dorjsuren,MGL,female,1991-03-11,1.6,59.0,judo,0,1,0 -311378781,Sumiya Erdenechimeg,MGL,female,1990-02-28,1.59,53.0,wrestling,0,0,0 -67222900,Sunayna Wahi,SUR,female,1990-08-14,,,athletics,0,0,0 -224948383,Sunette Viljoen,RSA,female,1983-10-06,1.7,72.0,athletics,0,1,0 -960642258,Sung Hyun Ko,KOR,male,1987-05-21,1.82,85.0,badminton,0,0,0 -733228088,Sungmin Kim,KOR,male,1987-06-29,1.9,130.0,judo,0,0,0 -773103124,Sungyun Gu,KOR,male,1994-06-27,1.95,82.0,football,0,0,0 -179464050,Sunhee Woo,KOR,female,1978-07-01,1.71,58.0,handball,0,0,0 -38293952,Sunil Sowmarpet,IND,male,1989-05-06,1.76,68.0,hockey,0,0,0 -286970753,Sunita Lakra,IND,female,1991-06-11,1.58,57.0,hockey,0,0,0 -187534242,Sunwoo Kim,KOR,female,1996-10-07,1.65,57.0,modern pentathlon,0,0,0 -278485243,Suping Meng,CHN,female,1989-07-17,1.73,120.0,weightlifting,1,0,0 -912469374,Suraju Saka,CGO,male,1976-05-05,1.7,68.0,table tennis,0,0,0 -118866777,Surender Kumar,IND,male,1993-11-23,1.79,77.0,hockey,0,0,0 -819423699,Susan Kuijken,NED,female,1986-07-08,1.72,54.0,athletics,0,0,0 -718822536,Susana Costa,POR,female,1984-09-22,1.77,64.0,athletics,0,0,0 -261214819,Susann Bjornsen,NOR,female,1993-05-28,1.91,72.0,aquatics,0,0,0 -280722315,Susanna Kallur,SWE,female,1981-02-16,1.7,62.0,athletics,0,0,0 -361971820,Susannah Townsend,GBR,female,1989-07-28,1.68,61.0,hockey,1,0,0 -199049873,Susanne Grainger,CAN,female,1990-12-30,1.88,86.0,rowing,0,0,0 -8359251,Sushila Pukhrambam,IND,female,1992-02-25,1.52,52.0,hockey,0,0,0 -72532043,Suthasini Sawettabut,THA,female,1994-12-09,1.68,63.0,table tennis,0,0,0 -292142118,Sutiya Jiewchaloemmit,THA,female,1986-05-03,1.69,61.0,shooting,0,0,0 -326533189,Suvi Mikkonen,FIN,female,1988-07-11,1.64,57.0,taekwondo,0,0,0 -418495066,Suyeon Back,KOR,female,1991-07-01,1.73,61.0,aquatics,0,0,0 -129659927,Suzana Lazovic,MNE,female,1992-01-28,1.78,72.0,handball,0,0,0 -819679922,Suzann Pettersen,NOR,female,1981-04-07,,,golf,0,0,0 -756706296,Suzanne Hearn,AUS,female,1956-02-13,,,equestrian,0,0,0 -809014014,Suzuka Hasegawa,JPN,female,2000-01-25,1.65,56.0,aquatics,0,0,0 -629383847,Sven Bender,GER,male,1989-04-27,1.85,73.0,football,0,1,0 -167125216,Sven Erik Bystrom,NOR,male,1992-01-21,,,cycling,0,0,0 -674985541,Sven Knipphals,GER,male,1985-09-20,1.89,88.0,athletics,0,0,0 -835906599,Sven Maresch,GER,male,1987-01-19,1.73,83.0,judo,0,0,0 -599845580,Sven Martin Skagestad,NOR,male,1995-01-13,,,athletics,0,0,0 -879259485,Sven Riederer,SUI,male,1981-03-27,1.82,69.0,triathlon,0,0,0 -538115271,Svenja Huth,GER,female,1991-01-25,1.63,54.0,football,1,0,0 -17641128,Svetlana Chimrova,RUS,female,1996-04-15,1.73,61.0,aquatics,0,0,0 -823601145,Svetlana Germanovich,KAZ,female,1986-09-21,1.76,67.0,rowing,0,0,0 -300072037,Svetlana Golendova,KAZ,female,1993-07-25,1.65,46.0,athletics,0,0,0 -514719063,Svetlana Kolesnichenko,RUS,female,1993-09-20,1.71,54.0,aquatics,1,0,0 -813630131,Svetlana Kuznetsova,RUS,female,1985-06-27,1.74,73.0,tennis,0,0,0 -82177310,Svetlana Radzivil,UZB,female,1987-01-17,1.86,61.0,athletics,0,0,0 -501420252,Svetlana Romashina,RUS,female,1989-09-21,1.73,55.0,aquatics,2,0,0 -798685031,Sviatlana Kudzelich,BLR,female,1987-05-07,1.7,51.0,athletics,0,0,0 -720211034,Svitlana Akhadova,UKR,female,1993-05-10,1.7,69.0,canoe,0,0,0 -524819089,Svitlana Iaromka,UKR,female,1989-04-09,1.62,78.0,judo,0,0,0 -237017632,Svitlana Stanko-Klymenko,UKR,female,1976-05-13,,,athletics,0,0,0 -734681524,Swe Li Myint Myint,MYA,female,1993-06-24,1.68,55.0,athletics,0,0,0 -923873687,Sydney McLaughlin,USA,female,1999-08-07,1.76,59.0,athletics,0,0,0 -897751532,Sydney Pickrem,CAN,female,1997-05-21,1.68,57.0,aquatics,0,0,0 -445792938,Sylvia Fowles,USA,female,1985-10-06,1.98,90.0,basketball,1,0,0 -640361977,Sylwester Bednarek,POL,male,1989-04-28,1.94,78.0,athletics,0,0,0 -365526151,Sylwia Bogacka,POL,female,1981-10-03,1.62,57.0,shooting,0,0,0 -687167760,Szandra Szogedi,GHA,female,1988-10-19,1.58,63.0,judo,0,0,0 -761194557,Szu-Yu Chen,TPE,female,1993-08-01,1.62,58.0,table tennis,0,0,0 -313930534,Szymon Staskiewicz,POL,male,1987-01-03,1.86,80.0,modern pentathlon,0,0,0 -654852458,Tabata de Carvalho,BRA,female,1996-04-23,1.71,61.0,athletics,0,0,0 -997432582,Tabea Alt,GER,female,2000-03-18,1.58,50.0,gymnastics,0,0,0 -57676016,Tabea Kemme,GER,female,1991-12-14,1.7,56.0,football,1,0,0 -990975913,Taciana Lima,GBS,female,1983-12-17,1.64,48.0,judo,0,0,0 -158812525,Tadas Suskevicius,LTU,male,1985-05-22,1.77,64.0,athletics,0,0,0 -134631716,Tadesse Abraham,SUI,male,1982-08-12,1.78,61.0,athletics,0,0,0 -359507589,Taehoon Lee,KOR,male,1986-05-18,1.8,74.0,sailing,0,0,0 -965284155,Taehun Kim,KOR,male,1994-08-15,1.82,59.0,taekwondo,0,0,1 -589375724,Taehwan Park,KOR,male,1989-09-27,1.83,73.0,aquatics,0,0,0 -193649296,Tafese Seboka,ETH,male,1993-09-29,1.77,66.0,athletics,0,0,0 -768671420,Tagir Khaibulaev,RUS,male,1984-07-24,1.82,100.0,judo,0,0,0 -539157373,Taha Akgul,TUR,male,1990-11-22,1.92,125.0,wrestling,1,0,0 -136534456,Tahesia Harrigan-Scott,IVB,female,1982-02-15,1.58,56.0,athletics,0,0,0 -477470052,Tahir Guelec,GER,male,1993-02-25,1.91,82.0,taekwondo,0,0,0 -161917472,Tahir Walsh,ANT,male,1994-02-24,1.82,91.0,athletics,0,0,0 -485849268,Taimuraz Friev Naskidaeva,ESP,male,1986-09-15,1.76,84.0,wrestling,0,0,0 -871329009,Taina Halasima,TGA,female,1997-12-11,1.63,65.0,athletics,0,0,0 -346992499,Taina Paixao,BRA,female,1991-11-29,1.71,69.0,basketball,0,0,0 -605417556,Tairat Bunsuk,THA,male,1993-01-11,1.61,69.0,weightlifting,0,0,0 -449390990,Tais Balconi,BRA,female,1991-04-11,1.64,63.0,rugby sevens,0,0,0 -658175291,Tais Rochel,BRA,female,1983-10-16,1.65,61.0,fencing,0,0,0 -615179383,Taiwo Awoniyi,NGR,male,1997-08-12,1.82,71.0,football,0,0,0 -615764014,Taizo Sugitani,JPN,male,1976-06-27,1.7,63.0,equestrian,0,0,0 -799687468,Takaharu Furukawa,JPN,male,1984-08-09,1.74,89.0,archery,0,0,0 -25012659,Takamasa Kitagawa,JPN,male,1996-09-05,1.77,69.0,athletics,0,0,0 -654328504,Takanori Nagase,JPN,male,1993-10-14,1.81,81.0,judo,0,0,1 -599789748,Takashi Eto,JPN,male,1991-02-05,1.83,67.0,athletics,0,0,0 -511495606,Takayuki Tanii,JPN,male,1983-02-14,1.67,57.0,athletics,0,0,0 -784189307,Takeshi Matsuda,JPN,male,1984-06-23,1.84,84.0,aquatics,0,0,1 -313024850,Takuma Asano,JPN,male,1994-11-10,1.71,70.0,football,0,0,0 -247907284,Takumi Minamino,JPN,male,1995-01-16,1.74,67.0,football,0,0,0 -798091579,Takuro Fujii,JPN,male,1985-04-21,1.84,82.0,aquatics,0,0,0 -874798045,Takuya Haneda,JPN,male,1987-07-17,1.75,70.0,canoe,0,0,1 -416084708,Takuya Iwanami,JPN,male,1994-06-18,1.86,72.0,football,0,0,0 -536937695,Talent Mandaza,ZIM,female,1985-12-11,1.58,60.0,football,0,0,0 -808567905,Tales Cerdeira,BRA,male,1987-01-21,,,aquatics,0,0,0 -842617684,Talgat Ilyasov,AUS,male,1981-02-25,1.75,74.0,wrestling,0,0,0 -74177936,Taliqua Clancy,AUS,female,1992-06-25,1.84,68.0,volleyball,0,0,0 -99503989,Talisa Lanoe,KEN,female,1994-07-25,1.67,50.0,aquatics,0,0,0 -296747783,Talita Baqlah,JOR,female,1995-10-27,1.72,62.0,aquatics,0,0,0 -122731267,Talita Marie Te Flan,CIV,female,1995-06-02,1.76,63.0,aquatics,0,0,0 -302432944,Talita Rocha,BRA,female,1982-08-29,1.81,64.0,volleyball,0,0,0 -877029068,Talles Frederico Silva,BRA,male,1991-08-20,1.9,78.0,athletics,0,0,0 -318548853,Tamara Csipes,HUN,female,1989-08-24,1.76,78.0,canoe,1,0,0 -732795832,Tamara Echegoyen Dominguez,ESP,female,1984-02-17,1.74,70.0,sailing,0,0,0 -818897478,Tamara Horacek,FRA,female,1995-11-05,1.79,80.0,handball,0,1,0 -800485643,Tamara Radocaj,SRB,female,1987-12-23,1.7,60.0,basketball,0,0,1 -545572234,Tamara Salaski,SRB,female,1988-10-16,1.65,54.0,athletics,0,0,0 -215126261,Tamara Tatham,CAN,female,1985-08-19,1.85,79.0,basketball,0,0,0 -168420206,Tamara Vega,MEX,female,1993-03-15,1.59,59.0,modern pentathlon,0,0,0 -431927399,Tamas Decsi,HUN,male,1982-10-15,1.78,82.0,fencing,0,0,0 -19585487,Tamas Kenderesi,HUN,male,1996-12-13,1.86,75.0,aquatics,0,0,1 -423484722,Tamas Lorincz,HUN,male,1986-12-20,1.72,75.0,wrestling,0,0,0 -540557883,Tamas Somoracz,HUN,male,1992-04-11,1.95,92.0,canoe,0,0,0 -270561251,Tamas Toth,HUN,male,1989-05-29,1.75,65.0,triathlon,0,0,0 -82978460,Tameka Butt,AUS,female,1991-06-16,1.58,55.0,football,0,0,0 -845644172,Tameka Williams,SKN,female,1989-08-31,,,athletics,0,0,0 -578801610,Tamika Catchings,USA,female,1979-07-21,1.85,77.0,basketball,1,0,0 -655850700,Tamila Holub,POR,female,1999-05-15,1.75,62.0,aquatics,0,0,0 -835127329,Tamirat Tola,ETH,male,1991-08-11,1.81,59.0,athletics,0,0,1 -19869535,Tamires,BRA,female,1987-10-10,1.61,54.0,football,0,0,0 -267796493,Tamires Lima de Araujo,BRA,female,1994-05-16,1.8,77.0,handball,0,0,0 -120820876,Tammy Takagi,BRA,female,1991-03-11,1.61,59.0,aquatics,0,0,0 -425857389,Tamsin Cook,AUS,female,1998-12-25,1.7,61.0,aquatics,0,1,0 -14783411,Tan Tai Hoang,VIE,male,1990-03-30,1.65,84.0,weightlifting,0,0,0 -395892993,Tanel Kangert,EST,male,1987-03-11,1.78,66.0,cycling,0,0,0 -338003590,Tanel Laanmae,EST,male,1989-09-29,1.83,95.0,athletics,0,0,0 -198967774,Tanguy Cosyns,BEL,male,1991-06-29,1.74,70.0,hockey,0,1,0 -572093564,Tania Arrayales,MEX,female,1996-08-01,1.58,53.0,fencing,0,0,0 -401251209,Tania Cagnotto,ITA,female,1985-05-15,1.6,53.0,aquatics,0,1,1 -415410367,Tania Calvo Barbero,ESP,female,1992-06-26,1.66,64.0,cycling,0,0,0 -424029874,Tania di Mario,ITA,female,1979-05-04,1.68,62.0,aquatics,0,1,0 -644104998,Tanja Frank,AUT,female,1993-01-24,1.67,57.0,sailing,0,0,1 -39470367,Tanja Kylliainen,FIN,female,1993-01-30,1.55,57.0,aquatics,0,0,0 -103257404,Tanja Perec,CRO,female,1992-06-08,1.66,85.0,shooting,0,0,0 -65779574,Tanja Zakelj,SLO,female,1988-09-15,1.59,56.0,cycling,0,0,0 -807845971,Tanumafili Malietoa Jungblut,ASA,male,1990-06-10,1.88,93.0,weightlifting,0,0,0 -410068536,Tanya Acosta,ARG,female,1991-03-11,1.82,70.0,volleyball,0,0,0 -520555163,Tanya Holliday,AUS,female,1988-09-21,1.67,52.0,athletics,0,0,0 -467489703,Tanya Seymour,RSA,female,1983-11-05,1.65,57.0,equestrian,0,0,0 -530816831,Tanyaporn Prucksakorn,THA,female,1990-01-08,1.68,70.0,shooting,0,0,0 -725478923,Tao Tian,CHN,male,1994-04-08,1.72,85.0,weightlifting,0,1,0 -701625147,Taoufik Makhloufi,ALG,male,1988-04-29,1.7,67.0,athletics,0,2,0 -152333159,Tapio Nirkko,FIN,male,1984-08-24,1.94,96.0,sailing,0,0,0 -366332579,Tara Pacheco van Rijnsoever,ESP,female,1988-10-03,1.67,63.0,sailing,0,0,0 -347121929,Tara Whitten,CAN,female,1980-07-13,1.66,67.0,cycling,0,0,0 -292576070,Taras Mishchuk,UKR,male,1995-07-22,1.87,85.0,canoe,0,0,1 -962319848,Tarasue Barnett,JAM,female,1993-09-10,1.78,81.0,athletics,0,0,0 -335960074,Tarek Ayad,EGY,male,1987-05-29,1.8,75.0,fencing,0,0,0 -769863760,Tarek Aziz Benaissa,ALG,male,1991-04-07,1.7,66.0,wrestling,0,0,0 -993851056,Tarik Langat Akdag,TUR,male,1988-06-16,1.76,60.0,athletics,0,0,0 -511260324,Tariq Ahmed Al-Amri,KSA,male,1990-12-23,1.65,49.0,athletics,0,0,0 -108994538,Taro Daniel,JPN,male,1993-01-27,1.91,76.0,tennis,0,0,0 -579881556,Taru Kuoppa,FIN,female,1983-11-14,1.73,63.0,archery,0,0,0 -373794222,Taryn Suttie,CAN,female,1990-12-07,1.83,103.0,athletics,0,0,0 -729866752,Tasa Jiya,NED,female,1997-09-16,1.84,68.0,athletics,0,0,0 -244910670,Tasama Moogas,ISR,male,1988-02-02,1.64,53.0,athletics,0,0,0 -983364224,Tashreeq Morris,RSA,male,1994-05-13,1.89,74.0,football,0,0,0 -858607766,Tatenda Tsumba,ZIM,male,1991-11-12,1.83,79.0,athletics,0,0,0 -245946322,Tatiana Ariza,COL,female,1991-02-21,1.61,52.0,football,0,0,0 -878935662,Tatiana Barsuk,RUS,female,1985-02-22,1.7,66.0,shooting,0,0,0 -539307863,Tatiana Chisca,MDA,female,1995-07-19,1.75,60.0,aquatics,0,0,0 -981120716,Tatiana Drozdovskaya,BLR,female,1978-12-06,1.75,69.0,sailing,0,0,0 -6056876,Tatiana Erokhina,RUS,female,1984-09-07,1.85,73.0,handball,1,0,0 -374496832,Tatiana Guderzo,ITA,female,1984-08-22,1.6,55.0,cycling,0,0,0 -326358515,Tatiana Kosheleva,RUS,female,1988-12-23,1.91,67.0,volleyball,0,0,0 -192110887,Tatiana Logunova,RUS,female,1980-07-03,1.74,62.0,fencing,0,0,1 -737519683,Tatiana Soledad Rizzo,ARG,female,1986-12-30,1.78,64.0,volleyball,0,0,0 -829450313,Tatiane Pacheco,BRA,female,1990-10-16,1.81,69.0,basketball,0,0,0 -12705539,Tatiele Roberta de Carvalho,BRA,female,1989-11-22,1.56,50.0,athletics,0,0,0 -848018614,Tatjana Djekanovic,BIH,female,1997-02-25,1.68,54.0,shooting,0,0,0 -177848801,Tatjana Pinto,GER,female,1992-07-02,1.7,56.0,athletics,0,0,0 -854144627,Tatsiana Khaladovich,BLR,female,1991-06-21,1.81,83.0,athletics,0,0,0 -993489737,Tatsiana Korzh,BLR,female,1993-03-17,1.75,75.0,athletics,0,0,0 -880574161,Tatsiana Kukhta,BLR,female,1990-06-13,1.85,77.0,rowing,0,0,0 -133803510,Tatsiana Likhtarovich,BLR,female,1988-03-29,1.8,67.0,basketball,0,0,0 -179784825,Tatsiana Piatrenia,BLR,female,1981-10-18,1.65,52.0,gymnastics,0,0,0 -194275450,Tatsiana Sharakova,BLR,female,1984-07-31,1.6,52.0,cycling,0,0,0 -840107784,Tatyana Troina,BLR,female,1981-06-30,1.88,78.0,basketball,0,0,0 -13841357,Tavis Bailey,USA,male,1992-01-06,1.91,124.0,athletics,0,0,0 -385700766,Tawin Hanprab,THA,male,1998-08-01,1.81,58.0,taekwondo,0,1,0 -417534147,Taybe Mustafa Yusein,BUL,female,1991-05-04,1.63,63.0,wrestling,0,0,0 -928304511,Taylor Curran,CAN,male,1992-05-19,1.83,80.0,hockey,0,0,0 -849242377,Taylor Ellis-Watson,USA,female,1993-05-06,,,athletics,1,0,0 -45282983,Taylor Madison Ruck,CAN,female,2000-05-28,1.83,60.0,aquatics,0,0,2 -721284471,Taylor McKeown,AUS,female,1995-03-17,1.78,65.0,aquatics,0,1,0 -240962566,Taylor Milne,CAN,male,1981-09-14,1.65,60.0,athletics,0,0,0 -32103246,Taylor Phinney,USA,male,1990-06-27,1.96,81.0,cycling,0,0,0 -84694873,Taylor Sander,USA,male,1992-03-17,1.96,80.0,volleyball,0,0,1 -288362768,Taylor Worth,AUS,male,1991-01-08,1.74,60.0,archery,0,0,1 -58561312,Teau McKenzie,COK,female,1995-03-12,1.83,67.0,sailing,0,0,0 -777261301,Tebogo Moerane,RSA,male,1995-04-07,1.67,64.0,football,0,0,0 -93902028,Teddy Atine - Venel,FRA,male,1985-03-16,1.84,77.0,athletics,0,0,0 -714540580,Teddy Riner,FRA,male,1989-04-07,2.03,139.0,judo,1,0,0 -97224345,Tega Odele,NGR,male,1995-12-06,1.88,80.0,athletics,0,0,0 -606135754,Teja Belak,SLO,female,1994-04-22,1.57,48.0,gymnastics,0,0,0 -556483244,Teliana Pereira,BRA,female,1988-07-20,1.67,62.0,tennis,0,0,0 -670067598,Telma Monteiro,POR,female,1985-12-27,1.61,57.0,judo,0,0,1 -781746584,Telma Santos,POR,female,1983-08-01,1.63,55.0,badminton,0,0,0 -679960058,Temuujin Purevjav,MGL,male,1994-06-02,1.73,63.0,taekwondo,0,0,0 -261206041,Temuulen Battulga,MGL,male,1988-10-07,1.83,124.0,judo,0,0,0 -7619971,Teodorico Caporaso,ITA,male,1987-09-14,1.7,55.0,athletics,0,0,0 -743311898,Teofilo Gutierrez,COL,male,1985-05-17,1.8,82.0,football,0,0,0 -675969120,Teofimo Andres Lopez Rivera,HON,male,1997-07-30,1.76,,boxing,0,0,0 -620846374,Teona Bostashvili,GEO,female,1998-01-19,1.64,52.0,aquatics,0,0,0 -654886163,Teray Smith,BAH,male,1994-09-28,1.88,,athletics,0,0,0 -214021045,Teresa Frassinetti,ITA,female,1985-12-24,1.78,75.0,aquatics,0,1,0 -57418889,Teresa Lu,TPE,female,1987-10-13,1.64,57.0,golf,0,0,0 -38799648,Teresa Patricia Almeida,ANG,female,1988-04-05,1.7,98.0,handball,0,0,0 -364848926,Teresa Portela,POR,female,1987-10-30,1.62,63.0,canoe,0,0,0 -18635175,Teresa Portela Rivas,ESP,female,1982-05-05,1.72,69.0,canoe,0,0,0 -923351948,Tereza Master,MAW,female,1988-09-21,1.49,45.0,athletics,0,0,0 -242422662,Terina Te Tamaki,NZL,female,1997-05-01,1.65,67.0,rugby sevens,0,1,0 -334953964,Tero Pitkamaki,FIN,male,1982-12-19,1.95,94.0,athletics,0,0,0 -672973707,Tero Seppo Vaelimaeki,FIN,male,1982-03-21,1.7,73.0,wrestling,0,0,0 -395956352,Terry Bouhraoua,FRA,male,1987-08-29,1.69,65.0,rugby sevens,0,0,0 -125367694,Teruya Goto,JPN,male,1991-12-18,1.77,82.0,rugby sevens,0,0,0 -510104446,Teruyoshi Akiyama,JPN,male,1971-12-25,1.68,65.0,shooting,0,0,0 -102862787,Tervel Ivaylov Dlagnev,USA,male,1985-11-19,1.88,122.0,wrestling,0,0,0 -655229990,Tesfaye Abera,ETH,male,1992-03-31,1.92,68.0,athletics,0,0,0 -135938063,Tess Oliveira,BRA,female,1987-01-06,1.65,58.0,aquatics,0,0,0 -651217766,Tess Wester,NED,female,1993-05-19,1.78,70.0,handball,0,0,0 -723655440,Tessa Gobbo,USA,female,1990-12-08,1.86,81.0,rowing,1,0,0 -847466727,Tessa Lavey,AUS,female,1993-03-29,1.72,68.0,basketball,0,0,0 -647467828,Tessa van Schagen,NED,female,1994-02-02,1.68,62.0,athletics,0,0,0 -166897661,Tessie Savelkouls,NED,female,1992-03-11,1.82,95.0,judo,0,0,0 -279816745,Tetiana Melnyk,UKR,female,1995-04-02,1.85,63.0,athletics,0,0,0 -740317343,Tetyana Bilenko,UKR,female,1983-11-23,1.78,67.0,table tennis,0,0,0 -649148509,Tetyana Kob,UKR,female,1987-10-25,1.61,,boxing,0,0,0 -159812118,Tewelde Estifanos,ERI,male,1981-11-02,,,athletics,0,0,0 -807447097,Teymur Mammadov,AZE,male,1993-01-11,1.96,,boxing,0,0,0 -370652177,Teymuraz Gabashvili,RUS,male,1985-05-23,1.87,85.0,tennis,0,0,0 -754858523,Thabiso Kutumela,RSA,male,1993-07-01,1.75,68.0,football,0,0,0 -210040104,Thadius Katua,PNG,male,1997-11-04,1.67,,boxing,0,0,0 -332545480,Thais Guedes,BRA,female,1993-01-20,1.64,60.0,football,0,0,0 -951388142,Thaisa,BRA,female,1988-12-17,1.66,59.0,football,0,0,0 -296920401,Thaisa Menezes,BRA,female,1987-05-15,1.96,75.0,volleyball,0,0,0 -900917316,Thanackal Gopi,IND,male,1988-05-24,1.66,56.0,athletics,0,0,0 -381843063,Thanasi Kokkinakis,AUS,male,1996-04-10,1.96,82.0,tennis,0,0,0 -699394575,Thanh An Vu,VIE,male,1992-08-07,1.85,80.0,fencing,0,0,0 -5130801,Thanh Ngung Nguyen,VIE,male,1992-04-08,1.65,55.0,athletics,0,0,0 -955206576,Thapelo Morena,RSA,male,1993-08-06,1.7,62.0,football,0,0,0 -315255712,Thea Lafond,DMA,female,1994-04-05,1.73,65.0,athletics,0,0,0 -439436986,Thembi Kgatlana,RSA,female,1996-05-02,1.56,50.0,football,0,0,0 -215472787,Theo Bos,NED,male,1983-08-22,1.9,85.0,cycling,0,0,0 -529392127,Theo Bussiere,FRA,male,1995-01-18,1.9,90.0,aquatics,0,0,0 -104108764,Theo Piniau,PNG,male,1993-06-08,1.72,59.0,athletics,0,0,0 -306944769,Theo Reinhardt,GER,male,1990-09-17,1.77,68.0,cycling,0,0,0 -263136966,Theo van de Vendel,NED,male,1980-10-24,1.76,73.0,equestrian,0,0,0 -648002342,Theodora Drakou,GRE,female,1992-02-06,1.69,67.0,aquatics,0,0,0 -470687922,Theodoros Iakovidis,GRE,male,1991-02-12,1.81,85.0,weightlifting,0,0,0 -466992964,Theophile Onfroy,FRA,male,1992-12-29,1.89,82.0,rowing,0,0,0 -119367610,Theorine Christelle Aboa Mbeza,CMR,female,1992-08-25,1.82,78.0,volleyball,0,0,0 -460776365,Theresa Fitzpatrick,NZL,female,1995-02-25,1.68,75.0,rugby sevens,0,1,0 -365498123,Therese Alshammar,SWE,female,1977-08-26,1.8,64.0,aquatics,0,0,0 -27368310,Thery Schir,SUI,male,1993-02-18,1.86,77.0,cycling,0,0,0 -559704929,Thi Anh Do,VIE,female,1996-02-09,1.65,58.0,fencing,0,0,0 -581029614,Thi Ha Thanh Phan,VIE,female,1991-10-16,1.63,48.0,gymnastics,0,0,0 -419742149,Thi Hang Vu,VIE,female,1992-05-25,1.58,47.0,wrestling,0,0,0 -89761321,Thi Huyen Nguyen,VIE,female,1993-05-19,1.58,45.0,athletics,0,0,0 -414794696,Thi Huyen Vuong,VIE,female,1992-06-22,1.55,48.0,weightlifting,0,0,0 -262269527,Thi Le Dung Nguyen,VIE,female,1985-09-09,1.68,55.0,fencing,0,0,0 -340277633,Thi Lua Nguyen,VIE,female,1991-07-24,1.65,53.0,wrestling,0,0,0 -927744714,Thi Nhu Hoa Nguyen,VIE,female,1984-02-21,1.7,60.0,fencing,0,0,0 -244236033,Thi Trang (b) Vu,VIE,female,1992-05-19,1.63,58.0,badminton,0,0,0 -666839179,Thiago Andre,BRA,male,1995-08-04,1.63,52.0,athletics,0,0,0 -176515757,Thiago Bomfim,BRA,male,1990-09-21,1.93,84.0,hockey,0,0,0 -544759251,Thiago Braz da Silva,BRA,male,1993-12-16,1.83,75.0,athletics,1,0,0 -682318749,Thiago Maia,BRA,male,1997-03-23,1.78,69.0,football,1,0,0 -79240685,Thiago Pereira,BRA,male,1986-01-26,1.87,83.0,aquatics,0,0,0 -36658255,Thiago Simon,BRA,male,1990-04-03,1.84,93.0,aquatics,0,0,0 -69081785,Thiagus dos Santos,BRA,male,1989-01-25,1.98,104.0,handball,0,0,0 -23647767,Thibault Colard,FRA,male,1992-01-13,1.87,70.0,rowing,0,0,1 -228211001,Thibault Rossard,FRA,male,1993-08-28,1.93,85.0,volleyball,0,0,0 -914516277,Thibaut Amani Danho,CIV,male,1994-01-15,1.85,82.0,aquatics,0,0,0 -814590429,Thibaut Simon,FRA,male,1983-12-18,1.92,98.0,aquatics,0,0,0 -999374064,Thibaut Vallette,FRA,male,1974-01-18,1.71,60.0,equestrian,1,0,0 -50266146,Thierry Omeyer,FRA,male,1976-11-02,1.92,93.0,handball,0,1,0 -963532102,Thijs Visser,ARU,male,1989-10-19,1.84,88.0,sailing,0,0,0 -326914230,Thint Myaat,MYA,male,2002-04-14,1.6,52.0,aquatics,0,0,0 -172048822,Thomas Baroukh,FRA,male,1987-12-15,1.83,70.0,rowing,0,0,1 -849127949,Thomas Barr,IRL,male,1992-07-24,1.83,70.0,athletics,0,0,0 -714454697,Thomas Barrows,USA,male,1987-11-02,1.86,82.0,sailing,0,0,0 -896818368,Thomas Boudat,FRA,male,1994-02-24,1.75,69.0,cycling,0,0,0 -360675944,Thomas Briceno,CHI,male,1993-09-16,1.86,90.0,judo,0,0,0 -168124839,Thomas Briels,BEL,male,1987-08-23,1.72,71.0,hockey,0,1,0 -475825949,Thomas Daley,GBR,male,1994-05-21,1.77,70.0,aquatics,0,0,1 -360616415,Thomas Dunstan,USA,male,1997-09-29,1.94,96.0,aquatics,0,0,0 -391980322,Thomas Fabbiano,ITA,male,1989-05-26,1.73,70.0,tennis,0,0,0 -521339306,Thomas Fraser-Holmes,AUS,male,1991-10-09,1.94,85.0,aquatics,0,0,0 -868940905,Thomas Hagelskjar,DEN,male,1995-02-04,1.87,81.0,football,0,0,0 -371431214,Thomas Heurtel,FRA,male,1989-04-10,1.88,83.0,basketball,0,0,0 -112978529,Thomas Jaeschke,USA,male,1993-09-04,1.98,84.0,volleyball,0,0,1 -219077110,Thomas Jordier,FRA,male,1994-08-12,1.7,65.0,athletics,0,0,0 -94661624,Thomas Mathis,AUT,male,1990-04-25,1.78,63.0,shooting,0,0,0 -374876485,Thomas Murray,NZL,male,1994-04-05,1.89,87.0,rowing,0,0,0 -782766062,Thomas Pieters,BEL,male,1992-01-27,1.96,85.0,golf,0,0,0 -870559020,Thomas Ploessel,GER,male,1988-04-29,1.81,80.0,sailing,0,0,1 -900233130,Thomas Rohler,GER,male,1991-09-30,1.92,92.0,athletics,1,0,0 -496341298,Thomas Simart,FRA,male,1987-10-09,1.8,82.0,canoe,0,0,0 -851753829,Thomas Springer,AUT,male,1984-11-06,1.84,70.0,triathlon,0,0,0 -514467487,Thomas Zajac,AUT,male,1985-09-22,1.8,77.0,sailing,0,0,1 -666963728,Thomas van der Plaetsen,BEL,male,1990-12-24,1.86,82.0,athletics,0,0,0 -831910280,Thomaz Bellucci,BRA,male,1987-12-30,1.88,82.0,tennis,0,0,0 -854048729,Thongchai Jaidee,THA,male,1969-11-08,1.7,63.0,golf,0,0,0 -272895419,Thorbjorn Olesen,DEN,male,1989-12-21,1.77,70.0,golf,0,0,0 -711886868,Thormodur Jonsson,ISL,male,1983-03-02,1.96,110.0,judo,0,0,0 -962042754,Thulasi Tharumalingam,QAT,male,1992-10-24,1.78,,boxing,0,0,0 -494000666,Tia-Adana Belle,BAR,female,1996-06-15,1.78,59.0,athletics,0,0,0 -971688032,Tia-Clair Toomey,AUS,female,1993-07-22,1.58,58.0,weightlifting,0,0,0 -512121702,Tiago Apolonia,POR,male,1986-07-28,1.85,76.0,table tennis,0,0,0 -850164905,Tiago Camilo,BRA,male,1982-05-24,1.8,90.0,judo,0,0,0 -192124301,Tiago Ilori,POR,male,1993-02-26,1.9,80.0,football,0,0,0 -697851562,Tiago Jorge Oliveira Ferreira,POR,male,1988-12-07,1.94,74.0,cycling,0,0,0 -977600409,Tiago Silva,POR,male,1993-06-02,1.7,70.0,football,0,0,0 -802574833,Tian Miao,CHN,female,1993-01-18,1.86,83.0,rowing,0,0,0 -562349871,Tianna Bartoletta,USA,female,1985-08-30,1.68,58.0,athletics,2,0,0 -439028036,Tianshi Zhong,CHN,female,1991-02-02,1.68,62.0,cycling,1,0,0 -399009704,Tianwei Feng,SIN,female,1986-08-31,1.63,55.0,table tennis,0,0,0 -290265598,Tiberiu Dolniceanu,ROU,male,1988-04-03,1.79,79.0,fencing,0,0,0 -579357884,Tibor Hufnagel,HUN,male,1991-03-18,1.75,75.0,canoe,0,0,0 -123268106,Tibor Linka,SVK,male,1995-02-13,1.99,95.0,canoe,0,1,0 -832967742,Tien Chen Chou,TPE,male,1990-01-08,1.8,78.0,badminton,0,0,0 -307265298,Tien Minh Nguyen,VIE,male,1983-02-12,1.68,60.0,badminton,0,0,0 -334432362,Tiexin Wang,CHN,male,1989-02-24,1.87,73.0,rowing,0,0,0 -589841221,Tiffany Chan,HKG,female,1993-09-12,1.61,52.0,golf,0,0,0 -336393065,Tiffany Foster,CAN,female,1984-07-24,1.65,50.0,equestrian,0,0,0 -103328512,Tiffany Geroudet,SUI,female,1986-09-03,1.7,67.0,fencing,0,0,0 -95014047,Tiffany Porter,GBR,female,1987-11-13,1.75,70.0,athletics,0,0,0 -939411758,Tigest Getent,BRN,female,1997-07-07,,,athletics,0,0,0 -608439897,Tigist Gashaw,BRN,female,1996-12-25,,,athletics,0,0,0 -775831149,Tigist Tufa,ETH,female,1987-01-26,,,athletics,0,0,0 -487073298,Tigst Assefa,ETH,female,1996-12-03,1.68,53.0,athletics,0,0,0 -578522258,Tiidrek Nurme,EST,male,1985-11-18,1.84,70.0,athletics,0,0,0 -418178080,Tijana Bogdanovic,SRB,female,1998-05-04,1.72,52.0,taekwondo,0,1,0 -404485825,Tijana Boskovic,SRB,female,1997-03-08,1.93,82.0,volleyball,0,1,0 -28414240,Tijana Malesevic,SRB,female,1991-03-18,1.85,78.0,volleyball,0,1,0 -280943173,Tikhomir Ivanov,BUL,male,1994-07-11,1.97,76.0,athletics,0,0,0 -250232095,Tilbe Senyurek,TUR,female,1995-04-26,1.89,86.0,basketball,0,0,0 -566924681,Tim Agaba,RSA,male,1989-07-23,1.93,104.0,rugby sevens,0,0,1 -321813613,Tim Deavin,AUS,male,1984-07-27,1.85,77.0,hockey,0,0,0 -108419989,Tim Erlandsson,SWE,male,1996-12-25,1.92,79.0,football,0,0,0 -162316722,Tim Heijbrock,NED,male,1985-10-28,1.89,70.0,rowing,0,0,0 -366414312,Tim Kneale,GBR,male,1982-10-16,1.82,76.0,shooting,0,0,0 -2888213,Tim Lips,NED,male,1985-10-07,1.68,55.0,equestrian,0,0,0 -757977623,Tim Mikkelson,NZL,male,1986-08-13,1.93,102.0,rugby sevens,0,0,0 -966796753,Tim Nedow,CAN,male,1990-10-16,2.0,140.0,athletics,0,0,0 -320004654,Tim Price,NZL,male,1979-04-03,1.89,75.0,equestrian,0,0,0 -746547519,Tim Schrijver,CAN,male,1992-02-07,2.03,104.0,rowing,0,0,0 -329815777,Tim Shuttleworth,GBR,male,1997-04-24,1.92,72.0,aquatics,0,0,0 -609839139,Tim Veldt,NED,male,1984-02-14,1.86,78.0,cycling,0,0,0 -157166055,Tim Wellens,BEL,male,1991-05-10,1.83,77.0,cycling,0,0,0 -429177971,Tima Tamoi,FIJ,female,1987-11-30,1.75,70.0,rugby sevens,0,0,0 -264598644,Timaima Ravisa,FIJ,female,1988-05-01,1.6,55.0,rugby sevens,0,0,0 -978453530,Timea Babos,HUN,female,1993-05-10,1.79,68.0,tennis,0,0,0 -693524589,Timea Bacsinszky,SUI,female,1989-06-08,1.71,62.0,tennis,0,1,0 -375628571,Timm Herzbruch,GER,male,1997-06-07,1.8,76.0,hockey,0,0,1 -169189076,Timo Boll,GER,male,1981-03-08,1.81,74.0,table tennis,0,0,1 -490797825,Timo Horn,GER,male,1993-05-12,1.91,89.0,football,0,1,0 -533145484,Timothey N'Guessan,FRA,male,1992-09-18,1.86,105.0,handball,0,1,0 -439125836,Timothy Cockram,IRL,male,1984-01-18,1.77,78.0,hockey,0,0,0 -114387888,Timothy Toroitich,UGA,male,1991-10-10,,,athletics,0,0,0 -170511922,Timothy Wang,USA,male,1991-08-17,1.76,65.0,table tennis,0,0,0 -88388879,Timothy Wynter,JAM,male,1996-01-16,1.87,77.0,aquatics,0,0,0 -715800350,Timur Khaidarov,KAZ,male,1996-03-28,1.85,86.0,canoe,0,0,0 -231378561,Timur Oruz,GER,male,1994-10-27,1.86,87.0,hockey,0,0,1 -503276231,Timur Safin,RUS,male,1992-08-04,1.82,82.0,fencing,1,0,1 -465038950,Tina Charles,USA,female,1988-12-05,1.93,88.0,basketball,1,0,0 -128638379,Tina Dietze,GER,female,1988-01-25,1.72,68.0,canoe,0,2,0 -763030230,Tina Mihelic,CRO,female,1988-12-30,1.76,66.0,sailing,0,0,0 -873408482,Tina Mrak,SLO,female,1988-02-06,1.65,62.0,sailing,0,0,0 -826167980,Tina Punzel,GER,female,1995-08-01,1.67,56.0,aquatics,0,0,0 -390366123,Tina Skaar,NOR,female,1993-08-31,,,taekwondo,0,0,0 -282154223,Tina Sutej,SLO,female,1988-11-07,1.73,58.0,athletics,0,0,0 -657940345,Tina Trstenjak,SLO,female,1990-08-24,1.62,65.0,judo,1,0,0 -727650750,Tindwende Thierry Sawadogo,BUR,male,1995-07-22,1.99,93.0,aquatics,0,0,0 -872228172,Ting Shao,CHN,female,1989-12-10,1.84,75.0,basketball,0,0,0 -622182387,Ting Wen Quah,SIN,female,1992-08-18,1.76,65.0,aquatics,0,0,0 -975655821,Ting Ying Huang,TPE,female,1990-05-29,1.6,,cycling,0,0,0 -738894846,Ting Zhu,CHN,female,1994-11-29,1.95,78.0,volleyball,1,0,0 -11288154,Tingmao Shi,CHN,female,1991-08-31,1.59,52.0,aquatics,2,0,0 -731978113,Tingting Liu,CHN,female,1990-01-29,1.78,87.0,athletics,0,0,0 -549917625,Tinne Wilhelmsson Silfven,SWE,female,1967-07-12,1.67,60.0,equestrian,0,0,0 -973558445,Tintu Lukka,IND,female,1989-04-26,1.63,50.0,athletics,0,0,0 -945244491,Tirfi Tsegaye,ETH,female,1984-11-25,1.62,52.0,athletics,0,0,0 -727998449,Tiril Bue,NOR,female,1993-04-26,,,sailing,0,0,0 -373463162,Tirunesh Dibaba,ETH,female,1985-06-01,1.66,50.0,athletics,0,0,1 -92191114,Tjasa Oder,SLO,female,1994-06-22,1.8,65.0,aquatics,0,0,0 -788072979,Tjasa Pintar,SLO,female,1997-02-15,1.75,65.0,aquatics,0,0,0 -472127952,Tjasa Vozel,SLO,female,1994-07-14,1.71,62.0,aquatics,0,0,0 -289284299,Tlotliso Leotlela,RSA,male,1998-05-12,1.78,74.0,athletics,0,0,0 -895052904,Toader-Andrei Gontaru,ROU,male,1993-02-07,1.9,92.0,rowing,0,0,0 -779371307,Tobias Dahm,GER,male,1987-05-23,2.05,124.0,athletics,0,0,0 -706990143,Tobias Englmaier,GER,male,1988-01-29,1.58,63.0,judo,0,0,0 -283196075,Tobias Figueiredo,POR,male,1994-02-02,1.9,80.0,football,0,0,0 -422345402,Tobias Franzmann,GER,male,1990-12-08,1.81,73.0,rowing,0,0,0 -404535189,Tobias Hauke,GER,male,1987-09-10,1.83,81.0,hockey,0,0,1 -287021554,Tobias Karlsson,SWE,male,1981-06-04,1.96,104.0,handball,0,0,0 -999276272,Tobias Reichmann,GER,male,1988-05-27,1.88,87.0,handball,0,0,1 -811535862,Tobias Scherbarth,GER,male,1985-08-17,1.95,87.0,athletics,0,0,0 -451233303,Tobin Heath,USA,female,1988-05-29,1.62,54.0,football,0,0,0 -26312714,Toea Wisil,PNG,female,1988-01-01,1.62,63.0,athletics,0,0,0 -128575502,Toghrul Asgarov,AZE,male,1992-09-17,1.7,65.0,wrestling,0,1,0 -893678601,Tom Boon,BEL,male,1990-01-25,1.84,81.0,hockey,0,1,0 -905671340,Tom Bosworth,GBR,male,1990-01-17,1.78,54.0,athletics,0,0,0 -282018185,Tom Burton,AUS,male,1990-06-27,1.8,81.0,sailing,1,0,0 -698962028,Tom Craig,AUS,male,1995-09-03,1.86,85.0,hockey,0,0,0 -115915135,Tom Cusack,AUS,male,1993-03-01,1.91,101.0,rugby sevens,0,0,0 -200377186,Tom Dumoulin,NED,male,1990-11-11,1.86,70.0,cycling,0,1,0 -967848122,Tom Farrell,GBR,male,1991-03-23,1.79,63.0,athletics,0,0,0 -694130746,Tom Grambusch,GER,male,1995-08-04,1.85,85.0,hockey,0,0,1 -217351049,Tom Kingston,AUS,male,1991-06-19,1.9,91.0,rugby sevens,0,0,0 -621529566,Tom Liebscher,GER,male,1993-08-03,1.89,89.0,canoe,1,0,0 -107180600,Tom Mitchell,GBR,male,1989-07-22,1.78,85.0,rugby sevens,0,1,0 -959754515,Tom Pelsmaekers,BEL,male,1993-01-26,1.77,81.0,sailing,0,0,0 -298179530,Tom Ramshaw,CAN,male,1991-11-18,1.83,95.0,sailing,0,0,0 -928952672,Tom Ransley,GBR,male,1985-09-06,1.98,101.0,rowing,1,0,0 -169080993,Tom Richard Goegebuer,BEL,male,1975-03-27,1.64,56.0,weightlifting,0,0,0 -771859160,Tom Shields,USA,male,1991-07-11,1.94,86.0,aquatics,1,0,0 -384204713,Toma Nikiforov,BEL,male,1993-01-25,1.9,100.0,judo,0,0,0 -915410677,Tomas,POR,male,1995-01-30,1.81,75.0,football,0,0,0 -986152006,Tomas Aguilera,MEX,male,1988-11-15,2.02,95.0,volleyball,0,0,0 -53247064,Tomas Gonzalez,CHI,male,1985-11-22,1.7,67.0,gymnastics,0,0,0 -542026026,Tomas Klobucnik,SVK,male,1990-06-21,1.85,88.0,aquatics,0,0,0 -633990854,Tomas Stanek,CZE,male,1991-06-13,1.9,125.0,athletics,0,0,0 -674282266,Tomas Walsh,NZL,male,1992-03-01,1.85,125.0,athletics,0,0,1 -324400159,Tomasz Jablonski,POL,male,1988-12-29,1.78,,boxing,0,0,0 -534870761,Tomasz Kaczor,POL,male,1989-08-04,1.84,85.0,canoe,0,0,0 -467871766,Tomasz Majewski,POL,male,1981-08-30,2.04,142.0,athletics,0,0,0 -800403831,Tomasz Polewka,POL,male,1994-08-05,2.0,96.0,aquatics,0,0,0 -211819667,Tommy Sugiarto,INA,male,1988-05-31,1.75,71.0,badminton,0,0,0 -201573939,Tomohiro Inoue,JPN,male,1987-07-17,1.7,74.0,wrestling,0,0,0 -867127082,Tomomi Aoki,JPN,female,1994-10-25,1.64,55.0,aquatics,0,0,0 -388992602,Tomomi Tanaka,JPN,female,1988-01-25,1.54,40.0,athletics,0,0,0 -940604349,Tomoya Miguchi,JPN,male,1986-04-26,1.8,67.0,modern pentathlon,0,0,0 -976832903,Tomoya Tamura,JPN,male,1992-08-20,1.78,68.0,athletics,0,0,0 -725466241,Tomoyoshi Fukushima,JPN,male,1993-06-03,1.77,75.0,aquatics,0,0,0 -711821462,Tomoyuki Matsuda,JPN,male,1975-12-12,1.74,73.0,shooting,0,0,0 -53505297,Toms Skujins,LAT,male,1991-06-15,1.8,70.0,cycling,0,0,0 -917643038,Tonci Stipanovic,CRO,male,1986-06-13,1.78,81.0,sailing,0,1,0 -189873894,Tone Wieten,NED,male,1994-03-17,2.01,102.0,rowing,0,0,1 -464586921,Toni Syarifudin,INA,male,1991-06-13,1.64,68.0,cycling,0,0,0 -605742974,Toni Wilhelm,GER,male,1983-02-05,1.82,76.0,sailing,0,0,0 -776154883,Toni-Ann Williams,JAM,female,1995-11-20,1.55,52.0,gymnastics,0,0,0 -535001481,Tonia Couch,GBR,female,1989-05-20,1.62,58.0,aquatics,0,0,0 -724384082,Tonje Angelsen,NOR,female,1990-01-17,,,athletics,0,0,0 -999578859,Tontowi Ahmad,INA,male,1987-07-18,1.79,72.0,badminton,1,0,0 -287253228,Tonu Endrekson,EST,male,1979-06-11,1.98,104.0,rowing,0,0,1 -870620846,Tony Azevedo,USA,male,1981-11-20,1.86,90.0,aquatics,0,0,0 -229204643,Tony Dodds,NZL,male,1987-06-16,1.83,68.0,triathlon,0,0,0 -238201796,Tony Martin,GER,male,1985-04-23,1.86,75.0,cycling,0,0,0 -947438087,Tony McQuay,USA,male,1990-04-16,1.81,72.0,athletics,1,0,0 -35377706,Tony Parker,FRA,male,1982-05-17,1.86,80.0,basketball,0,0,0 -479731950,Tony Tuivuna,FIJ,male,1995-03-20,1.92,77.0,football,0,0,0 -34099719,Tony Victor James Yoka,FRA,male,1992-04-27,2.0,,boxing,1,0,0 -291445847,Torben Grimmel,DEN,male,1975-11-23,1.82,90.0,shooting,0,0,0 -764236472,Tore Navrestad,NOR,male,1996-02-19,,,cycling,0,0,0 -207258213,Tori Bowie,USA,female,1990-08-27,1.76,58.0,athletics,1,1,1 -966701899,Tori Pena,IRL,female,1987-07-30,1.67,58.0,athletics,0,0,0 -219098253,Tory Nyhaug,CAN,male,1992-04-17,1.85,92.0,cycling,0,0,0 -734322281,Toshikazu Yamashita,JPN,male,1977-02-21,1.7,70.0,shooting,0,0,0 -651640660,Toshiki Masui,JPN,male,1969-11-13,1.65,58.0,equestrian,0,0,0 -550282908,Tosin Oke,NGR,male,1980-10-01,1.79,78.0,athletics,0,0,0 -166005937,Townley Haas,USA,male,1996-12-13,1.96,83.0,aquatics,1,0,0 -741082455,Tracey Lambrechs,NZL,female,1985-08-27,1.67,107.0,weightlifting,0,0,0 -393618726,Tracy Eisser,USA,female,1989-11-20,1.86,83.0,rowing,0,0,0 -729594636,Tracy Keith-Matchitt,COK,female,1990-03-30,1.67,60.0,aquatics,0,0,0 -287146482,Travis Mahoney,AUS,male,1990-07-24,1.9,81.0,aquatics,0,0,0 -286089485,Travis Stevens,USA,male,1986-02-28,1.81,80.0,judo,0,1,0 -932981795,Trayvon Bromell,USA,male,1995-07-10,1.73,70.0,athletics,0,0,0 -521984542,Trent Jones,NZL,male,1994-08-12,1.85,87.0,cycling,0,0,0 -244396184,Trevor Barry,BAH,male,1983-06-14,,,athletics,0,0,0 -824123948,Trevor Clevenot,FRA,male,1994-06-28,1.99,89.0,volleyball,0,0,0 -244800135,Trihas Gebre,ESP,female,1990-04-29,1.62,49.0,athletics,0,0,0 -638183156,Tristan Flore,FRA,male,1995-01-02,1.78,64.0,table tennis,0,0,0 -209701462,Trixi Worrack,GER,female,1981-09-28,1.59,50.0,cycling,0,0,0 -579249475,Triyatno,INA,male,1987-12-20,1.61,69.0,weightlifting,0,0,0 -555716378,Troy Doris,GUY,male,1989-04-12,1.72,72.0,athletics,0,0,0 -783577088,Tsanko Arnaudov,POR,male,1992-03-14,1.98,154.0,athletics,0,0,0 -239480068,Tsegai Tewelde,GBR,male,1989-12-08,1.72,56.0,athletics,0,0,0 -49752494,Tsendbaatar Erdenebat,MGL,male,1996-10-16,1.63,,boxing,0,0,0 -646516993,Tsepang Sello,LES,female,1997-02-23,,,athletics,0,0,0 -132965685,Tsepo Mathibelle,LES,male,1991-06-30,,,athletics,0,0,0 -169995559,Tsgabu Gebremaryam Grmay,ETH,male,1991-08-25,1.75,63.0,cycling,0,0,0 -960328623,Tsholofelo Thipe,RSA,female,1986-12-09,1.53,55.0,athletics,0,0,0 -874089469,Tsimafei Dzeinichenka,BLR,male,1986-11-05,1.86,98.0,wrestling,0,0,0 -1591635,Tsogtbaatar Tsend-Ochir,MGL,male,1996-03-16,1.7,68.0,judo,0,0,0 -811274381,Tsolmon Adiyasambuu,MGL,female,1992-11-07,1.6,52.0,judo,0,0,0 -570804014,Tsotne Machavariani,GEO,male,1997-09-26,1.8,75.0,shooting,0,0,0 -950036284,Tsubasa Sasaki,JPN,male,1995-03-23,1.7,60.0,canoe,0,0,0 -760127003,Tsukasa Shiotani,JPN,male,1988-12-05,1.82,80.0,football,0,0,0 -353390338,Tsvetana Pironkova,BUL,female,1987-09-13,1.78,65.0,tennis,0,0,0 -8380241,Tsvetelina Naydenova,BUL,female,1994-04-28,1.68,49.0,gymnastics,0,0,1 -671805698,Tugba Guvenc,TUR,female,1994-07-09,1.73,52.0,athletics,0,0,0 -720168381,Tugce Canitez,TUR,female,1990-11-10,1.88,85.0,basketball,0,0,0 -709022430,Tugce Sahutoglu,TUR,female,1988-05-01,1.8,120.0,athletics,0,0,0 -313585891,Tuiana Dashidorzhieva,RUS,female,1996-04-14,1.69,57.0,archery,0,1,0 -520039315,Tumurkhuleg Davaadorj,MGL,male,1990-09-29,1.72,70.0,judo,0,0,0 -176135968,Tunde Szabo,HUN,female,1989-02-08,1.54,46.0,athletics,0,0,0 -863102848,Tutya Yilmaz,TUR,female,1999-06-04,1.5,48.0,gymnastics,0,0,0 -164349169,Tuula Tenkanen,FIN,female,1990-08-11,1.67,69.0,sailing,0,0,0 -589830090,Tuuli Petaja-Siren,FIN,female,1983-11-09,1.69,59.0,sailing,0,0,0 -300389824,Tuvshinbat Byamba,MGL,male,1987-03-27,1.74,,boxing,0,0,0 -848114193,Tuvshinbayar Naidan,MGL,male,1984-06-01,1.78,104.0,judo,0,0,0 -380875835,Twan van Gendt,NED,male,1992-06-09,1.78,85.0,cycling,0,0,0 -503739035,Tyla Nathan-Wong,NZL,female,1994-07-01,1.63,58.0,rugby sevens,0,1,0 -737278135,Tyler Martin,AUS,male,1990-06-28,1.96,98.0,aquatics,0,0,0 -87568178,Tyler Mislawchuk,CAN,male,1994-08-19,1.72,59.0,triathlon,0,0,0 -42726050,Tyler Nase,USA,male,1990-08-30,1.83,71.0,rowing,0,0,0 -834107099,Tyler Sanders,CAN,male,1991-12-14,1.91,81.0,volleyball,0,0,0 -77544133,Tynia Gaither,BAH,female,1993-03-16,1.58,,athletics,0,0,0 -930293846,Tyroane Sandows,RSA,male,1995-02-12,1.72,61.0,football,0,0,0 -356052736,Tyrone Smith,BER,male,1984-08-07,1.83,74.0,athletics,0,0,0 -259271156,Tyson Gay,USA,male,1982-08-09,1.81,80.0,athletics,0,0,0 -189270629,Tze Liang Ooi,MAS,male,1993-11-19,1.7,68.0,aquatics,0,0,0 -504555048,Tzu Ying Tai,TPE,female,1994-06-20,1.62,57.0,badminton,0,0,0 -991287855,Tzu-Chi Lin,TPE,female,1988-03-19,1.58,63.0,weightlifting,0,0,0 -784808590,Ubaldina Valoyes Cuesta,COL,female,1982-07-06,1.62,74.0,weightlifting,0,0,0 -98938624,Ugo Crousillat,FRA,male,1990-10-27,1.9,94.0,aquatics,0,0,0 -2045907,Uhunoma Osazuwa,NGR,female,1987-11-23,1.75,65.0,athletics,0,0,0 -617399912,Uijo Hwang,KOR,male,1992-08-28,,,football,0,0,0 -662685854,Uilson,BRA,male,1994-04-28,1.86,80.0,football,1,0,0 -674769379,Uitumen Orgodol,MGL,male,1989-04-29,1.75,82.0,wrestling,0,0,0 -13916415,Uladzislau Hancharou,BLR,male,1995-12-02,1.73,66.0,gymnastics,1,0,0 -646451751,Uladzislau Pramau,BLR,male,1984-08-03,1.77,66.0,athletics,0,0,0 -902352891,Ulrich Kirchhoff,UKR,male,1967-08-09,1.9,86.0,equestrian,0,0,0 -605302051,Umutcan Emektas,TUR,male,1991-06-29,1.82,81.0,athletics,0,0,0 -680257961,Un Hyang Kim,PRK,female,1991-10-21,1.6,54.0,aquatics,0,0,0 -556048033,Un Jong Hong,PRK,female,1989-03-09,1.56,47.0,gymnastics,0,0,0 -578632185,Un Ju Kang,PRK,female,1995-02-01,1.68,60.0,archery,0,0,0 -163301823,Unurbat Purevjav,MGL,male,1988-02-15,1.7,80.0,wrestling,0,0,0 -624095019,Urantsetseg Munkhbat,MGL,female,1990-03-14,1.64,51.0,judo,0,0,0 -988341584,Urata Rama,KOS,female,1986-12-20,1.73,64.0,shooting,0,0,0 -958189284,Urban Lesjak,SLO,male,1990-08-24,1.87,103.0,handball,0,0,0 -725017162,Uros CUCKOVIC,MNE,male,1990-04-25,1.99,101.0,aquatics,0,0,0 -705134000,Uros Zorman,SLO,male,1980-01-09,1.9,98.0,handball,0,0,0 -370406221,Ursa Kragelj,SLO,female,1988-07-02,1.66,56.0,canoe,0,0,0 -586282820,Ursula Gonzalez Garate,MEX,female,1991-11-22,1.71,71.0,fencing,0,0,0 -718417906,Ursula Grobler,RSA,female,1980-02-06,1.73,60.0,rowing,0,0,0 -88669293,Ursula Wikstrom,FIN,female,1980-07-03,1.66,55.0,golf,0,0,0 -579416064,Usain Bolt,JAM,male,1986-08-21,1.96,95.0,athletics,3,0,0 -149615945,Uschi Freitag,NED,female,1989-08-19,1.67,68.0,aquatics,0,0,0 -530272776,Ushangi Kokauri,AZE,male,1992-01-10,1.95,135.0,judo,0,0,0 -39407530,Usman Muhammed,NGR,male,1994-03-02,1.69,,football,0,0,1 -308104666,Uthappa Sannuvanda,IND,male,1993-12-02,1.83,84.0,hockey,0,0,0 -67296664,Uuganbaatar Otgonbaatar,MGL,male,1988-02-19,1.78,81.0,judo,0,0,0 -653685150,Uvis Kalnins,LAT,male,1993-10-24,1.92,85.0,aquatics,0,0,0 -409519783,Uwe Gensheimer,GER,male,1986-10-26,1.88,88.0,handball,0,0,1 -878263725,V Shem Goh,MAS,male,1989-05-20,1.8,70.0,badminton,0,1,0 -700082082,V. Sindhu Pusarla,IND,female,1995-07-05,1.79,65.0,badminton,0,1,0 -645060029,Vadim Anokhin,RUS,male,1992-01-02,1.92,91.0,fencing,0,0,0 -818449053,Vadim Kaptur,BLR,male,1987-07-12,1.74,69.0,aquatics,0,0,0 -609128454,Vadim Skorovarov,UZB,male,1996-08-04,1.77,67.0,shooting,0,0,0 -620954526,Vadzim Lialin,BLR,male,1982-11-15,2.0,100.0,rowing,0,0,0 -446977875,Vadzim Straltsou,BLR,male,1986-04-30,1.7,94.0,weightlifting,0,1,0 -723818458,Vagner Junior Souta,BRA,male,1991-02-10,1.87,84.0,canoe,0,0,0 -751743792,Vahan Mkhitaryan,ARM,male,1996-08-16,1.86,92.0,aquatics,0,0,0 -348559533,Vaida Zusinaite,LTU,female,1988-01-13,1.67,50.0,athletics,0,0,0 -655768386,Vaidas Kariniauskas,LTU,male,1993-11-16,1.97,,basketball,0,0,0 -180683514,Vaipava Nevo Ioane,SAM,male,1988-04-14,1.52,62.0,weightlifting,0,0,0 -976373633,Valdas Dopolskas,LTU,male,1992-04-30,1.83,69.0,athletics,0,0,0 -839835613,Valdivia,BRA,male,1994-10-04,,,football,0,0,0 -807344614,Valent Sinkovic,CRO,male,1988-08-02,1.87,93.0,rowing,1,0,0 -720426877,Valentin Belaud,FRA,male,1992-09-16,1.81,73.0,modern pentathlon,0,0,0 -142185153,Valentin Demyanenko,AZE,male,1983-10-23,1.93,93.0,canoe,0,1,0 -594179521,Valentin Onfroy,FRA,male,1993-11-16,1.95,84.0,rowing,0,0,0 -177896250,Valentin Porte,FRA,male,1990-09-07,1.9,92.0,handball,0,1,0 -371546325,Valentin Prades,FRA,male,1992-09-29,1.94,90.0,modern pentathlon,0,0,0 -908116241,Valentin Verga,NED,male,1989-10-07,1.8,87.0,hockey,0,0,0 -236754653,Valentina Ardean Elisei,ROU,female,1982-06-05,1.72,64.0,handball,0,0,0 -961400674,Valentina Gustin,CRO,female,1996-11-20,1.72,61.0,shooting,0,0,0 -332548817,Valentina Kibalnikova,UZB,female,1990-10-16,1.74,56.0,athletics,0,0,0 -943412077,Valentina Kogan,ARG,female,1980-02-19,1.73,71.0,handball,0,0,0 -480835097,Valentina Liashenko,GEO,female,1981-01-30,1.76,63.0,athletics,0,0,0 -285574820,Valentina Moscatt,ITA,female,1987-03-16,1.52,48.0,judo,0,0,0 -532724520,Valentina Rodini,ITA,female,1995-01-28,1.67,54.0,rowing,0,0,0 -352760379,Valentina Truppa,ITA,female,1986-03-18,1.6,55.0,equestrian,0,0,0 -833598839,Valentino Gallo,ITA,male,1985-07-17,1.92,95.0,aquatics,0,0,1 -125173784,Valentino Manfredonia,ITA,male,1989-09-29,1.8,,boxing,0,0,0 -809873385,Valentyna Myronchuk,UKR,female,1994-08-10,1.67,55.0,athletics,0,0,0 -44861563,Valeria Bianchi,ARG,female,1985-09-16,1.7,62.0,handball,0,0,0 -645890847,Valeria Straneo,ITA,female,1976-04-05,1.65,45.0,athletics,0,0,0 -876235051,Valerian Sauveplane,FRA,male,1980-07-25,1.8,77.0,shooting,0,0,0 -33162690,Valeriane Ayayi,FRA,female,1994-04-29,1.84,72.0,basketball,0,0,0 -107671084,Valerie Adams,NZL,female,1984-10-06,1.93,120.0,athletics,0,1,0 -457663173,Valerie Gruest Slowing,GUA,female,1999-03-14,1.72,63.0,aquatics,0,0,0 -10196334,Valerii Andriitsev,UKR,male,1987-02-27,1.81,97.0,wrestling,0,0,0 -977206167,Valeriia Gudym,UKR,female,1995-03-01,1.72,54.0,gymnastics,0,0,0 -975146982,Valeriia Koblova Zholobova,RUS,female,1992-10-09,1.64,58.0,wrestling,0,1,0 -704663853,Valeriu Duminica,MDA,male,1987-04-08,1.75,81.0,judo,0,0,0 -953876771,Valeriya Davidova,UZB,female,1997-12-15,1.68,48.0,gymnastics,0,0,0 -4089378,Valeriya Pischelina,BLR,female,1995-02-27,1.7,50.0,gymnastics,0,0,0 -898179597,Valmir Berisha,SWE,male,1996-06-06,1.82,80.0,football,0,0,0 -624026807,Vandana Katariya,IND,female,1992-04-15,1.59,48.0,hockey,0,0,0 -416201651,Vanessa Boslak,FRA,female,1982-06-11,1.7,57.0,athletics,0,0,0 -287472239,Vanessa Chefer,BRA,female,1990-03-05,1.78,68.0,athletics,0,0,0 -87607313,Vanessa Cozzi,BRA,female,1984-03-25,1.7,57.0,rowing,0,0,0 -321151857,Vanessa Ferrari,ITA,female,1990-11-10,1.45,45.0,gymnastics,0,0,0 -638923697,Vanessa Garcia,PUR,female,1984-07-18,1.73,57.0,aquatics,0,0,0 -648578848,Vanessa Grimberg,GER,female,1993-01-28,1.76,63.0,aquatics,0,0,0 -93934997,Vanessa Lunga,ZIM,female,1994-06-16,1.63,70.0,football,0,0,0 -931127637,Vanessa Rial,ESP,female,1982-03-01,1.72,68.0,rugby sevens,0,0,0 -205431593,Vanessa Zambotti,MEX,female,1982-03-04,1.75,125.0,judo,0,0,0 -952439055,Vania Neves,POR,female,1990-09-04,1.71,61.0,aquatics,0,0,0 -853711227,Varlam Liparteliani,GEO,male,1989-02-27,1.87,94.0,judo,0,1,0 -739791788,Varvara Filiou,GRE,female,1994-12-29,1.65,45.0,gymnastics,0,0,0 -499001095,Vasek Pospisil,CAN,male,1990-06-23,1.94,87.0,tennis,0,0,0 -488491698,Vashti Cunningham,USA,female,1998-01-18,1.86,55.0,athletics,0,0,0 -877590829,Vasil Kiryienka,BLR,male,1981-06-28,1.82,75.0,cycling,0,0,0 -937539913,Vasilii Egorov,RUS,male,1993-09-16,1.6,,boxing,0,0,0 -80619746,Vasilij Zbogar,SLO,male,1975-10-04,1.89,98.0,sailing,0,1,0 -374718100,Vasiliki Millousi,GRE,female,1984-05-04,1.57,45.0,gymnastics,0,0,0 -989508305,Vasilisa Marzaliuk,BLR,female,1987-06-23,1.8,77.0,wrestling,0,0,0 -484731780,Vasily Mosin,RUS,male,1972-05-09,1.83,70.0,shooting,0,0,0 -582848333,Vasily Pogreban,RUS,male,1989-06-26,1.81,91.0,canoe,0,0,0 -1152723,Vassiliki Vougiouka,GRE,female,1986-06-20,1.81,70.0,fencing,0,0,0 -235882258,Vassiliy Levit,KAZ,male,1988-02-24,1.85,,boxing,0,1,0 -759852624,Vatemo Ravouvou,FIJ,male,1990-07-31,1.71,81.0,rugby sevens,1,0,0 -623335062,Vazha Margvelashvili,GEO,male,1993-10-03,1.67,66.0,judo,0,0,0 -83660833,Veerle Dejaeghere,BEL,female,1973-08-01,1.59,46.0,athletics,0,0,0 -655301496,Vegard Stake Laengen,NOR,male,1989-02-07,,,cycling,0,0,0 -356549350,Veli-Matti Partanen,FIN,male,1991-10-28,1.81,63.0,athletics,0,0,0 -193269452,Velimir Stjepanovic,SRB,male,1993-08-07,1.82,74.0,aquatics,0,0,0 -37625294,Vendula Frintova,CZE,female,1983-09-04,1.68,50.0,triathlon,0,0,0 -967486655,Venilton Teixeira,BRA,male,1995-09-06,1.82,58.0,taekwondo,0,0,0 -714368005,Ventsislav Aydarski,BUL,male,1991-02-17,1.68,60.0,aquatics,0,0,0 -137700019,Venus Williams,USA,female,1980-06-17,1.86,74.0,tennis,0,1,0 -322708523,Vera Adrian,NAM,female,1993-10-28,1.68,57.0,cycling,0,0,0 -58043295,Vera Barbosa,POR,female,1989-01-13,1.68,58.0,athletics,0,0,0 -617188961,Vera Biriukova,RUS,female,1998-04-11,1.68,47.0,gymnastics,1,0,0 -867313353,Vera Vetrova,RUS,female,1986-08-21,1.8,73.0,volleyball,0,0,0 -735168991,Vera van Pol,NED,female,1993-12-17,1.57,54.0,gymnastics,0,0,0 -246757906,Veronica Bertolini,ITA,female,1995-10-19,1.67,48.0,gymnastics,0,0,0 -156804186,Veronica Campbell-Brown,JAM,female,1982-05-15,1.68,58.0,athletics,0,1,0 -92890058,Veronica Cepede Royg,PAR,female,1992-01-21,1.63,65.0,tennis,0,0,0 -436719037,Veronica Inglese,ITA,female,1990-11-22,1.6,43.0,athletics,0,0,0 -386589042,Veronica Kristiansen,NOR,female,1990-07-10,1.75,,handball,0,0,1 -19586649,Veronika Ivasiuk,UKR,female,1995-10-12,1.7,57.0,weightlifting,0,0,0 -362735511,Veronika Kozelska Fenclova,CZE,female,1981-01-21,1.71,70.0,sailing,0,0,0 -501784075,Veronika Macarol,SLO,female,1987-03-28,1.78,68.0,sailing,0,0,0 -464186121,Veronika Marchenko,UKR,female,1993-04-03,1.57,48.0,archery,0,0,0 -405423858,Veronika Popova,RUS,female,1991-01-20,1.82,68.0,aquatics,0,0,0 -497079300,Veronika Yesipovich,BLR,female,1996-04-10,1.66,48.0,aquatics,0,0,0 -580004143,Vesa Tornroos,FIN,male,1982-09-02,1.74,93.0,shooting,0,0,0 -520281849,Viacheslav Andrusenko,RUS,male,1992-05-14,1.94,85.0,aquatics,0,0,0 -140638063,Viacheslav Krasilnikov,RUS,male,1991-04-28,1.95,90.0,volleyball,0,0,0 -454698818,Vicenc Ruiz,ESP,male,1991-10-30,1.81,70.0,hockey,0,0,0 -721846159,Vicente Hernandez,ESP,male,1991-04-20,1.81,74.0,triathlon,0,0,0 -885291469,Vicky Holland,GBR,female,1986-01-12,1.68,56.0,triathlon,0,0,1 -690173780,Victoire Pauline L'or Ngon Ntame,CMR,female,1985-12-31,1.77,79.0,volleyball,0,0,0 -198342904,Victor Aravena,CHI,male,1990-02-05,1.66,55.0,athletics,0,0,0 -753873468,Victor Claver,ESP,male,1988-08-30,2.06,107.0,basketball,0,0,1 -616975176,Victor Cuesta,ARG,male,1998-11-19,1.64,65.0,football,0,0,0 -952852133,Victor Estrella Burgos,DOM,male,1980-08-02,1.73,65.0,tennis,0,0,0 -322853200,Victor Guzman,MEX,male,1995-02-03,1.73,72.0,football,0,0,0 -928628945,Victor Koretzky,FRA,male,1994-08-26,1.8,69.0,cycling,0,0,0 -798252460,Victor Lebedev,RUS,male,1988-03-10,1.64,57.0,wrestling,0,0,0 -536024925,Victor Martin Martin,ESP,male,1993-09-25,1.87,82.0,aquatics,0,0,0 -477856605,Victor Ortega,COL,male,1988-01-27,1.72,68.0,aquatics,0,0,0 -706996244,Victor Penalber,BRA,male,1990-05-22,1.74,81.0,judo,0,0,0 -70687418,Victor Rodriguez,VEN,male,1995-03-27,1.67,,boxing,0,0,0 -400723583,Victor Scvortov,UAE,male,1988-03-30,1.73,73.0,judo,0,0,0 -810144612,Victoria Chamorro,BRA,female,1996-07-10,1.76,78.0,aquatics,0,0,0 -718592963,Victoria Crivelli,ARG,female,1990-09-30,1.76,65.0,handball,0,0,0 -563090035,Victoria Esson,NZL,female,1991-03-06,1.74,66.0,football,0,0,0 -873800996,Victoria Folayan,USA,female,1985-05-27,1.66,72.0,rugby sevens,0,0,0 -814941630,Victoria Jurczok,GER,female,1990-03-25,1.61,56.0,sailing,0,0,0 -461914927,Victoria Kaminskaya,POR,female,1995-10-07,1.64,52.0,aquatics,0,0,0 -904905925,Victoria Lovelady,BRA,female,1986-11-29,1.62,48.0,golf,0,0,0 -665731338,Victoria Max-Theurer,AUT,female,1985-10-24,1.68,58.0,equestrian,0,0,0 -98600250,Victoria Mitchell,AUS,female,1982-04-25,1.64,47.0,athletics,0,0,0 -478490073,Victoria Thornley,GBR,female,1987-11-30,1.73,76.0,rowing,0,1,0 -42693483,Victoria Travascio,ARG,female,1988-07-14,1.61,59.0,sailing,0,0,0 -360949513,Victoria Zhilinskayte,RUS,female,1989-03-06,1.88,80.0,handball,1,0,0 -870095817,Victoria Zuloaga,ARG,female,1988-02-14,1.75,70.0,hockey,0,0,0 -142988798,Vid Hidvegi,HUN,male,1986-08-23,1.7,60.0,gymnastics,0,0,0 -842228874,Vid Kavticnik,SLO,male,1984-05-24,1.91,90.0,handball,0,0,0 -630821194,Vid Poteko,SLO,male,1991-04-05,1.94,103.0,handball,0,0,0 -127732051,Vien Nguyen Thi Anh,VIE,female,1996-11-09,1.72,60.0,aquatics,0,0,0 -827760060,Vigen Christensen,DEN,male,1994-08-15,1.82,77.0,football,0,0,0 -233722724,Vijona Kryeziu,KOS,female,1997-10-08,1.67,51.0,athletics,0,0,0 -197455500,Vikas Dahiya,IND,male,1995-05-08,1.8,69.0,hockey,0,0,0 -221050487,Vikas Gowda,IND,male,1983-07-05,2.05,135.0,athletics,0,0,0 -545176784,Viktar Staselovich,BLR,male,1994-05-28,1.88,83.0,aquatics,0,0,0 -940975705,Viktor Axelsen,DEN,male,1994-01-04,1.94,88.0,badminton,0,0,1 -970603995,Viktor Bromer,DEN,male,1993-04-20,1.94,87.0,aquatics,0,0,0 -194536089,Viktor Lorincz,HUN,male,1990-04-28,1.76,89.0,wrestling,0,0,0 -250896019,Viktor Minibaev,RUS,male,1991-07-18,1.73,65.0,aquatics,0,0,0 -494951044,Viktor Nagy,HUN,male,1984-07-24,1.98,96.0,aquatics,0,0,0 -640015435,Viktor Nemes,SRB,male,1993-07-21,1.7,75.0,wrestling,0,0,0 -602955950,Viktor Ruban,UKR,male,1981-05-24,1.78,70.0,archery,0,0,0 -547888117,Viktor Teply,CZE,male,1990-10-19,1.83,82.0,sailing,0,0,0 -336471664,Viktor Troicki,SRB,male,1986-02-10,1.9,85.0,tennis,0,0,0 -603907916,Viktoria Chaika,BLR,female,1980-12-26,1.64,50.0,shooting,0,0,0 -656515082,Viktoria Egri,HUN,female,1998-01-18,1.68,59.0,shooting,0,0,0 -166768587,Viktoria Madarasz,HUN,female,1985-05-12,1.58,44.0,athletics,0,0,0 -409487161,Viktoria Pavlovich,BLR,female,1978-05-08,1.75,60.0,table tennis,0,0,0 -636310483,Viktoria Schwarz,AUT,female,1985-07-02,1.73,64.0,canoe,0,0,0 -120044426,Viktoria Zeynep Gunes,TUR,female,1998-06-19,1.85,66.0,aquatics,0,0,0 -797742247,Viktoriia Andreeva,RUS,female,1992-06-21,1.9,74.0,aquatics,0,0,0 -441538649,Viktoriia Kalinina,RUS,female,1988-12-08,1.83,74.0,handball,1,0,0 -535803587,Viktoriia Poliudina,KGZ,female,1989-06-29,1.65,55.0,athletics,0,0,0 -684932486,Viktoriia Turks,UKR,female,1987-10-20,1.78,78.0,judo,0,0,0 -485757978,Viktoriia Us,UKR,female,1993-04-29,1.67,63.0,canoe,0,0,0 -444006582,Viktoriya Tkachuk,UKR,female,1994-11-08,1.79,67.0,athletics,0,0,0 -517107497,Viktoriya Zyabkina,KAZ,female,1992-09-04,1.74,62.0,athletics,0,0,0 -209908723,Viliame Mata,FIJ,male,1991-10-22,1.96,106.0,rugby sevens,1,0,0 -418792764,Villo Kormos,HUN,female,1988-08-02,1.7,62.0,aquatics,0,0,0 -3169233,Vilma Pegado Nenganga,ANG,female,1996-09-12,1.7,59.0,handball,0,0,0 -45735715,Vilmarie Mojica,PUR,female,1985-08-13,1.8,63.0,volleyball,0,0,0 -537474477,Vincent Anstett,FRA,male,1982-07-26,1.78,78.0,fencing,0,0,0 -926029117,Vincent Breet,RSA,male,1993-04-26,1.95,92.0,rowing,0,0,0 -932426895,Vincent Farkas,SVK,male,1993-01-13,1.93,90.0,canoe,0,0,0 -509765681,Vincent Gerard,FRA,male,1986-12-16,1.89,100.0,handball,0,1,0 -403479855,Vincent Hancock,USA,male,1989-03-19,1.73,79.0,shooting,0,0,0 -925594598,Vincent Inigo,FRA,male,1983-02-10,1.73,80.0,rugby sevens,0,0,0 -833794513,Vincent Luis,FRA,male,1989-06-27,1.77,65.0,triathlon,0,0,0 -646986835,Vincent Riendeau,CAN,male,1996-12-13,1.78,68.0,aquatics,0,0,0 -825166782,Vincent Vanasch,BEL,male,1987-12-21,1.8,78.0,hockey,0,1,0 -96494298,Vincent van der Want,NED,male,1985-10-21,1.98,90.0,rowing,0,0,0 -974436408,Vincenzo Capelli,ITA,male,1988-10-26,1.94,95.0,rowing,0,0,0 -983925141,Vincenzo Mangiacapre,ITA,male,1989-01-17,1.7,,boxing,0,0,0 -242715735,Vincenzo Nibali,ITA,male,1984-11-14,1.8,60.0,cycling,0,0,0 -969140836,Vinesh Vinesh,IND,female,1994-08-25,1.65,56.0,wrestling,0,0,0 -723726654,Viniana Riwai,FIJ,female,1991-06-06,1.65,70.0,rugby sevens,0,0,0 -253097119,Vinicius Antonelli,BRA,male,1990-03-01,1.83,82.0,aquatics,0,0,0 -29581250,Vinicius Teixeira,BRA,male,1988-04-03,1.88,110.0,handball,0,0,0 -989120045,Violah Cheptoo Lagat,KEN,female,1989-03-01,1.65,,athletics,0,0,0 -428647770,Violetta Kolobova,RUS,female,1991-07-27,1.76,64.0,fencing,0,0,1 -680801774,Virginia Bardach Martin,ARG,female,1992-04-03,1.74,57.0,aquatics,0,0,0 -569654952,Virginia Thrasher,USA,female,1997-02-28,1.55,54.0,shooting,1,0,0 -756374787,Virginie Cueff,FRA,female,1988-06-18,1.7,63.0,cycling,0,0,0 -3602282,Virimi Vakatawa,FRA,male,1992-05-01,1.85,87.0,rugby sevens,0,0,0 -424311527,Visiline Jepkesho,KEN,female,1989-12-30,1.6,43.0,athletics,0,0,0 -930876031,Vita Heine,NOR,female,1984-11-21,,,cycling,0,0,0 -955304524,Vitali Bubnovich,BLR,male,1974-11-12,1.69,70.0,shooting,0,0,0 -94067692,Vitalii Butrym,UKR,male,1991-01-10,1.8,75.0,athletics,0,0,0 -872401203,Vitalina Batsarashkina,RUS,female,1996-10-01,1.62,60.0,shooting,0,1,0 -628786964,Vitaliy Khudyakov,KAZ,male,1994-08-07,1.86,79.0,aquatics,0,0,0 -806574554,Vitaly Dunaytsev,RUS,male,1992-04-12,1.74,,boxing,0,0,1 -310488544,Vitaly Fokeev,RUS,male,1974-02-15,1.8,45.0,shooting,0,0,0 -819853942,Vitezslav Gebas,CZE,male,1984-03-24,1.79,75.0,canoe,0,0,0 -87145914,Vitezslav Vesely,CZE,male,1983-02-27,1.86,93.0,athletics,0,0,0 -990500234,Vitiny Hemthon,CAM,female,1993-10-07,1.63,50.0,aquatics,0,0,0 -411101006,Vitor Benite,BRA,male,1990-02-20,1.9,88.0,basketball,0,0,0 -987131879,Vitor Hugo dos Santos,BRA,male,1996-02-01,1.85,74.0,athletics,0,0,0 -623036229,Vitoria Cristina Rosa,BRA,female,1996-01-12,1.7,60.0,athletics,0,0,0 -506125969,Vittorio Bissaro,ITA,male,1987-06-01,1.83,72.0,sailing,0,0,0 -239639221,Vivian Jepkemoi Cheruiyot,KEN,female,1983-09-11,1.54,40.0,athletics,1,1,0 -527372670,Vivian Kah Mun Hoo,MAS,female,1990-03-19,1.66,56.0,badminton,0,0,0 -563216361,Viviana Chavez,ARG,female,1987-05-28,1.64,52.0,athletics,0,0,0 -110185191,Viviane Bahia,BRA,female,1994-02-14,1.76,67.0,aquatics,0,0,0 -696988201,Vjekoslav Paskovic,MNE,male,1985-03-23,1.8,86.0,aquatics,0,0,0 -732110114,Vlad-Dragos Aicoboae,ROU,male,1993-10-10,1.97,91.0,rowing,0,0,0 -843584108,Vlada Chigireva,RUS,female,1994-12-18,1.62,48.0,aquatics,1,0,0 -156876267,Vladimer Khinchegashvili,GEO,male,1991-04-18,1.7,57.0,wrestling,1,0,0 -635923594,Vladimir Gontcharov,RUS,male,1977-05-04,1.68,58.0,shooting,0,0,0 -878150693,Vladimir Isakov,RUS,male,1970-02-28,1.78,83.0,shooting,0,0,0 -407568391,Vladimir Issachenko,KAZ,male,1982-12-27,1.95,90.0,shooting,0,0,0 -431379852,Vladimir Ivanov,RUS,male,1987-07-03,1.98,93.0,badminton,0,0,0 -481376047,Vladimir Letnicov,MDA,male,1981-10-07,1.78,70.0,athletics,0,0,0 -557934502,Vladimir Malkov,RUS,male,1986-04-09,1.88,79.0,badminton,0,0,0 -229367719,Vladimir Margaryan,ARM,male,1991-03-08,1.72,,boxing,0,0,0 -592631027,Vladimir Maslennikov,RUS,male,1994-08-17,1.71,73.0,shooting,0,0,1 -108548232,Vladimir Morozov,RUS,male,1992-06-16,,,aquatics,0,0,0 -610704045,Vladimir Nikitin,RUS,male,1990-03-25,1.65,,boxing,0,0,1 -53064858,Vladimir Samsonov,BLR,male,1976-04-17,1.89,83.0,table tennis,0,0,0 -559441315,Vladimir Savanovic,SRB,male,1985-06-12,1.8,70.0,athletics,0,0,0 -54519941,Vladimir Stimac,SRB,male,1987-08-25,2.11,112.0,basketball,0,1,0 -795903491,Vladimir Torubarov,SRB,male,1993-03-22,1.91,94.0,canoe,0,0,0 -784092954,Vladimir Vladimirov Dubov,BUL,male,1988-02-20,1.56,64.0,wrestling,0,0,0 -230293143,Vladislav Mustafin,UZB,male,1995-09-26,1.81,78.0,aquatics,0,0,0 -889300340,Vladislav Ryabcev,RUS,male,1987-12-13,1.96,96.0,rowing,0,0,0 -207226972,Vladislav Yakovlev,KAZ,male,1993-01-01,1.88,85.0,rowing,0,0,0 -316731530,Vladlena Bobrovnikova,RUS,female,1987-10-24,1.8,72.0,handball,1,0,0 -122222,Vladyslav Hryko,UKR,male,1997-01-25,1.69,57.0,gymnastics,0,0,0 -771388004,Vlasios Maras,GRE,male,1983-03-31,1.6,52.0,gymnastics,0,0,0 -854069870,Volha Khudzenka,BLR,female,1992-05-12,1.78,78.0,canoe,0,0,1 -559528855,Volha Mazuronak,BLR,female,1989-04-14,1.65,49.0,athletics,0,0,0 -443487207,Volha Sudarava,BLR,female,1984-02-22,1.76,61.0,athletics,0,0,0 -365006989,Volha Ziuzkova,BLR,female,1983-06-14,1.71,69.0,basketball,0,0,0 -109518008,Volodymyr Hoza,UKR,male,1996-04-15,1.83,94.0,weightlifting,0,0,0 -204414092,Volodymyr Matviichuk,UKR,male,1982-12-29,1.71,,boxing,0,0,0 -826347697,Vsevolod Zanko,RUS,male,1995-07-30,1.9,85.0,aquatics,0,0,0 -855823071,Vyron Kokkalanis,GRE,male,1985-08-19,1.86,75.0,sailing,0,0,0 -32220093,Wadha Al Balushi,OMA,female,1989-11-30,1.6,75.0,shooting,0,0,0 -751514676,Wael Jallouz,TUN,male,1991-05-03,1.97,105.0,handball,0,0,0 -754088322,Wagner Domingos,BRA,male,1983-03-26,1.87,100.0,athletics,0,0,0 -539224149,Waheed Abdulridha Waheed Karaawi,IRQ,male,1983-05-22,1.78,,boxing,0,0,0 -620093926,Wai Sze Lee,HKG,female,1987-05-12,1.65,62.0,cycling,0,0,0 -419317871,Walace,BRA,male,1995-04-04,1.88,75.0,football,1,0,0 -929418601,Walid Bidani,ALG,male,1994-06-11,1.85,123.0,weightlifting,0,0,0 -422425972,Walid Mohamed,EGY,male,1993-08-22,1.67,,boxing,0,0,0 -194921763,Walide Khyar,FRA,male,1995-06-09,1.65,60.0,judo,0,0,0 -344970215,Wallace de Souza,BRA,male,1987-06-26,1.98,87.0,volleyball,1,0,0 -202452253,Walton Eller,USA,male,1982-01-06,1.88,81.0,shooting,0,0,0 -788654634,Wan Ho Son,KOR,male,1988-05-17,1.76,70.0,badminton,0,0,0 -850200304,Wander Mateo,DOM,male,1989-12-24,1.73,66.0,judo,0,0,0 -268487166,Wannes van Laer,BEL,male,1985-03-05,1.81,81.0,sailing,0,0,0 -187564963,Warren Barguil,FRA,male,1991-10-28,1.84,62.0,cycling,0,0,0 -781765976,Warren Potent,AUS,male,1962-04-07,1.77,73.0,shooting,0,0,0 -258919182,Wataru Endo,JPN,male,1993-02-09,1.78,75.0,football,0,0,0 -495782479,Wataru Yazawa,JPN,male,1991-07-02,1.77,64.0,athletics,0,0,0 -190206268,Wayde van Niekerk,RSA,male,1992-07-15,1.83,70.0,athletics,1,0,0 -437812263,Wayne Snyman,RSA,male,1985-03-08,1.77,65.0,athletics,0,0,0 -686326172,Wee Kiong Tan,MAS,male,1989-05-21,1.77,77.0,badminton,0,1,0 -59547454,Wei Deng,CHN,female,1993-02-14,1.59,62.0,weightlifting,1,0,0 -843515403,Wei Hong,CHN,male,1989-10-04,1.92,86.0,badminton,0,0,0 -739056222,Wei Jin,CHN,male,1987-02-26,1.83,70.0,rowing,0,0,0 -450932930,Wei Liu,CHN,male,1987-11-27,1.75,,boxing,0,0,0 -358145132,Wei Pang,CHN,male,1986-07-19,1.78,77.0,shooting,0,0,1 -520932256,Wei Sun,CHN,male,1992-10-27,1.93,69.0,fencing,0,0,0 -416880116,Wei Wang,CHN,male,1988-07-07,1.8,70.0,sailing,0,0,0 -253018225,Wei Yu,CHN,male,1987-09-11,1.8,55.0,athletics,0,0,0 -434190912,Wei-Ling Chen,TPE,female,1982-01-04,1.49,47.0,weightlifting,0,0,0 -711688611,Wei-Ting Liu,TPE,male,1995-01-06,1.96,81.0,taekwondo,0,0,0 -42043973,Weiwei Zhang,CHN,female,1990-10-07,1.82,66.0,aquatics,0,0,0 -254471828,Weiwei Zhu,CHN,female,1990-05-22,1.8,70.0,rowing,0,0,0 -114920742,Welisson Rosa da Silva,BRA,male,1983-11-22,1.6,85.0,weightlifting,0,0,0 -676724984,Welson Sim,MAS,male,1997-03-29,1.81,70.0,aquatics,0,0,0 -682325171,Wen Lu,CHN,female,1990-02-26,1.88,78.0,basketball,0,0,0 -272832003,Wen-Ling Chen,TPE,female,1994-08-16,1.75,69.0,wrestling,0,0,0 -447697493,Wen-Tang Lin,TPE,male,1974-06-28,1.74,77.0,golf,0,0,0 -74774569,Wenda Nel,RSA,female,1988-07-30,1.65,57.0,athletics,0,0,0 -465452582,Wendie Renard,FRA,female,1990-07-20,1.87,70.0,football,0,0,0 -406064567,Wendy Cornejo,BOL,female,1993-01-07,1.62,54.0,athletics,0,0,0 -628804962,Wenjun Guo,CHN,female,1984-06-22,1.68,60.0,shooting,0,0,0 -992307110,Wenjun Ren,CHN,female,1992-01-15,1.75,67.0,canoe,0,0,0 -566882529,Wenjun Xie,CHN,male,1990-07-11,1.9,87.0,athletics,0,0,0 -111882527,Wenna He,CHN,female,1989-01-19,1.6,50.0,gymnastics,0,0,0 -886760487,Wenxiu Zhang,CHN,female,1986-03-22,1.83,105.0,athletics,0,1,0 -526167499,Wenyan Sun,CHN,female,1989-12-27,1.7,58.0,aquatics,0,2,0 -71120079,Wenyi Huang,CHN,female,1991-03-06,1.78,63.0,rowing,0,0,1 -466231516,Werner Kok,RSA,male,1993-01-17,1.8,91.0,rugby sevens,0,0,1 -487398764,Weronika Deresz,POL,female,1985-09-05,1.7,57.0,rowing,0,0,0 -530905134,Wesley Korir,KEN,male,1982-11-15,1.77,54.0,athletics,0,0,0 -934575114,Wesley Roberts,COK,male,1997-06-24,1.88,74.0,aquatics,0,0,0 -539265480,Wesley Vazquez,PUR,male,1994-03-27,1.92,77.0,athletics,0,0,0 -455850470,Weverton,BRA,male,1987-12-13,1.86,75.0,football,1,0,0 -81682988,Whitney Ashley,USA,female,1989-02-18,1.76,90.0,athletics,0,0,0 -146065119,Whitney Engen,USA,female,1987-11-28,1.72,56.0,football,0,0,0 -916383501,Wiam Dislam,MAR,female,1987-10-22,1.8,69.0,taekwondo,0,0,0 -674266190,Wianka van Dorp,NED,female,1987-12-01,1.77,72.0,rowing,0,0,0 -30448632,Wiktor Chabel,POL,male,1985-11-23,1.97,93.0,rowing,0,0,0 -973792476,Wilfried Bingangoye,GAB,male,1985-03-25,1.72,80.0,athletics,0,0,0 -637431753,Wilhem Belocian,FRA,male,1995-06-22,1.78,78.0,athletics,0,0,0 -667133584,Will Brown,USA,male,1991-12-31,1.71,61.0,shooting,0,0,0 -745203052,Will Claye,USA,male,1991-06-13,1.81,72.0,athletics,0,1,0 -202043393,Will Crothers,CAN,male,1987-06-14,1.95,95.0,rowing,0,0,0 -212657714,Will Dean,CAN,male,1987-06-10,1.95,95.0,rowing,0,0,0 -971499315,Will Fletcher,GBR,male,1989-12-24,1.86,73.0,rowing,0,0,0 -878681107,Will Godward,AUS,male,1984-04-15,1.89,95.0,shooting,0,0,0 -281145945,Will Ryan,AUS,male,1988-12-23,1.93,75.0,sailing,0,1,0 -323245205,Willem Coertzen,RSA,male,1982-12-30,1.86,79.0,athletics,0,0,0 -73209628,Willem van Schuerbeeck,BEL,male,1984-10-24,1.79,60.0,athletics,0,0,0 -860282405,Willemijn Bos,NED,female,1988-05-02,1.81,69.0,hockey,0,1,0 -550290516,William,BRA,male,1995-04-03,1.71,65.0,football,1,0,0 -624517317,William Arjona,BRA,male,1979-07-31,1.85,78.0,volleyball,1,0,0 -151807374,William Chetcuti,MLT,male,1985-01-07,1.8,93.0,shooting,0,0,0 -63879528,William Collazo,CUB,male,1986-08-31,1.72,71.0,athletics,0,0,0 -213332524,William Ekong,NGR,male,1993-09-01,1.75,,football,0,0,1 -410962720,William Fox-Pitt,GBR,male,1969-01-02,1.96,80.0,equestrian,0,0,0 -974426000,William Lockwood,AUS,male,1988-05-13,1.91,88.0,rowing,0,1,0 -177039593,William Meynard,FRA,male,1987-07-11,1.92,85.0,aquatics,0,1,0 -150307137,William Reid Priddy,USA,male,1977-10-01,1.94,89.0,volleyball,0,0,1 -886806565,William Satch,GBR,male,1989-06-09,1.98,100.0,rowing,1,0,0 -770119639,William Tesillo,COL,male,1990-02-02,1.86,76.0,football,0,0,0 -925824275,Willian Giaretton,BRA,male,1990-09-26,1.93,73.0,rowing,0,0,0 -1567734,Willie Ambaka,KEN,male,1990-05-14,1.93,100.0,rugby sevens,0,0,0 -879180304,Willy Hernangomez,ESP,male,1994-05-27,2.1,115.0,basketball,0,0,1 -580584287,Wilma Murto,FIN,female,1998-06-11,1.81,68.0,athletics,0,0,0 -509216615,Wilmar Barrios,COL,male,1993-10-16,1.79,74.0,football,0,0,0 -732552796,Wim Stroetinga,NED,male,1985-05-23,1.76,70.0,cycling,0,0,0 -2681418,Windi Graterol,VEN,male,1986-09-10,2.05,110.0,basketball,0,0,0 -630860281,Winnie Nanyondo,UGA,female,1993-08-23,,,athletics,0,0,0 -266764491,Winny Chebet,KEN,female,1990-12-20,1.52,48.0,athletics,0,0,0 -425181289,Winston George,GUY,male,1987-05-19,1.67,66.0,athletics,0,0,0 -404382792,Winston Hill,FIJ,male,1993-09-17,,,boxing,0,0,0 -923747031,Wirimai Juwawo,ZIM,male,1980-11-07,1.72,63.0,athletics,0,0,0 -446464349,Wisam Nawar,EGY,male,1990-02-14,1.85,111.0,handball,0,0,0 -680507143,Wissem Hosni,TUN,male,1985-03-08,1.75,57.0,athletics,0,0,0 -618949367,Witoon Mingmoon,THA,male,1996-02-10,1.57,56.0,weightlifting,0,0,0 -640840803,Witthaya Thamwong,THA,male,1987-09-18,1.8,82.0,archery,0,0,0 -984494121,Wojciech Nowicki,POL,male,1989-02-22,1.96,128.0,athletics,0,0,1 -539569636,Wojciech Theiner,POL,male,1986-06-25,1.89,84.0,athletics,0,0,0 -595198365,Wojciech Wojdak,POL,male,1996-03-13,1.86,84.0,aquatics,0,0,0 -76593466,Won Chol Yun,PRK,male,1989-07-03,1.63,59.0,wrestling,0,0,0 -457937949,Won Jin Kim,KOR,male,1992-05-01,1.68,60.0,judo,0,0,0 -663385639,Wonchul Yoo,KOR,male,1984-07-20,1.65,59.0,gymnastics,0,0,0 -69693053,Woojin Kim,KOR,male,1992-06-20,1.8,95.0,archery,1,0,0 -651375784,Woongtae Jun,KOR,male,1995-08-01,1.75,66.0,modern pentathlon,0,0,0 -474296927,Wout Poels,NED,male,1987-10-01,1.86,67.0,cycling,0,0,0 -640901977,Wuileixis De Jesus Rivas Espinoza,VEN,male,1990-08-27,1.74,,wrestling,0,0,0 -851340643,Wuta Waco Bige Dombaxi,ANG,female,1986-04-05,1.8,92.0,handball,0,0,0 -332172731,Wuttichai Masuk,THA,male,1990-03-16,1.75,,boxing,0,0,0 -830874507,Xan de Waard,NED,female,1995-11-08,1.63,55.0,hockey,0,1,0 -283897896,Xantal Gine,ESP,female,1992-09-23,1.68,62.0,hockey,0,0,0 -618448909,Xavier Lleonart,ESP,male,1990-06-22,1.8,70.0,hockey,0,0,0 -9403852,Xavier Vela Maggi,BRA,male,1989-08-07,1.78,71.0,rowing,0,0,0 -175218887,Xaysa Anousone,LAO,male,1994-03-20,1.8,65.0,athletics,0,0,0 -821538769,Xenia Krizsan,HUN,female,1993-01-13,1.72,64.0,athletics,0,0,0 -933749251,Xia Ding,CHN,female,1990-01-13,1.8,67.0,volleyball,1,0,0 -304876700,Xia Lian Ni,LUX,female,1963-07-04,1.57,58.0,table tennis,0,0,0 -944403692,Xiang Li,CHN,male,1993-07-02,1.91,85.0,aquatics,0,0,0 -427125476,Xiang Liu,CHN,female,1996-09-01,1.8,66.0,aquatics,0,0,0 -371898771,Xiang Wei Jasmine Ser,SIN,female,1990-09-24,1.55,47.0,shooting,0,0,0 -591962828,Xiangyu Gong,CHN,female,1997-04-21,1.86,72.0,volleyball,1,0,0 -591353538,Xiao Gu,CHN,female,1993-03-18,1.74,60.0,aquatics,0,1,0 -695366318,Xiao Juan Diao,HKG,female,1986-03-15,1.7,59.0,cycling,0,0,0 -46661716,Xiao Sun,CHN,female,1992-06-13,1.67,57.0,hockey,0,0,0 -476593351,Xiao Yu Liang,SIN,female,1996-01-11,1.63,55.0,badminton,0,0,0 -470006570,Xiaochuan Zhai,CHN,male,1993-03-24,2.04,100.0,basketball,0,0,0 -306770484,Xiaohan Mei,CHN,female,1996-11-11,1.8,100.0,aquatics,0,0,0 -247133219,Xiaohong Li,CHN,female,1995-01-08,1.62,48.0,athletics,0,0,0 -161425340,Xiaojia Chen,CHN,female,1988-04-02,1.82,68.0,basketball,0,0,0 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Lin,CHN,female,1996-02-25,1.71,65.0,golf,0,0,0 -285179001,Xoana Iacoi,ARG,female,1992-06-03,1.61,70.0,handball,0,0,0 -56252755,Xuan Vinh Hoang,VIE,male,1974-10-06,1.75,75.0,shooting,1,1,0 -124699891,Xue Li,FRA,female,1985-04-14,1.65,57.0,table tennis,0,0,0 -773136288,Xuechen Huang,CHN,female,1990-02-25,1.75,62.0,aquatics,0,2,0 -199638855,Xuechun Zhong,CHN,female,1994-01-18,1.66,55.0,wrestling,0,0,0 -597272613,Xueer Wang,CHN,female,1998-01-15,1.78,67.0,aquatics,0,0,0 -652008471,Xuerui Li,CHN,female,1991-01-24,1.75,65.0,badminton,0,0,0 -283158368,Xuesong Gu,CHN,male,1993-06-21,1.78,76.0,archery,0,0,0 -166545543,Xunzhao Cheng,CHN,male,1991-02-09,1.85,90.0,judo,0,0,1 -152863317,Ya Tan,CHN,male,1992-07-18,1.74,70.0,canoe,0,0,0 -872511641,Ya-Ting Tan,TPE,female,1993-11-07,1.58,53.0,archery,0,0,1 -338589899,Yaaqoub Alsaadi,UAE,male,1996-06-13,1.75,62.0,aquatics,0,0,0 -137239847,Yadinys Amaris,COL,female,1984-04-01,1.62,57.0,judo,0,0,0 -552083817,Yadira Silva,MEX,female,1985-12-24,1.59,55.0,table tennis,0,0,0 -438851517,Yadisleidis Pedroso,ITA,female,1987-01-28,1.7,50.0,athletics,0,0,0 -539240729,Yael Castiglione,ARG,female,1985-09-27,1.84,75.0,volleyball,0,0,0 -626583802,Yago Lange,ARG,male,1988-03-22,1.8,77.0,sailing,0,0,0 -786911278,Yaime Perez,CUB,female,1991-05-29,1.75,74.0,athletics,0,0,0 -420817403,Yajie Si,CHN,female,1998-12-04,1.64,57.0,aquatics,0,1,0 -267368627,Yajun Li,CHN,female,1993-04-27,1.51,53.0,weightlifting,0,0,0 -549751997,Yakov Toumarkin,ISR,male,1992-02-15,1.92,90.0,aquatics,0,0,0 -445656817,Yalennis Castillo,CUB,female,1986-05-21,1.74,78.0,judo,0,0,0 -294527962,Yali Jing,CHN,female,1989-05-25,1.76,63.0,cycling,0,0,0 -380260850,Yamil Alberto Peralta,ARG,male,1991-07-16,1.92,,boxing,0,0,0 -450541833,Yan Jiang,CHN,female,1989-01-10,1.78,70.0,rowing,0,0,0 -333015501,Yan Wang,CHN,female,1991-08-22,1.75,61.0,football,0,0,0 -44353519,Yan Wang,CHN,female,1999-10-30,1.4,33.0,gymnastics,0,0,1 -664863938,Yan Yee Ng,MAS,female,1993-07-11,1.55,51.0,aquatics,0,0,0 -76793158,Yana Alekseevna,AZE,female,1987-10-30,1.69,,boxing,0,0,0 -541557997,Yana Belomoina,UKR,female,1992-11-02,1.64,46.0,cycling,0,0,0 -223640307,Yana Egorian,RUS,female,1993-12-20,1.75,64.0,fencing,2,0,0 -243275636,Yana Kudryavtseva,RUS,female,1997-09-29,1.7,47.0,gymnastics,0,1,0 -43753188,Yana Pavlova,RUS,female,1996-01-06,1.56,51.0,gymnastics,0,0,0 -435033885,Yana Shcherban,RUS,female,1989-09-06,1.85,71.0,volleyball,0,0,0 -297532714,Yana Shemyakina,UKR,female,1986-01-05,1.68,60.0,fencing,0,0,0 -218946675,Yana Tie,HKG,female,1979-05-13,1.6,59.0,table tennis,0,0,0 -308918609,Yanan Sun,CHN,female,1992-09-15,1.61,51.0,wrestling,0,0,1 -63153273,Yancarlos Martinez,DOM,male,1992-07-08,1.71,60.0,athletics,0,0,0 -599927932,Yane Marcia Marques,BRA,female,1984-01-07,1.66,55.0,modern pentathlon,0,0,0 -919768617,Yanet Ursula Sovero Nino,PER,female,1983-05-02,1.63,58.0,wrestling,0,0,0 -167053501,Yanfei Shen,ESP,female,1979-12-24,1.65,65.0,table tennis,0,0,0 -293632748,Yang Chen,CHN,female,1991-07-10,1.8,97.0,athletics,0,0,0 -136270822,Yang Fan,CHN,male,1990-01-03,1.83,78.0,cycling,0,0,0 -760332947,Yang Gao,CHN,female,1993-03-01,1.78,105.0,athletics,0,0,0 -490068469,Yang Liu,CHN,male,1994-09-10,1.62,61.0,gymnastics,0,0,1 -99159206,Yang Lyu,CHN,female,1993-11-26,1.83,77.0,rowing,0,0,0 -349451420,Yang Peng,CHN,female,1992-01-17,1.63,59.0,hockey,0,0,0 -344496691,Yang Sun,CHN,male,1991-12-01,2.0,92.0,aquatics,1,1,0 -619307407,Yang Wang,SVK,male,1994-09-24,1.83,73.0,table tennis,0,0,0 -270107002,Yang Yu,CHN,female,1986-04-07,1.66,62.0,badminton,0,0,0 -188592965,Yanhan Ai,CHN,female,2002-02-07,1.68,54.0,aquatics,0,0,0 -801724577,Yanic Gentry Torfer,MEX,male,1991-02-20,1.84,82.0,sailing,0,0,0 -846532054,Yaniel Carrero,CUB,male,1995-08-17,1.74,74.0,athletics,0,0,0 -491775896,Yanina Wickmayer,BEL,female,1989-10-20,1.82,72.0,tennis,0,0,0 -528693937,Yanislav Gerchev,BUL,male,1989-10-04,1.7,60.0,judo,0,0,0 -273498776,Yaniuska Isabel Espinosa,VEN,female,1986-12-05,1.72,114.0,weightlifting,0,0,0 -871784511,Yaniuvis Lopez,CUB,female,1986-02-01,1.8,71.0,athletics,0,0,0 -413602471,Yanmei Xiang,CHN,female,1992-06-13,1.63,69.0,weightlifting,1,0,0 -982324748,Yann Candele,CAN,male,1971-03-11,1.73,82.0,equestrian,0,0,0 -943819642,Yann Siccardi,MON,male,1986-02-03,1.64,60.0,judo,0,0,0 -260382082,Yannick Agnel,FRA,male,1992-06-09,2.01,80.0,aquatics,0,0,0 -709856654,Yannick Borel,FRA,male,1988-11-05,1.97,100.0,fencing,1,0,0 -959595922,Yannick Brauchli,SUI,male,1988-07-30,1.73,63.0,sailing,0,0,0 -796628018,Yannick Kaeser,SUI,male,1992-07-03,1.86,81.0,aquatics,0,0,0 -190574076,Yannick Lefebvre,BEL,male,1988-11-19,1.81,79.0,sailing,0,0,0 -210648871,Yanyuhang Ding,CHN,male,1993-08-20,2.0,91.0,basketball,0,0,0 -876086077,Yarden Gerbi,ISR,female,1989-07-08,1.69,63.0,judo,0,0,1 -665930240,Yared Shegumo,POL,male,1983-01-10,1.78,62.0,athletics,0,0,0 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Toledo,CUB,male,1989-09-15,1.75,,boxing,0,0,0 -557145585,Yassine Hethat,ALG,male,1991-08-30,1.75,65.0,athletics,0,0,0 -455207704,Yassine Trabelsi,TUN,male,1990-07-12,1.85,80.0,taekwondo,0,0,0 -931792230,Yasuhiro Koseki,JPN,male,1992-03-14,1.88,83.0,aquatics,0,0,0 -960225637,Yasunari Hirai,JPN,male,1990-04-02,1.75,75.0,aquatics,0,0,0 -842346625,Yating Sun,CHN,female,1988-02-24,1.8,76.0,aquatics,0,0,0 -81264500,Yauhen Tsurkin,BLR,male,1990-12-09,1.82,77.0,aquatics,0,0,0 -767771937,Yauheni Karaliou,BLR,male,1991-03-26,1.69,69.0,aquatics,0,0,0 -631259932,Yavuz Ilnam,TUR,male,1987-07-23,1.85,140.0,shooting,0,0,0 -371339083,Yawen Hou,CHN,female,1998-09-09,1.81,71.0,aquatics,0,0,0 -874979172,Yaxin Liu,CHN,female,1999-06-16,1.78,69.0,aquatics,0,0,0 -462092758,Yayoi Matsumoto,JPN,female,1990-03-08,1.68,65.0,aquatics,0,0,0 -91699110,Ye Na Chang,KOR,female,1989-12-13,1.72,61.0,badminton,0,0,0 -35665719,Ye Tun Naung,MYA,male,1983-05-26,1.75,60.0,shooting,0,0,0 -145504141,Ye Yang,CHN,female,1994-05-26,1.72,50.0,gymnastics,0,0,0 -6445762,Yehia Elderaa,EGY,male,1995-07-17,1.86,82.0,handball,0,0,0 -139859282,Yehualeye Beletew,ETH,female,1998-07-31,1.65,52.0,athletics,0,0,0 -166734609,Yeison Rivas,COL,male,1987-09-24,1.73,66.0,athletics,0,0,0 -628369292,Yeji Kim,KOR,female,1994-11-17,1.74,72.0,rowing,0,0,0 -680581412,Yekaterina Ektova,KAZ,female,1992-08-30,1.7,59.0,athletics,0,0,0 -892955705,Yekaterina Larionova,KAZ,female,1994-01-23,1.58,63.0,wrestling,0,0,1 -206089177,Yekaterina Nemich,KAZ,female,1995-01-03,1.69,47.0,aquatics,0,0,0 -247875436,Yekaterina Rudenko,KAZ,female,1994-10-16,1.8,69.0,aquatics,0,0,0 -953630497,Yekaterina Smirnova,KAZ,female,1988-05-21,1.65,63.0,canoe,0,0,0 -17150577,Yeldos Smetov,KAZ,male,1992-09-09,1.67,60.0,judo,0,1,0 -932118784,Yelena Leuchanka,BLR,female,1983-04-30,1.95,86.0,basketball,0,0,0 -805715080,Yelena Ryabova,TKM,female,1990-11-03,1.7,56.0,athletics,0,0,0 -485869325,Yelizaveta Korol,KAZ,female,1994-09-02,1.67,69.0,shooting,0,0,0 -53802020,Yelyzaveta Bryzgina,UKR,female,1989-11-28,1.73,63.0,athletics,0,0,0 -550672127,Yemane Haileselassie,ERI,male,1998-02-21,,,athletics,0,0,0 -252505900,Yemi Geoffrey Apithy,BEN,male,1989-04-05,1.92,96.0,fencing,0,0,0 -399306136,Yen-Hsun Lu,TPE,male,1983-08-14,1.8,83.0,tennis,0,0,0 -839200601,Yeon Jae Son,KOR,female,1994-05-28,1.65,45.0,gymnastics,0,0,0 -383061108,Yeon Ju Bae,KOR,female,1990-10-26,1.67,57.0,badminton,0,0,0 -699050231,Yeon Koung Kim,KOR,female,1988-02-26,1.92,73.0,volleyball,0,0,0 -769058232,Yeon Seong Yoo,KOR,male,1986-08-19,1.81,75.0,badminton,0,0,0 -664297422,Yeongeun Jeon,KOR,female,1988-05-24,1.58,43.0,athletics,0,0,0 -597746590,Yeongsin Nam,KOR,female,1990-08-27,1.75,82.0,handball,0,0,0 -765164830,Yerenman Salazar,VEN,male,1978-10-24,1.65,52.0,athletics,0,0,0 -617589431,Yerko Araya,CHI,male,1986-02-14,1.78,64.0,athletics,0,0,0 -185479822,Yeseida Carrillo,COL,female,1993-10-22,1.68,52.0,athletics,0,0,0 -5964702,Yesenia Miranda,ESA,female,1994-03-26,,,athletics,0,0,0 -640675211,Yessica Camilo Gonzalez,DOM,female,1993-02-23,1.68,73.0,archery,0,0,0 -124805111,Yessy Venisia Yosaputra,INA,female,1994-08-27,1.67,62.0,aquatics,0,0,0 -720063529,Yesui Bayar,MGL,female,2000-07-21,1.76,61.0,aquatics,0,0,0 -824906625,Yevgeniy Alexeyev,KAZ,male,1977-12-11,1.86,93.0,canoe,0,0,0 -81837062,Yevgeniy Labutov,KAZ,male,1984-11-17,1.94,120.0,athletics,0,0,0 -877812765,Yevgeniya Gomon,UKR,female,1995-03-25,1.74,55.0,gymnastics,0,0,0 -423607344,Yevhen Vynohradov,UKR,male,1984-04-30,1.95,105.0,athletics,0,0,0 -586074732,Ygor Coelho de Oliveira,BRA,male,1996-11-24,1.83,78.0,badminton,0,0,0 -130088138,Yi Chun Lin,TPE,female,1981-07-05,1.61,70.0,shooting,0,0,0 -923838612,Yi Mao,CHN,female,1999-09-16,1.51,35.0,gymnastics,0,0,1 -154566738,Yi Tang,CHN,female,1993-01-08,1.77,70.0,aquatics,0,0,0 -45723184,Yi-Hua Huang,TPE,female,1984-07-20,1.67,56.0,table tennis,0,0,0 -318947279,Yi-Ting Huang,TPE,female,1990-01-16,1.7,65.0,rowing,0,0,0 -657056905,Yidiel Contreras,ESP,male,1992-11-27,1.8,74.0,athletics,0,0,0 -666759438,Yiech Pur Biel,ROT,male,1995-01-01,1.78,62.0,athletics,0,0,0 -763147414,Yifan Xu,CHN,female,1988-08-08,1.65,60.0,tennis,0,0,0 -62526339,Yifei Cao,CHN,male,1988-05-20,1.75,75.0,shooting,0,0,0 -390260480,Yigal Kopinsky,SUR,male,1985-10-16,,,judo,0,0,0 -192425723,Yigrem Demelash,ETH,male,1994-01-26,1.67,48.0,athletics,0,0,0 -221613357,Yihan Wang,CHN,female,1988-01-18,1.78,65.0,badminton,0,0,0 -370970040,Yihan Zhou,SIN,female,1994-01-30,1.68,54.0,table tennis,0,0,0 -567715213,Yijun Feng,USA,male,1997-02-12,1.86,71.0,table tennis,0,0,0 -522417913,Yilin Fan,CHN,female,1999-11-11,1.48,37.0,gymnastics,0,0,1 -780210993,Yilin Zhou,CHN,female,1992-09-18,1.75,63.0,aquatics,0,0,0 -639260564,Ying Chen,CHN,female,1977-11-04,1.64,67.0,shooting,0,0,0 -421292121,Ying Han,GER,female,1983-04-29,1.7,60.0,table tennis,0,1,0 -911763971,Ying Li,CHN,female,1993-01-07,1.7,61.0,football,0,0,0 -51254244,Ying Lu,CHN,female,1989-01-22,1.75,65.0,aquatics,0,0,0 -9996220,Ying Luo,CHN,female,1991-01-11,1.64,62.0,badminton,0,0,0 -329937737,Ying Suet Tse,HKG,female,1991-11-09,1.66,66.0,badminton,0,0,0 -854775269,Yingnan Ma,CHN,female,1984-03-03,1.58,52.0,judo,0,0,0 -196292572,Yirisleydi Ford,CUB,female,1991-08-18,1.68,66.0,athletics,0,0,0 -102152415,Yisela Cuesta,COL,female,1991-09-27,1.66,59.0,football,0,0,0 -712061980,Yislena Hernandez,CUB,female,1990-03-13,1.67,57.0,rowing,0,0,0 -56600686,Yiwen Sun,CHN,female,1992-06-17,1.77,63.0,fencing,0,1,1 -413280688,Yixuan Hu,CHN,male,1994-10-23,1.85,83.0,aquatics,0,0,0 -904689732,Yoandry Iriarte Galvez,CUB,male,1986-05-05,1.77,79.0,fencing,0,0,0 -115955580,Yoandys Lescay,CUB,male,1994-01-05,1.81,77.0,athletics,0,0,0 -857109220,Yoann Kowal,FRA,male,1987-05-28,1.72,58.0,athletics,0,0,0 -540772965,Yodgoroy Mirzaeva,UZB,female,1996-04-22,1.6,,boxing,0,0,0 -607223536,Yoel Segundo Finol,VEN,male,1996-09-21,1.68,,boxing,0,0,1 -652136434,Yoelmis Hernandez Paumier,CUB,male,1986-04-25,1.68,84.0,weightlifting,0,0,0 -613058255,Yogeshwar Dutt,IND,male,1982-11-02,1.68,65.0,wrestling,0,0,0 -269473268,Yohan Blake,JAM,male,1989-12-26,1.8,80.0,athletics,1,0,0 -512633616,Yohann Diniz,FRA,male,1978-01-01,1.85,66.0,athletics,0,0,0 -266326638,Yoichi Itokazu,JPN,male,1991-05-24,1.6,62.0,weightlifting,0,0,0 -946096698,Yolande Bukasa,ROT,female,1987-09-08,1.7,70.0,judo,0,0,0 -336097063,Yolande Juliana Amana Guigolo,CMR,female,1997-09-15,1.84,78.0,volleyball,0,0,0 -101826251,Yolymar Pineda,VEN,female,1985-11-14,1.65,53.0,athletics,0,0,0 -237054888,Yon Soriano,DOM,male,1987-01-02,1.66,62.0,athletics,0,0,0 -437784364,Yona Knight-Wisdom,JAM,male,1995-05-12,1.88,87.0,aquatics,0,0,0 -822236313,Yonas Kinde,ROT,male,1980-05-07,1.72,57.0,athletics,0,0,0 -291959436,Yonathan Monsalve,VEN,male,1989-06-28,1.76,62.0,cycling,0,0,0 -281612078,Yonder Roman Garcia Alvarez,CUB,male,1993-02-26,1.83,78.0,volleyball,0,0,0 -358811033,Yong Dae Lee,KOR,male,1988-09-11,1.8,74.0,badminton,0,0,0 -608871682,Yong Gwang Kwon,PRK,male,1996-01-14,1.65,68.0,weightlifting,0,0,0 -6528240,Yong Hui Pak,PRK,female,1970-08-24,1.6,63.0,shooting,0,0,0 -798606388,Yong Suk Jo,PRK,female,1988-09-05,1.63,65.0,shooting,0,0,0 -144141011,Yongli Wei,CHN,female,1991-10-11,1.68,58.0,athletics,0,0,0 -194045564,Yongqing Lin,CHN,male,1992-12-24,1.9,85.0,aquatics,0,0,0 -368147232,Yongran Oh,KOR,female,1972-09-06,1.71,73.0,handball,0,0,0 -338890574,Yongshi Liu,CHN,female,1990-02-19,1.76,62.0,fencing,0,0,0 -970544941,Yongwoo Park,KOR,male,1993-09-10,1.86,80.0,football,0,0,0 -430969568,Yoo Na Bae,KOR,female,1989-11-30,1.82,66.0,volleyball,0,0,0 -366615764,Yoojin Hong,KOR,female,1989-02-21,1.66,56.0,hockey,0,0,0 -400985709,Yoosun Nam,KOR,female,1985-07-23,1.69,52.0,aquatics,0,0,0 -824249467,Yordan L. O'Farrill,CUB,male,1993-02-09,1.83,72.0,athletics,0,0,0 -860020616,Yordani Garcia,CUB,male,1988-11-21,1.95,84.0,athletics,0,0,0 -365796318,Yordanys Duranona,DMA,male,1988-06-16,1.85,83.0,athletics,0,0,0 -889022842,Yorgelis Rodriguez,CUB,female,1995-01-25,1.7,66.0,athletics,0,0,0 -693270892,Yosbany Veitia,CUB,male,1992-03-12,1.6,,boxing,0,0,0 -564568653,Yoshiaki Oiwa,JPN,male,1976-07-19,1.7,67.0,equestrian,0,0,0 -549187126,Yoshihide Kiryu,JPN,male,1995-12-15,1.75,69.0,athletics,0,1,0 -133419662,Yoshitaka Tokunaga,JPN,male,1992-04-10,1.85,100.0,rugby sevens,0,0,0 -435666182,Yoshitaku Nagasako,JPN,male,1993-09-16,1.72,70.0,cycling,0,0,0 -180574339,Yoshua Shing,VAN,male,1993-06-20,1.73,65.0,table tennis,0,0,0 -201354275,Yosiry Urrutia,COL,female,1986-06-26,1.76,65.0,athletics,0,0,0 -844481129,Yosra Dhieb,TUN,female,1995-08-31,1.78,120.0,weightlifting,0,0,0 -391193514,Yosuke Ideguchi,JPN,male,1996-08-23,1.71,69.0,football,0,0,0 -326797163,Yosuke Nakayama,JPN,male,1987-03-20,1.61,62.0,weightlifting,0,0,0 -922648062,Yosvani Gonzalez Nicolas,CUB,male,1988-04-18,1.96,85.0,volleyball,0,0,0 -942438156,Youba Sissokho Ndiaye,ESP,male,1991-11-07,1.82,,boxing,0,0,0 -897401460,Youcef Reguigui,ALG,male,1990-01-09,1.74,68.0,cycling,0,0,0 -62442615,Youn Joo Hwang,KOR,female,1986-08-13,1.77,63.0,volleyball,0,0,0 -462706353,Youndry Andujar,DOM,male,1990-07-05,1.71,60.0,athletics,0,0,0 -450541080,Younes Essalhi,MAR,male,1993-02-20,1.8,64.0,athletics,0,0,0 -253601980,Young Mi Kang,KOR,female,1985-03-01,1.64,63.0,fencing,0,0,0 -654665067,Younghee Son,KOR,female,1993-04-24,1.73,110.0,weightlifting,0,0,0 -502665912,Youngjun Byun,KOR,male,1984-03-20,1.75,59.0,athletics,0,0,0 -800057489,Youngjun Won,KOR,male,1998-01-08,1.87,83.0,aquatics,0,0,0 -447706545,Youngsik Jeoung,KOR,male,1992-01-20,1.82,65.0,table tennis,0,0,0 -460973844,Youngsil Lee,KOR,female,1987-03-13,1.67,59.0,hockey,0,0,0 -421409834,Yousef Mirza Banihammad,UAE,male,1988-10-08,1.76,60.0,cycling,0,0,0 -290661628,Yousef Shriha,LBA,male,1991-11-18,1.77,58.0,taekwondo,0,0,0 -996923699,Yousra Helmy,EGY,female,1995-12-03,1.65,50.0,table tennis,0,0,0 -581419074,Youssef Akrout,TUN,male,1990-11-15,1.8,78.0,sailing,0,0,0 -577987694,Youssef Selim,EGY,male,1997-12-14,1.65,88.0,aquatics,0,0,0 -906406227,Youssra Zakarani,MAR,female,1995-01-07,1.62,58.0,fencing,0,0,0 -804864776,Youxue Mo,CHN,male,1996-02-10,1.8,65.0,athletics,0,0,0 -191719659,Yowlys Bonne Rodriguez,CUB,male,1983-11-02,1.52,57.0,wrestling,0,0,0 -403722515,Ysaora Thibus,FRA,female,1991-08-22,1.74,59.0,fencing,0,0,0 -866421007,Yu Asai,JPN,female,1996-01-08,1.72,63.0,hockey,0,0,0 -470277926,Yu Luo,CHN,female,1991-01-11,1.64,66.0,badminton,0,0,0 -245945587,Yu Wang,CHN,male,1991-08-18,1.9,65.0,athletics,0,0,0 -262263057,Yu Xing,CHN,male,1991-03-12,1.88,90.0,archery,0,0,0 -112835195,Yu Zhou,CHN,female,1989-01-23,1.74,67.0,canoe,0,0,0 -964683123,Yu-Hsuan Chen,TPE,female,1993-01-16,1.57,50.0,athletics,0,0,0 -886518793,Yuan Cao,CHN,male,1995-02-07,1.67,62.0,aquatics,1,0,1 -960243282,Yuan Yue,CHN,female,1987-07-23,1.82,72.0,volleyball,0,0,0 -50733042,Yuanhui Fu,CHN,female,1996-01-07,1.79,67.0,aquatics,0,0,1 -247982546,Yuanting Tang,CHN,female,1994-08-02,1.75,70.0,badminton,0,0,0 -27660873,Yuchen Zou,CHN,male,1996-07-05,2.03,107.0,basketball,0,0,0 -622790122,Yucheng Han,CHN,male,1978-12-16,1.78,55.0,athletics,0,0,0 -68915192,Yuderqui Maridalia Contreras,DOM,female,1986-03-27,1.58,58.0,weightlifting,0,0,0 -427057376,Yudiao Zhao,CHN,female,1989-05-25,1.74,68.0,hockey,0,0,0 -426336563,Yue Cao,CHN,female,1995-10-29,1.78,72.0,aquatics,0,0,0 -282448416,Yue Li,CHN,female,1993-10-27,1.7,65.0,canoe,0,0,0 -500859635,Yue Lin,CHN,male,1991-07-24,1.64,58.0,aquatics,1,0,0 -920857282,Yue Wu,USA,female,1990-01-04,1.61,50.0,table tennis,0,0,0 -23443327,Yue Zhang,CHN,female,1990-09-30,1.85,64.0,football,0,0,0 -517058377,Yuehong Li,CHN,male,1989-08-28,1.73,71.0,shooting,0,0,1 -607487573,Yuen Yin Lee,HKG,female,1989-07-16,1.61,58.0,rowing,0,0,0 -968356783,Yufei Zhang,CHN,female,1998-04-19,1.76,62.0,aquatics,0,0,0 -928265049,Yuhan Tan,BEL,male,1987-04-21,1.82,74.0,badminton,0,0,0 -97065462,Yuhan Zhang,CHN,female,1995-01-06,1.73,60.0,aquatics,0,0,0 -122679223,Yuhang Wu,CHN,male,1997-06-03,1.78,72.0,aquatics,0,0,0 -480817405,Yuhong Qi,CHN,female,1989-08-25,1.66,78.0,archery,0,0,0 -845709024,Yuichi Sugita,JPN,male,1988-09-18,1.73,66.0,tennis,0,0,0 -834189492,Yujie Sun,CHN,female,1992-08-10,1.85,78.0,fencing,0,1,0 -714978858,Yuka Kanematsu,JPN,female,1982-06-17,1.59,59.0,rugby sevens,0,0,0 -85090644,Yuka Mamiya,JPN,female,1990-04-03,1.85,74.0,basketball,0,0,0 -192455905,Yuka Sato,JPN,female,1992-01-18,1.71,56.0,triathlon,0,0,0 -206045487,Yuka Takashima,JPN,female,1988-05-12,1.53,39.0,athletics,0,0,0 -47621676,Yukari Mano,JPN,female,1994-03-04,1.52,53.0,hockey,0,0,0 -12505155,Yuki Ebihara,JPN,female,1985-10-28,1.64,68.0,athletics,0,0,0 -384346925,Yuki Hayashi,JPN,female,1984-10-02,1.63,65.0,archery,0,0,0 -596523422,Yuki Ishii,JPN,female,1991-05-08,1.8,68.0,volleyball,0,0,0 -917190651,Yuki Kadono,JPN,male,1990-09-14,1.77,72.0,aquatics,0,0,0 -324618185,Yuki Kobori,JPN,male,1993-11-25,1.83,77.0,aquatics,0,0,1 -192515244,Yuki Matsushita,JPN,male,1991-09-09,1.76,65.0,athletics,0,0,0 -223316076,Yuki Miyazawa,JPN,female,1993-06-02,1.82,70.0,basketball,0,0,0 -413134994,Yuki Ota,JPN,male,1985-11-25,1.71,69.0,fencing,0,0,0 -108168547,Yuki Uchiyama,JPN,female,1998-01-13,1.59,49.0,gymnastics,0,0,0 -581218184,Yukie Nakayama,JPN,female,1979-03-07,1.61,54.0,shooting,0,0,0 -703800372,Yukiko Inui,JPN,female,1990-12-04,1.7,56.0,aquatics,0,0,2 -760257652,Yukio Makino,JPN,male,1980-05-06,1.84,78.0,sailing,0,0,0 -492326920,Yukiya Arashiro,JPN,male,1984-09-22,1.71,64.0,cycling,0,0,0 -751772771,Yuko Kitai,JPN,female,1973-01-15,1.62,50.0,equestrian,0,0,0 -440552252,Yulenmis Aguilar,CUB,female,1996-08-03,1.65,66.0,athletics,0,0,0 -86099624,Yulia Efimova,RUS,female,1992-04-03,,,aquatics,0,2,0 -247132053,Yulia Timoshinina,RUS,female,1998-01-23,1.57,46.0,aquatics,0,0,0 -13825492,Yuliia Khavaldzhy Blahinya,UKR,female,1990-02-21,1.62,53.0,wrestling,0,0,0 -541028495,Yuliia Levchenko,UKR,female,1997-11-28,1.79,59.0,athletics,0,0,0 -957767135,Yuliia Tkach Ostapchuk,UKR,female,1989-09-26,1.7,63.0,wrestling,0,0,0 -109328208,Yulimar Rojas,VEN,female,1995-10-21,1.92,72.0,athletics,0,1,0 -750465466,Yuliya Bichyk,BLR,female,1983-04-01,1.84,84.0,rowing,0,0,0 -313792842,Yuliya Gavrilova,RUS,female,1989-07-20,1.69,56.0,fencing,1,0,0 -379286270,Yuliya Karol,BLR,female,1991-06-26,1.62,57.0,athletics,0,0,0 -416483040,Yuliya Khitraya,BLR,female,1989-09-11,1.8,66.0,aquatics,0,0,0 -131403835,Yuliya Leantsiuk,BLR,female,1984-01-31,1.8,80.0,athletics,0,0,0 -323145387,Yuliya Lobzhenidze,GEO,female,1977-08-23,1.77,80.0,archery,0,0,0 -983041007,Yuliya Rakhmanova,KAZ,female,1991-10-25,1.68,58.0,athletics,0,0,0 -745030632,Yuliya Ratkevich,AZE,female,1985-07-16,1.62,58.0,wrestling,0,0,0 -521174364,Yuliya Rytsikava,BLR,female,1986-09-08,1.8,69.0,basketball,0,0,0 -693790163,Yuliya Tarasova,UZB,female,1986-03-13,1.74,66.0,athletics,0,0,0 -272506840,Yuliya Yelistratova,UKR,female,1988-02-15,1.63,53.0,triathlon,0,0,0 -346397008,Yume Okuroda,JPN,female,1994-07-06,1.57,62.0,rugby sevens,0,0,0 -304244735,Yumi Kida,JPN,female,1985-06-30,1.6,55.0,aquatics,0,0,0 -109766656,Yun Chol Om,PRK,male,1991-11-18,1.51,56.0,weightlifting,0,1,0 -660495063,Yun Hu,HKG,male,1981-08-31,1.78,73.0,badminton,0,0,0 -402313954,Yung-Jan Chan,TPE,female,1989-08-17,1.71,65.0,tennis,0,0,0 -491896324,Yunlei Zhao,CHN,female,1986-08-25,1.73,60.0,badminton,0,0,1 -787363560,Yunli Xu,CHN,female,1987-08-02,1.95,75.0,volleyball,1,0,0 -243639998,Yunlong Jiao,CHN,male,1988-05-19,1.9,87.0,fencing,0,0,0 -274497328,Yuqing Bao,CHN,female,1993-09-23,1.73,55.0,gymnastics,0,0,0 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a/data/.ipynb_checkpoints/land_global_temperature_anomaly-1880-2016-checkpoint.csv b/data/.ipynb_checkpoints/land_global_temperature_anomaly-1880-2016-checkpoint.csv new file mode 100644 index 0000000..749296e --- /dev/null +++ b/data/.ipynb_checkpoints/land_global_temperature_anomaly-1880-2016-checkpoint.csv @@ -0,0 +1,142 @@ +Global Land Temperature Anomalies, January-December +Units: Degrees Celsius +Base Period: 1901-2000 +Missing: -999.0000 +Year,Value +1880,-0.47 +1881,-0.45 +1882,-0.41 +1883,-0.60 +1884,-0.60 +1885,-0.52 +1886,-0.48 +1887,-0.47 +1888,-0.46 +1889,-0.31 +1890,-0.49 +1891,-0.55 +1892,-0.52 +1893,-0.56 +1894,-0.35 +1895,-0.40 +1896,-0.27 +1897,-0.29 +1898,-0.37 +1899,-0.18 +1900,-0.13 +1901,-0.09 +1902,-0.33 +1903,-0.39 +1904,-0.46 +1905,-0.42 +1906,-0.20 +1907,-0.60 +1908,-0.49 +1909,-0.39 +1910,-0.33 +1911,-0.44 +1912,-0.51 +1913,-0.34 +1914,-0.07 +1915,-0.07 +1916,-0.36 +1917,-0.51 +1918,-0.37 +1919,-0.32 +1920,-0.28 +1921,-0.06 +1922,-0.24 +1923,-0.21 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+2011,0.91 +2012,0.92 +2013,1.01 +2014,1.02 +2015,1.35 +2016,1.45 diff --git a/data/.ipynb_checkpoints/primary-energy-consumption-by-region-checkpoint.csv b/data/.ipynb_checkpoints/primary-energy-consumption-by-region-checkpoint.csv new file mode 100644 index 0000000..facd19c --- /dev/null +++ b/data/.ipynb_checkpoints/primary-energy-consumption-by-region-checkpoint.csv @@ -0,0 +1,5066 @@ +Entity,Code,Year,Primary Energy Consumption (terawatt-hours) +Africa,,1965,710.9496218370446 +Africa,,1966,742.3557894211085 +Africa,,1967,750.4345544205105 +Africa,,1968,791.0998844157018 +Africa,,1969,807.9623052554762 +Africa,,1970,875.8575993480164 +Africa,,1971,951.3538360556091 +Africa,,1972,1011.5591171299385 +Africa,,1973,1087.611638468385 +Africa,,1974,1142.4470925130538 +Africa,,1975,1209.361200720239 +Africa,,1976,1316.082352326862 +Africa,,1977,1384.1726687059763 +Africa,,1978,1437.690751907023 +Africa,,1979,1567.8167111229407 +Africa,,1980,1688.4608236158724 +Africa,,1981,1882.3847187089277 +Africa,,1982,2010.4197830152507 +Africa,,1983,2065.874518832706 +Africa,,1984,2136.94122794506 +Africa,,1985,2217.8016016972447 +Africa,,1986,2269.3208429987676 +Africa,,1987,2340.164150562731 +Africa,,1988,2508.9537976665138 +Africa,,1989,2516.310250001407 +Africa,,1990,2597.4525020354927 +Africa,,1991,2594.732004911744 +Africa,,1992,2638.661744244721 +Africa,,1993,2649.1260676244697 +Africa,,1994,2727.1853143504954 +Africa,,1995,2837.7403384537865 +Africa,,1996,2914.1053605107545 +Africa,,1997,2969.5663494548953 +Africa,,1998,3015.0363850147273 +Africa,,1999,3106.265531917238 +Africa,,2000,3179.78678924349 +Africa,,2001,3306.5696263568316 +Africa,,2002,3344.0058205715295 +Africa,,2003,3504.9254473059495 +Africa,,2004,3757.6515298153213 +Africa,,2005,3786.0332515163486 +Africa,,2006,3855.000009214428 +Africa,,2007,3994.037595619741 +Africa,,2008,4249.897933458425 +Africa,,2009,4326.0884858189065 +Africa,,2010,4464.043502961286 +Africa,,2011,4481.371714602618 +Africa,,2012,4643.128590988004 +Africa,,2013,4765.0683798958 +Africa,,2014,4914.695261020759 +Africa,,2015,5001.8845992674505 +Africa,,2016,5110.664038390359 +Africa,,2017,5217.289746039103 +Africa,,2018,5367.396523588756 +Algeria,DZA,1965,24.684921944444447 +Algeria,DZA,1966,29.736455277777825 +Algeria,DZA,1967,28.10500111111115 +Algeria,DZA,1968,30.145581388888942 +Algeria,DZA,1969,33.425377499999996 +Algeria,DZA,1970,36.57900583333339 +Algeria,DZA,1971,39.617660277777766 +Algeria,DZA,1972,44.49832916666663 +Algeria,DZA,1973,53.01575250000003 +Algeria,DZA,1974,58.753500833333355 +Algeria,DZA,1975,70.26040750000004 +Algeria,DZA,1976,81.96365250000001 +Algeria,DZA,1977,90.12660361111114 +Algeria,DZA,1978,113.29676472222219 +Algeria,DZA,1979,148.44730722222283 +Algeria,DZA,1980,177.05753999999953 +Algeria,DZA,1981,215.05758277777784 +Algeria,DZA,1982,247.95175777777746 +Algeria,DZA,1983,270.8881611111113 +Algeria,DZA,1984,260.05752638888856 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+Argentina,ARG,2010,897.632885972413 +Argentina,ARG,2011,915.5681227302288 +Argentina,ARG,2012,942.2604514311959 +Argentina,ARG,2013,982.6040098322283 +Argentina,ARG,2014,978.4022981521706 +Argentina,ARG,2015,1001.3302034887311 +Argentina,ARG,2016,999.2181065296804 +Argentina,ARG,2017,1001.3857253097067 +Argentina,ARG,2018,989.1598669446079 +Asia Pacific,,1965,5135.237611123247 +Asia Pacific,,1966,5615.9561930005175 +Asia Pacific,,1967,5885.708835984587 +Asia Pacific,,1968,6327.74005936363 +Asia Pacific,,1969,7208.321129955075 +Asia Pacific,,1970,8197.82993761301 +Asia Pacific,,1971,8971.673288008604 +Asia Pacific,,1972,9512.956347692863 +Asia Pacific,,1973,10293.739388263812 +Asia Pacific,,1974,10492.027434247144 +Asia Pacific,,1975,10885.766862062637 +Asia Pacific,,1976,11435.314403207325 +Asia Pacific,,1977,12063.522072120442 +Asia Pacific,,1978,12865.552992497078 +Asia Pacific,,1979,13462.980033905915 +Asia Pacific,,1980,13584.492010882585 +Asia Pacific,,1981,13665.634540948055 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Pacific,,2007,48849.62466112426 +Asia Pacific,,2008,50197.43811765898 +Asia Pacific,,2009,51388.901659926516 +Asia Pacific,,2010,54678.99138665818 +Asia Pacific,,2011,57620.864304346745 +Asia Pacific,,2012,59564.68647930671 +Asia Pacific,,2013,61341.002858802836 +Asia Pacific,,2014,62681.384756379775 +Asia Pacific,,2015,63682.05659089313 +Asia Pacific,,2016,64976.325083947915 +Asia Pacific,,2017,66849.68780629005 +Asia Pacific,,2018,69615.32381130465 +Australia,AUS,1965,404.18988403508786 +Australia,AUS,1966,443.55216925438623 +Australia,AUS,1967,470.45545456140405 +Australia,AUS,1968,498.94848248538057 +Australia,AUS,1969,519.4448253508773 +Australia,AUS,1970,566.6456486258483 +Australia,AUS,1971,594.9155380013505 +Australia,AUS,1972,622.1784750277434 +Australia,AUS,1973,665.1684946804619 +Australia,AUS,1974,707.1458616619885 +Australia,AUS,1975,718.7814240665318 +Australia,AUS,1976,744.211050388576 +Australia,AUS,1977,781.3206722945035 +Australia,AUS,1978,800.5856379382421 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Africa,,1985,186.3439960416344 +Eastern Africa,,1986,196.24173123120124 +Eastern Africa,,1987,197.26798745779942 +Eastern Africa,,1988,213.42856514907072 +Eastern Africa,,1989,216.32532460324714 +Eastern Africa,,1990,243.33811075689454 +Eastern Africa,,1991,231.5208790743406 +Eastern Africa,,1992,227.49110100961752 +Eastern Africa,,1993,242.43442575155723 +Eastern Africa,,1994,243.97599999846042 +Eastern Africa,,1995,248.50260818743595 +Eastern Africa,,1996,249.86350976010468 +Eastern Africa,,1997,259.65858920140994 +Eastern Africa,,1998,275.74562711897164 +Eastern Africa,,1999,281.4265931626468 +Eastern Africa,,2000,284.9423792713391 +Eastern Africa,,2001,306.9656397344554 +Eastern Africa,,2002,314.9317290935945 +Eastern Africa,,2003,311.5809016135721 +Eastern Africa,,2004,337.5903059249893 +Eastern Africa,,2005,349.87579191426136 +Eastern Africa,,2006,362.8434064369178 +Eastern Africa,,2007,380.0265484493778 +Eastern Africa,,2008,377.7146055895042 +Eastern Africa,,2009,398.6336669761924 +Eastern Africa,,2010,433.4146225702026 +Eastern Africa,,2011,456.8645342058124 +Eastern Africa,,2012,465.0880786241501 +Eastern Africa,,2013,506.03311207735874 +Eastern Africa,,2014,559.45297569285 +Eastern Africa,,2015,584.880380844766 +Eastern Africa,,2016,576.1773141796415 +Eastern Africa,,2017,605.0603323873368 +Eastern Africa,,2018,629.1256565759878 +Ecuador,ECU,1965,8.827777499999996 +Ecuador,ECU,1966,9.304819444444444 +Ecuador,ECU,1967,10.082515277777784 +Ecuador,ECU,1968,11.870032222222262 +Ecuador,ECU,1969,12.630500833333329 +Ecuador,ECU,1970,15.04082472222219 +Ecuador,ECU,1971,16.53522027777772 +Ecuador,ECU,1972,18.469451388888867 +Ecuador,ECU,1973,18.389198611111144 +Ecuador,ECU,1974,21.232815555555582 +Ecuador,ECU,1975,21.19462277777781 +Ecuador,ECU,1976,23.498849166666638 +Ecuador,ECU,1977,28.94387555555558 +Ecuador,ECU,1978,29.89433222222225 +Ecuador,ECU,1979,31.071763611111155 +Ecuador,ECU,1980,39.26167666666666 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Africa,,1989,91.85944917844951 +Middle Africa,,1990,85.0418490752833 +Middle Africa,,1991,79.88120662492939 +Middle Africa,,1992,81.80592570943818 +Middle Africa,,1993,81.89321653015354 +Middle Africa,,1994,79.58075236012911 +Middle Africa,,1995,81.69136750454848 +Middle Africa,,1996,82.18224221856343 +Middle Africa,,1997,82.02290730711694 +Middle Africa,,1998,79.00580957848379 +Middle Africa,,1999,82.50644566336852 +Middle Africa,,2000,90.05890712488984 +Middle Africa,,2001,101.52126294652004 +Middle Africa,,2002,107.59237027090636 +Middle Africa,,2003,117.07960821899908 +Middle Africa,,2004,131.4342790427874 +Middle Africa,,2005,131.92797973833703 +Middle Africa,,2006,145.3938256187248 +Middle Africa,,2007,149.1697090400252 +Middle Africa,,2008,169.01424774833723 +Middle Africa,,2009,179.6430088240256 +Middle Africa,,2010,193.55408764521977 +Middle Africa,,2011,210.39465044919658 +Middle Africa,,2012,214.59404169763084 +Middle Africa,,2013,228.9487855432747 +Middle Africa,,2014,240.82475422147698 +Middle Africa,,2015,234.47001329047058 +Middle Africa,,2016,244.84730364292847 +Middle Africa,,2017,254.17568254966983 +Middle Africa,,2018,266.2348522785487 +Middle East,,1965,561.4094753361031 +Middle East,,1966,585.3364189259314 +Middle East,,1967,612.4623033901796 +Middle East,,1968,646.450219491899 +Middle East,,1969,680.0805547768631 +Middle East,,1970,714.978789822836 +Middle East,,1971,757.0964813800279 +Middle East,,1972,823.5227715107023 +Middle East,,1973,902.4664005286799 +Middle East,,1974,961.0591368334901 +Middle East,,1975,959.0271123362097 +Middle East,,1976,1085.1335366143264 +Middle East,,1977,1233.7959964641839 +Middle East,,1978,1293.2584952353407 +Middle East,,1979,1518.9456521825502 +Middle East,,1980,1469.7778320185669 +Middle East,,1981,1601.1805385576204 +Middle East,,1982,1773.6107063121424 +Middle East,,1983,1940.440666330837 +Middle East,,1984,2192.492620350803 +Middle East,,1985,2331.5712581869857 +Middle East,,1986,2435.5800171494147 +Middle East,,1987,2605.496637827771 +Middle East,,1988,2767.9652147348124 +Middle East,,1989,2917.693649152668 +Middle East,,1990,3014.4537823019145 +Middle East,,1991,3148.9934558451077 +Middle East,,1992,3433.7478927027887 +Middle East,,1993,3536.005252344063 +Middle East,,1994,3947.067109223678 +Middle East,,1995,4084.6679654680456 +Middle East,,1996,4215.5973808406725 +Middle East,,1997,4417.01265299148 +Middle East,,1998,4489.485602990267 +Middle East,,1999,4570.561476078293 +Middle East,,2000,4801.8999492805215 +Middle East,,2001,5035.957261668717 +Middle East,,2002,5312.897354669109 +Middle East,,2003,5546.521126299047 +Middle East,,2004,5976.944614139297 +Middle East,,2005,6387.858689541813 +Middle East,,2006,6713.692583636376 +Middle East,,2007,7091.871787080568 +Middle East,,2008,7602.660559885327 +Middle East,,2009,7836.487404295822 +Middle East,,2010,8255.216254636349 +Middle East,,2011,8587.724653378496 +Middle East,,2012,8924.21372776715 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Zealand,NZL,1978,124.99185386159483 +New Zealand,NZL,1979,125.92940736958407 +New Zealand,NZL,1980,126.37513974628786 +New Zealand,NZL,1981,126.71436651753282 +New Zealand,NZL,1982,131.37930761018936 +New Zealand,NZL,1983,136.32389808318348 +New Zealand,NZL,1984,143.94292030458655 +New Zealand,NZL,1985,145.64772133170163 +New Zealand,NZL,1986,152.33767077175295 +New Zealand,NZL,1987,156.9769735375028 +New Zealand,NZL,1988,160.99128588016558 +New Zealand,NZL,1989,164.80481876242487 +New Zealand,NZL,1990,171.21474143104368 +New Zealand,NZL,1991,176.83992854295172 +New Zealand,NZL,1992,178.06717217652232 +New Zealand,NZL,1993,183.4141998276277 +New Zealand,NZL,1994,194.84495237163193 +New Zealand,NZL,1995,201.1000650841631 +New Zealand,NZL,1996,206.67980073814968 +New Zealand,NZL,1997,208.6874181340691 +New Zealand,NZL,1998,208.46733006315753 +New Zealand,NZL,1999,210.66135167770486 +New Zealand,NZL,2000,219.85435785092557 +New Zealand,NZL,2001,219.02409964683858 +New Zealand,NZL,2002,226.98107801958827 +New Zealand,NZL,2003,220.41750365145145 +New Zealand,NZL,2004,228.0788157104949 +New Zealand,NZL,2005,221.2624763145691 +New Zealand,NZL,2006,223.87016286123526 +New Zealand,NZL,2007,223.6653218874445 +New Zealand,NZL,2008,224.7211211171855 +New Zealand,NZL,2009,223.80972223171128 +New Zealand,NZL,2010,231.21717282797204 +New Zealand,NZL,2011,229.66897799727707 +New Zealand,NZL,2012,232.34783238220453 +New Zealand,NZL,2013,235.07851912340848 +New Zealand,NZL,2014,246.54853562733615 +New Zealand,NZL,2015,248.3235525069312 +New Zealand,NZL,2016,250.76116376577716 +New Zealand,NZL,2017,257.80862996936906 +New Zealand,NZL,2018,252.82263129953392 +North America,,1965,16171.820137905172 +North America,,1966,17089.84765381383 +North America,,1967,17697.166772199103 +North America,,1968,18793.134462156027 +North America,,1969,19813.92978079766 +North America,,1970,20559.041491230822 +North America,,1971,21005.634290985683 +North America,,1972,22137.74157152797 +North America,,1973,23103.594598187672 +North America,,1974,22701.772223877928 +North America,,1975,22218.213705152404 +North America,,1976,23443.196883157714 +North America,,1977,24128.849572941657 +North America,,1978,24338.487390029084 +North America,,1979,24699.941461076938 +North America,,1980,24054.38704846231 +North America,,1981,23468.93998842471 +North America,,1982,22696.473668779883 +North America,,1983,22574.869186512005 +North America,,1984,23797.656316393164 +North America,,1985,23877.679354744065 +North America,,1986,23966.241563561936 +North America,,1987,24781.550577697064 +North America,,1988,25859.028995304376 +North America,,1989,26468.39718578985 +North America,,1990,26516.228994800884 +North America,,1991,26531.2317765977 +North America,,1992,26966.84520908619 +North America,,1993,27510.936253056283 +North America,,1994,28133.04487597688 +North America,,1995,28675.043928329622 +North America,,1996,29614.752411788944 +North America,,1997,29864.136533743233 +North America,,1998,30051.03736476611 +North America,,1999,30615.017665030256 +North America,,2000,31375.938859108104 +North America,,2001,30684.4794707478 +North America,,2002,31234.67918099587 +North America,,2003,31377.478792113838 +North America,,2004,32052.04391386819 +North America,,2005,32240.452576358286 +North America,,2006,32065.669940409127 +North America,,2007,32604.651650203537 +North America,,2008,31994.260700870298 +North America,,2009,30506.749864037854 +North America,,2010,31515.213161112883 +North America,,2011,31568.552927292316 +North America,,2012,30905.102744064276 +North America,,2013,31750.8202889215 +North America,,2014,32085.85645773006 +North America,,2015,31822.278028924884 +North America,,2016,31834.14723837123 +North America,,2017,32046.209621687736 +North America,,2018,32935.90731321991 +Norway,NOR,1965,199.10104350777783 +Norway,NOR,1966,204.3975330777774 +Norway,NOR,1967,219.3991582411114 +Norway,NOR,1968,245.63467979666697 +Norway,NOR,1969,246.95988048222208 +Norway,NOR,1970,259.212358175555 +Norway,NOR,1971,273.6116725688885 +Norway,NOR,1972,290.013375638889 +Norway,NOR,1973,306.9035789088893 +Norway,NOR,1974,306.7773764866671 +Norway,NOR,1975,311.5040987422223 +Norway,NOR,1976,333.46135581222177 +Norway,NOR,1977,308.4265118422227 +Norway,NOR,1978,346.03209941333375 +Norway,NOR,1979,370.1233946800003 +Norway,NOR,1980,352.6639100022217 +Norway,NOR,1981,371.14839029555606 +Norway,NOR,1982,366.4330825622218 +Norway,NOR,1983,401.36573559555586 +Norway,NOR,1984,406.9502880400006 +Norway,NOR,1985,402.1257656809345 +Norway,NOR,1986,393.5235154384198 +Norway,NOR,1987,412.7327468772501 +Norway,NOR,1988,424.6586813241512 +Norway,NOR,1989,451.64293560011623 +Norway,NOR,1990,455.8564492532515 +Norway,NOR,1991,420.83581370274703 +Norway,NOR,1992,442.9710760932576 +Norway,NOR,1993,457.4949924435046 +Norway,NOR,1994,442.9942428416684 +Norway,NOR,1995,469.0815064154271 +Norway,NOR,1996,432.74288868653474 +Norway,NOR,1997,454.7943567504043 +Norway,NOR,1998,472.4635223518637 +Norway,NOR,1999,484.1186567332834 +Norway,NOR,2000,534.2051965135344 +Norway,NOR,2001,487.961097330873 +Norway,NOR,2002,508.8442802409884 +Norway,NOR,2003,453.4604904205348 +Norway,NOR,2004,463.4798195627405 +Norway,NOR,2005,529.0139161638015 +Norway,NOR,2006,487.41674381874907 +Norway,NOR,2007,524.7253246566137 +Norway,NOR,2008,539.4345480742497 +Norway,NOR,2009,500.709409408148 +Norway,NOR,2010,483.2746864413113 +Norway,NOR,2011,492.9949293540692 +Norway,NOR,2012,549.8535535804514 +Norway,NOR,2013,515.348866714301 +Norway,NOR,2014,531.6483842264718 +Norway,NOR,2015,541.1740171729516 +Norway,NOR,2016,549.2282559471997 +Norway,NOR,2017,553.9895192407646 +Norway,NOR,2018,551.0421487509285 +Oman,OMN,1965,5.291939166666668 +Oman,OMN,1966,5.5202780555555515 +Oman,OMN,1967,5.759781666666671 +Oman,OMN,1968,6.022882500000005 +Oman,OMN,1969,6.273933333333329 +Oman,OMN,1970,6.561514722222218 +Oman,OMN,1971,15.471502777777827 +Oman,OMN,1972,17.677216666666723 +Oman,OMN,1973,15.375913888888883 +Oman,OMN,1974,14.127883333333378 +Oman,OMN,1975,12.904697222222245 +Oman,OMN,1976,14.856397222222265 +Oman,OMN,1977,14.312168888888948 +Oman,OMN,1978,16.64237638888893 +Oman,OMN,1979,19.17322694444448 +Oman,OMN,1980,17.058377777777796 +Oman,OMN,1981,18.02799749999998 +Oman,OMN,1982,23.881949444444444 +Oman,OMN,1983,22.350086388888922 +Oman,OMN,1984,32.493594722222234 +Oman,OMN,1985,33.571021111111065 +Oman,OMN,1986,31.459568611111095 +Oman,OMN,1987,35.43523500000001 +Oman,OMN,1988,34.38907666666665 +Oman,OMN,1989,39.01986250000003 +Oman,OMN,1990,48.86399166666672 +Oman,OMN,1991,64.8682466666667 +Oman,OMN,1992,62.49089861111113 +Oman,OMN,1993,59.8380080555556 +Oman,OMN,1994,56.84055124294433 +Oman,OMN,1995,65.89965408803272 +Oman,OMN,1996,67.78353564329402 +Oman,OMN,1997,69.71264101147702 +Oman,OMN,1998,71.64712888664364 +Oman,OMN,1999,82.48155384588505 +Oman,OMN,2000,114.5245906918614 +Oman,OMN,2001,126.48572001099987 +Oman,OMN,2002,137.64600530157867 +Oman,OMN,2003,142.57997596349733 +Oman,OMN,2004,132.4522922100182 +Oman,OMN,2005,159.32631457482643 +Oman,OMN,2006,174.04108614090063 +Oman,OMN,2007,173.57794460405268 +Oman,OMN,2008,203.93386142209616 +Oman,OMN,2009,204.33816844106354 +Oman,OMN,2010,240.05752498042315 +Oman,OMN,2011,262.2162777782752 +Oman,OMN,2012,285.0694841427613 +Oman,OMN,2013,318.43206949563324 +Oman,OMN,2014,317.11862984619177 +Oman,OMN,2015,334.98185126917673 +Oman,OMN,2016,336.89659542485634 +Oman,OMN,2017,341.14854042153496 +Oman,OMN,2018,356.9154646492128 +Other Asia & Pacific,,1965,189.65560049091442 +Other Asia & Pacific,,1966,201.37595585646952 +Other Asia & Pacific,,1967,219.0271624127035 +Other Asia & Pacific,,1968,235.77727495706478 +Other Asia & Pacific,,1969,250.31298698420736 +Other Asia & Pacific,,1970,266.9858778409897 +Other Asia & Pacific,,1971,292.71679539165433 +Other Asia & Pacific,,1972,303.64218754906204 +Other Asia & Pacific,,1973,314.4226221561983 +Other Asia & Pacific,,1974,336.21620585199133 +Other Asia & Pacific,,1975,345.40419068170957 +Other Asia & Pacific,,1976,363.3750439774996 +Other Asia & Pacific,,1977,383.96716173056785 +Other Asia & Pacific,,1978,405.64026608648606 +Other Asia & Pacific,,1979,437.24820958676816 +Other Asia & Pacific,,1980,468.78926721806863 +Other Asia & Pacific,,1981,481.3380428565706 +Other Asia & Pacific,,1982,502.10748443663806 +Other Asia & Pacific,,1983,522.142047995243 +Other Asia & Pacific,,1984,531.1286827253125 +Other Asia & Pacific,,1985,566.7546925045198 +Other Asia & Pacific,,1986,572.7170693312663 +Other Asia & Pacific,,1987,557.1205766939619 +Other Asia & Pacific,,1988,565.2513997494997 +Other Asia & Pacific,,1989,558.852138883567 +Other Asia & Pacific,,1990,546.2839804402416 +Other Asia & Pacific,,1991,537.9321534034708 +Other Asia & Pacific,,1992,487.9751851632183 +Other Asia & Pacific,,1993,467.13742763130074 +Other Asia & Pacific,,1994,448.3002103880559 +Other Asia & Pacific,,1995,443.0440141433426 +Other Asia & Pacific,,1996,409.74834171887795 +Other Asia & Pacific,,1997,406.12339773103946 +Other Asia & Pacific,,1998,390.3157871027124 +Other Asia & Pacific,,1999,411.27522757080976 +Other Asia & Pacific,,2000,438.4082159082215 +Other Asia & Pacific,,2001,455.2166495005974 +Other Asia & Pacific,,2002,448.38792013365486 +Other Asia & Pacific,,2003,472.70454205201366 +Other Asia & Pacific,,2004,479.92215337775076 +Other Asia & Pacific,,2005,504.80631647906773 +Other Asia & Pacific,,2006,523.5724886533841 +Other Asia & Pacific,,2007,512.6124021761541 +Other Asia & Pacific,,2008,536.8284504636149 +Other Asia & Pacific,,2009,490.92214143546335 +Other Asia & Pacific,,2010,527.761178859239 +Other Asia & Pacific,,2011,526.1304463994712 +Other Asia & Pacific,,2012,561.4522195345568 +Other Asia & Pacific,,2013,567.137483816757 +Other Asia & Pacific,,2014,613.1754122385938 +Other Asia & Pacific,,2015,627.3375126310706 +Other Asia & Pacific,,2016,704.1099874184868 +Other Asia & Pacific,,2017,725.3637294597162 +Other Asia & Pacific,,2018,760.40987591313 +Other CIS,,1985,385.2375784561139 +Other CIS,,1986,367.76880343078767 +Other CIS,,1987,378.339014421497 +Other CIS,,1988,417.44445062205057 +Other CIS,,1989,400.49582949962917 +Other CIS,,1990,400.6403081877884 +Other CIS,,1991,335.4694223791902 +Other CIS,,1992,282.99198109671664 +Other CIS,,1993,223.82496733989038 +Other CIS,,1994,186.2302125770409 +Other CIS,,1995,162.72859092426006 +Other CIS,,1996,175.1036657634392 +Other CIS,,1997,165.89044600243798 +Other CIS,,1998,165.13590500028872 +Other CIS,,1999,157.38625498457006 +Other CIS,,2000,151.7752372668114 +Other CIS,,2001,149.5869698270477 +Other CIS,,2002,149.9821358231864 +Other CIS,,2003,158.0506996124266 +Other CIS,,2004,171.16261193450555 +Other CIS,,2005,177.91907744688 +Other CIS,,2006,179.43241183646205 +Other CIS,,2007,194.61445470283346 +Other CIS,,2008,188.35049694863014 +Other CIS,,2009,181.27925038791636 +Other CIS,,2010,184.9036243194829 +Other CIS,,2011,197.59388272702566 +Other CIS,,2012,208.76547570221672 +Other CIS,,2013,198.83692828948503 +Other CIS,,2014,203.9076700994507 +Other CIS,,2015,202.88833369076886 +Other CIS,,2016,204.05905314774196 +Other CIS,,2017,209.02108785631597 +Other CIS,,2018,221.358680276232 +Other Caribbean,,1965,174.78273780820115 +Other Caribbean,,1966,182.8118200283623 +Other Caribbean,,1967,190.37164727381054 +Other Caribbean,,1968,195.81217162738665 +Other Caribbean,,1969,207.8317221419787 +Other Caribbean,,1970,224.76100659738105 +Other Caribbean,,1971,321.9597700757386 +Other Caribbean,,1972,332.07845026976054 +Other Caribbean,,1973,361.6224053664168 +Other Caribbean,,1974,359.90317345260956 +Other Caribbean,,1975,347.6347710679376 +Other Caribbean,,1976,361.24300351258046 +Other Caribbean,,1977,372.56884728772457 +Other Caribbean,,1978,385.1228006650773 +Other Caribbean,,1979,405.5341225763542 +Other Caribbean,,1980,448.0614600971784 +Other Caribbean,,1981,454.05952803548274 +Other Caribbean,,1982,411.2543873759835 +Other Caribbean,,1983,407.69619359391174 +Other Caribbean,,1984,412.97796334190383 +Other Caribbean,,1985,384.59958153480983 +Other Caribbean,,1986,383.50839601131486 +Other Caribbean,,1987,395.67039454609886 +Other Caribbean,,1988,396.49246553635027 +Other Caribbean,,1989,411.6457642351271 +Other Caribbean,,1990,411.24634871956255 +Other Caribbean,,1991,399.81818842116115 +Other Caribbean,,1992,379.0731455220565 +Other Caribbean,,1993,377.512725539307 +Other Caribbean,,1994,395.25252971989323 +Other Caribbean,,1995,397.67640476215826 +Other Caribbean,,1996,412.6834163940832 +Other Caribbean,,1997,448.4834658717484 +Other Caribbean,,1998,465.67340569539425 +Other Caribbean,,1999,446.84874662901456 +Other Caribbean,,2000,495.3950693089652 +Other Caribbean,,2001,504.3058654253304 +Other Caribbean,,2002,512.7008029304377 +Other Caribbean,,2003,524.5774402870933 +Other Caribbean,,2004,530.6073697447844 +Other Caribbean,,2005,532.6671888213943 +Other Caribbean,,2006,545.1137263464536 +Other Caribbean,,2007,530.6495080387995 +Other Caribbean,,2008,506.5529505013749 +Other Caribbean,,2009,479.4761296639808 +Other Caribbean,,2010,478.3555936942854 +Other Caribbean,,2011,485.5109394410783 +Other Caribbean,,2012,471.5027219926814 +Other Caribbean,,2013,458.119628909192 +Other Caribbean,,2014,459.56183612965515 +Other Caribbean,,2015,468.3665797248601 +Other Caribbean,,2016,483.7653612148162 +Other Caribbean,,2017,484.63584166553954 +Other Caribbean,,2018,493.24398091148635 +Other Middle East,,1965,71.85889595505549 +Other Middle East,,1966,75.1344438269019 +Other Middle East,,1967,78.77254032977035 +Other Middle East,,1968,82.54602232520618 +Other Middle East,,1969,86.67432620155724 +Other Middle East,,1970,86.18015072640581 +Other Middle East,,1971,92.33321735394959 +Other Middle East,,1972,92.66646372333746 +Other Middle East,,1973,102.68200307415543 +Other Middle East,,1974,114.33771486725584 +Other Middle East,,1975,125.74206078241266 +Other Middle East,,1976,140.86627841641078 +Other Middle East,,1977,155.61312453658897 +Other Middle East,,1978,165.37813876793155 +Other Middle East,,1979,189.8827640497265 +Other Middle East,,1980,182.40141666962916 +Other Middle East,,1981,191.6301996468435 +Other Middle East,,1982,225.15526489379573 +Other Middle East,,1983,240.47320069564455 +Other Middle East,,1984,259.3347079750229 +Other Middle East,,1985,272.4894249571401 +Other Middle East,,1986,276.7917929066761 +Other Middle East,,1987,291.1998977710073 +Other Middle East,,1988,299.9876565897481 +Other Middle East,,1989,305.9102411439141 +Other Middle East,,1990,322.08505330050014 +Other Middle East,,1991,340.66488610247745 +Other Middle East,,1992,354.47954433110004 +Other Middle East,,1993,366.23480336143643 +Other Middle East,,1994,378.24962401790026 +Other Middle East,,1995,406.1777383705827 +Other Middle East,,1996,411.0477494484644 +Other Middle East,,1997,448.9024678022901 +Other Middle East,,1998,475.9120838192482 +Other Middle East,,1999,479.52570078095516 +Other Middle East,,2000,486.6537440464917 +Other Middle East,,2001,499.9978361789408 +Other Middle East,,2002,518.8729808158157 +Other Middle East,,2003,541.5997492065741 +Other Middle East,,2004,558.0941232192957 +Other Middle East,,2005,609.4227944104629 +Other Middle East,,2006,632.2802522662064 +Other Middle East,,2007,654.9117797164843 +Other Middle East,,2008,677.6749511774474 +Other Middle East,,2009,680.1982890419774 +Other Middle East,,2010,677.1531861097455 +Other Middle East,,2011,636.6319395206988 +Other Middle East,,2012,594.4343210704194 +Other Middle East,,2013,584.5051914063176 +Other Middle East,,2014,582.189656841593 +Other Middle East,,2015,562.453819001195 +Other Middle East,,2016,557.7991071437206 +Other Middle East,,2017,558.3064935393877 +Other Middle East,,2018,556.9315343789482 +Other Northern Africa,,1965,17.52508856223459 +Other Northern Africa,,1966,19.19670840500067 +Other Northern Africa,,1967,20.445144929460685 +Other Northern Africa,,1968,23.315377282860506 +Other Northern Africa,,1969,25.994050932137572 +Other Northern Africa,,1970,30.81143310962559 +Other Northern Africa,,1971,28.474137620539047 +Other Northern Africa,,1972,36.067692626828276 +Other Northern Africa,,1973,49.04354032146429 +Other Northern Africa,,1974,53.89957027579855 +Other Northern Africa,,1975,60.192831583046676 +Other Northern Africa,,1976,70.56340968234282 +Other Northern Africa,,1977,80.07725094713565 +Other Northern Africa,,1978,89.16209554033085 +Other Northern Africa,,1979,116.09863267199886 +Other Northern Africa,,1980,120.73186703719423 +Other Northern Africa,,1981,128.71391033232615 +Other Northern Africa,,1982,131.82913162516482 +Other Northern Africa,,1983,138.79324606960878 +Other Northern Africa,,1984,137.5357245961311 +Other Northern Africa,,1985,154.79124701397754 +Other Northern Africa,,1986,158.52014819220196 +Other Northern Africa,,1987,175.55713819581044 +Other Northern Africa,,1988,178.42607841368832 +Other Northern Africa,,1989,192.2446538771723 +Other Northern Africa,,1990,187.9342373293598 +Other Northern Africa,,1991,186.40498301718998 +Other Northern Africa,,1992,186.77345839819301 +Other Northern Africa,,1993,198.25764146737143 +Other Northern Africa,,1994,215.72434690596816 +Other Northern Africa,,1995,227.4880880729179 +Other Northern Africa,,1996,233.19386140018884 +Other Northern Africa,,1997,243.0175773565438 +Other Northern Africa,,1998,253.03127981268406 +Other Northern Africa,,1999,250.2574812996915 +Other Northern Africa,,2000,269.4186178231078 +Other Northern Africa,,2001,275.3139725668877 +Other Northern Africa,,2002,272.6566324353091 +Other Northern Africa,,2003,281.79324611951904 +Other Northern Africa,,2004,304.20521359701655 +Other Northern Africa,,2005,312.29824484991343 +Other Northern Africa,,2006,313.19025536769095 +Other Northern Africa,,2007,304.23793209403266 +Other Northern Africa,,2008,319.09355704596277 +Other Northern Africa,,2009,339.5943014838559 +Other Northern Africa,,2010,348.1911268649572 +Other Northern Africa,,2011,251.06134452075872 +Other Northern Africa,,2012,299.5037873907529 +Other Northern Africa,,2013,323.6818590629265 +Other Northern Africa,,2014,318.5356825956313 +Other Northern Africa,,2015,279.1054036235332 +Other Northern Africa,,2016,268.7972896745987 +Other Northern Africa,,2017,284.35081398743034 +Other Northern Africa,,2018,295.93588485562 +Other South America,,1965,30.776313201852314 +Other South America,,1966,34.95816974089913 +Other South America,,1967,37.25547127561004 +Other South America,,1968,38.65972315658486 +Other South America,,1969,41.21441454467098 +Other South America,,1970,43.7575904737845 +Other South America,,1971,48.2298730352148 +Other South America,,1972,53.91031326952279 +Other South America,,1973,55.71849468412672 +Other South America,,1974,50.802129624658036 +Other South America,,1975,56.75488900471012 +Other South America,,1976,57.59399602419227 +Other South America,,1977,61.400976070602255 +Other South America,,1978,68.10682673955849 +Other South America,,1979,71.24198810831693 +Other South America,,1980,75.27514539709442 +Other South America,,1981,71.21202581960708 +Other South America,,1982,68.92780569790176 +Other South America,,1983,66.1130348909097 +Other South America,,1984,66.88378118258265 +Other South America,,1985,77.17712871266735 +Other South America,,1986,100.34448290849494 +Other South America,,1987,118.04486817829854 +Other South America,,1988,125.4566261511615 +Other South America,,1989,137.41034287027392 +Other South America,,1990,148.20091253980476 +Other South America,,1991,156.5048514860845 +Other South America,,1992,158.75715737565284 +Other South America,,1993,171.7890008635691 +Other South America,,1994,192.23786448071718 +Other South America,,1995,215.78625796434267 +Other South America,,1996,225.797811972918 +Other South America,,1997,244.81898338999451 +Other South America,,1998,254.53446274922177 +Other South America,,1999,254.82511860540453 +Other South America,,2000,249.71100889011845 +Other South America,,2001,232.28031866297331 +Other South America,,2002,241.12185669765992 +Other South America,,2003,253.5424883998591 +Other South America,,2004,250.55225981802917 +Other South America,,2005,253.95914089148027 +Other South America,,2006,254.86443417845615 +Other South America,,2007,275.23061720539033 +Other South America,,2008,284.0454022722826 +Other South America,,2009,292.7662724393725 +Other South America,,2010,308.4551515926872 +Other South America,,2011,318.6458859386119 +Other South America,,2012,328.3297858247919 +Other South America,,2013,338.50617970648125 +Other South America,,2014,333.86128248044935 +Other South America,,2015,340.2577568763926 +Other South America,,2016,369.0155369608513 +Other South America,,2017,363.9987553967819 +Other South America,,2018,368.7916307830972 +Other Southern Africa,,1965,4.368125637560205 +Other Southern Africa,,1966,4.893960434366463 +Other Southern Africa,,1967,5.181055682466851 +Other Southern Africa,,1968,5.766111653954747 +Other Southern Africa,,1969,5.949465211135069 +Other Southern Africa,,1970,6.3601406412973 +Other Southern Africa,,1971,6.56710496497595 +Other Southern Africa,,1972,7.062212809755694 +Other Southern Africa,,1973,7.25614631686588 +Other Southern Africa,,1974,7.632523261771835 +Other Southern Africa,,1975,7.798623815038955 +Other Southern Africa,,1976,8.457215039164968 +Other Southern Africa,,1977,8.199357640156457 +Other Southern Africa,,1978,8.46699200777131 +Other Southern Africa,,1979,8.563322066480566 +Other Southern Africa,,1980,9.125240772629702 +Other Southern Africa,,1981,10.830063195755676 +Other Southern Africa,,1982,10.99136498259778 +Other Southern Africa,,1983,10.433658623542234 +Other Southern Africa,,1984,10.802150162235105 +Other Southern Africa,,1985,12.28367012161679 +Other Southern Africa,,1986,12.817631352700808 +Other Southern Africa,,1987,13.251287403578898 +Other Southern Africa,,1988,14.32614654071802 +Other Southern Africa,,1989,15.410181750834072 +Other Southern Africa,,1990,20.25459562973814 +Other Southern Africa,,1991,24.736556475887795 +Other Southern Africa,,1992,26.565917418378675 +Other Southern Africa,,1993,26.930455111708653 +Other Southern Africa,,1994,27.638063087158834 +Other Southern Africa,,1995,29.904716430840978 +Other Southern Africa,,1996,28.84678621129746 +Other Southern Africa,,1997,29.603615498708734 +Other Southern Africa,,1998,32.815362198013325 +Other Southern Africa,,1999,35.00839405506917 +Other Southern Africa,,2000,36.23226932871573 +Other Southern Africa,,2001,37.21287600301907 +Other Southern Africa,,2002,37.16648128470967 +Other Southern Africa,,2003,37.179727407697946 +Other Southern Africa,,2004,37.37436406444525 +Other Southern Africa,,2005,40.155721824409014 +Other Southern Africa,,2006,39.59362959495739 +Other Southern Africa,,2007,41.206113985741354 +Other Southern Africa,,2008,42.914170768679 +Other Southern Africa,,2009,44.2906637446784 +Other Southern Africa,,2010,40.54692395182359 +Other Southern Africa,,2011,43.64555300007127 +Other Southern Africa,,2012,48.486861392841924 +Other Southern Africa,,2013,50.4407600183525 +Other Southern Africa,,2014,54.63140495736441 +Other Southern Africa,,2015,55.93716340492369 +Other Southern Africa,,2016,56.69334881954999 +Other Southern Africa,,2017,60.05946960684948 +Other Southern Africa,,2018,58.45864391200311 +Pakistan,PAK,1965,81.50971138888889 +Pakistan,PAK,1966,82.12597250000005 +Pakistan,PAK,1967,90.87251166666667 +Pakistan,PAK,1968,103.11285027777782 +Pakistan,PAK,1969,103.55751333333332 +Pakistan,PAK,1970,102.00093777777776 +Pakistan,PAK,1971,98.91317416666668 +Pakistan,PAK,1972,87.0857538888889 +Pakistan,PAK,1973,92.88082083333335 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z{=F*rR*&%C3p4YqEmi{(G4WdZv~7Lc*lk3x45OcTTziO>*)1Gl98t@((CITw^7-kx?F4-a)T?+QBRwp7asU}sA-^4sb>4~{{a7zl%)Uw literal 0 HcmV?d00001 diff --git a/images/robot_design.svg b/images/robot_design.svg new file mode 100644 index 0000000..88df829 --- /dev/null +++ b/images/robot_design.svg @@ -0,0 +1,1397 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + x + y + z + + d + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + x + z + x + z + + diff --git a/notebooks/.ipynb_checkpoints/01_Cheers_Stats_Beers-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/01_Cheers_Stats_Beers-checkpoint.ipynb index e95fca8..fa8752b 100644 --- a/notebooks/.ipynb_checkpoints/01_Cheers_Stats_Beers-checkpoint.ipynb +++ b/notebooks/.ipynb_checkpoints/01_Cheers_Stats_Beers-checkpoint.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "###### Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2017 L.A. Barba, N.C. Clementi" + "###### Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2019 Ryan C. Cooper" ] }, { @@ -14,8 +14,7 @@ "# Cheers! Stats with Beers\n", "\n", "\n", - "\n", - "Welcome to the second module in _Engineering Computations_, our series in computational thinking for undergraduate engineering students. This module explores practical statistical analysis with Python.\n", + "Welcome to the second module in _Computational Mechanics_, our series in computational thinking for undergraduate engineering students. This module explores practical data and statistical analysis with Python.\n", "\n", "This first lesson explores how we can answer questions using data combined with practical methods from statistics.\n", "\n", @@ -23,7 +22,7 @@ "\n", "The data source ([@craftcans](https://twitter.com/craftcans) on Twitter) doesn't say that the set includes *all* the canned beers brewed in the country. So we have to asume that the data is a sample and may contain biases.\n", "\n", - "We'll manipulate the data using **numpy**—the array library for Python that we learned about in [Module 1, lesson 4](http://go.gwu.edu/engcomp1lesson4). But we'll also learn about a new Python library for data analysis called **pandas**. \n", + "We'll process the data using **numpy**—the array library for Python that we learned about in [Module 1, lesson 2: Getting Started](../../CompMech01-Getting-started/notebooks/02-Getting-started.ipynb). But we'll also learn about a new Python library for data analysis called **pandas**. \n", "\n", "[`pandas`](http://pandas.pydata.org/) is an open-source library providing high-performance, easy-to-use data structures and data-analysis tools. Even though `pandas` is great for data analysis, we won't exploit all its power in this lesson. But we'll learn more about it later on!\n", "\n", @@ -34,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -90,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -99,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -108,7 +107,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -126,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -228,7 +227,7 @@ " ...\n", " \n", " \n", - " 2405\n", + " 2402\n", " 2405\n", " 0.067\n", " 45.0\n", @@ -239,7 +238,7 @@ " 12.0\n", " \n", " \n", - " 2406\n", + " 2403\n", " 2406\n", " 0.052\n", " NaN\n", @@ -250,7 +249,7 @@ " 12.0\n", " \n", " \n", - " 2407\n", + " 2404\n", " 2407\n", " 0.055\n", " NaN\n", @@ -261,7 +260,7 @@ " 12.0\n", " \n", " \n", - " 2408\n", + " 2405\n", " 2408\n", " 0.055\n", " 40.0\n", @@ -272,7 +271,7 @@ " 12.0\n", " \n", " \n", - " 2409\n", + " 2406\n", " 2409\n", " 0.052\n", " NaN\n", @@ -284,7 +283,7 @@ " \n", " \n", "\n", - "

2410 rows × 8 columns

\n", + "

2407 rows × 8 columns

\n", "" ], "text/plain": [ @@ -295,11 +294,11 @@ "3 3 0.090 NaN 2263 Sinister \n", "4 4 0.075 NaN 2262 Sex and Candy \n", "... ... ... ... ... ... \n", - "2405 2405 0.067 45.0 928 Belgorado \n", - "2406 2406 0.052 NaN 807 Rail Yard Ale \n", - "2407 2407 0.055 NaN 620 B3K Black Lager \n", - "2408 2408 0.055 40.0 145 Silverback Pale Ale \n", - "2409 2409 0.052 NaN 84 Rail Yard Ale (2009) \n", + "2402 2405 0.067 45.0 928 Belgorado \n", + "2403 2406 0.052 NaN 807 Rail Yard Ale \n", + "2404 2407 0.055 NaN 620 B3K Black Lager \n", + "2405 2408 0.055 40.0 145 Silverback Pale Ale \n", + "2406 2409 0.052 NaN 84 Rail Yard Ale (2009) \n", "\n", " style brewery_id ounces \n", "0 American Pale Lager 408 12.0 \n", @@ -308,16 +307,16 @@ "3 American Double / Imperial IPA 177 12.0 \n", "4 American IPA 177 12.0 \n", "... ... ... ... \n", - "2405 Belgian IPA 424 12.0 \n", + "2402 Belgian IPA 424 12.0 \n", + "2403 American Amber / Red Ale 424 12.0 \n", + "2404 Schwarzbier 424 12.0 \n", + "2405 American Pale Ale (APA) 424 12.0 \n", "2406 American Amber / Red Ale 424 12.0 \n", - "2407 Schwarzbier 424 12.0 \n", - "2408 American Pale Ale (APA) 424 12.0 \n", - "2409 American Amber / Red Ale 424 12.0 \n", "\n", - "[2410 rows x 8 columns]" + "[2407 rows x 8 columns]" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -362,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -371,7 +370,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -389,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -408,7 +407,7 @@ "Name: abv, dtype: float64" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -428,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -437,16 +436,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2410" + "2407" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -464,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -480,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -489,7 +488,7 @@ "2348" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -507,7 +506,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -516,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -533,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -542,7 +541,7 @@ "numpy.ndarray" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -560,16 +559,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2410" + "2407" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -582,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -591,7 +590,7 @@ "1405" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -638,7 +637,7 @@ "## Step 3: Ready, stats, go!\n", "\n", "\n", - "Now that we have numpy arrays with clean data, let's see how we can manipulate them to get some useful information. \n", + "Now that we have numpy arrays with clean data, let's see how we can process them to get some useful information. \n", "\n", "Focusing on the numerical variables `abv` and `ibu`, we'll walk through some \"descriptive statistics,\" below. In other words, we aim to generate statistics that summarize the data concisely." ] @@ -662,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -672,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -698,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -708,7 +707,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -744,7 +743,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -765,7 +764,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -789,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -820,7 +819,7 @@ "\n", "While the mean indicates where's the center of your data, the **variance** and **standard deviation** describe the *spread* or variability of the data. We already mentioned that the _range_ (difference between largest and smallest data values) is also an indication of variability. But the standard deviation is the most common measure of variability.\n", "\n", - "We really like the way [Prof. Kristin Sainani](https://profiles.stanford.edu/kristin-sainani), of Stanford University, presents this in her online course on [Statistics in Medicine](https://lagunita.stanford.edu/courses/Medicine/MedStats-SP/SelfPaced/about). In her lecture \"Describing Quantitative Data: Whhat is the variability in the data?\", available [on YouTube](https://youtu.be/hlFeEQF5tDc), she asks: _What if someone were to ask you to devise a statistic that gives the avarage distance from the mean?_ Think about this a little bit.\n", + "We really like the way [Prof. Kristin Sainani](https://profiles.stanford.edu/kristin-sainani), of Stanford University, presents this in her online course on [Statistics in Medicine](https://lagunita.stanford.edu/courses/Medicine/MedStats-SP/SelfPaced/about). In her lecture \"Describing Quantitative Data: What is the variability in the data?\", available [on YouTube](https://youtu.be/hlFeEQF5tDc), she asks: _What if someone were to ask you to devise a statistic that gives the avarage distance from the mean?_ Think about this a little bit.\n", "\n", "The distance from the mean, for any data value, is $x_i - \\bar{x}$. So what is the average of the distances from the mean? If we try to simply compute the average of all the values $x_i - \\bar{x}$, some of which are negative, you'll just get zero! It doesn't work.\n", "\n", @@ -868,7 +867,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -927,7 +926,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -958,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -975,7 +974,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1009,12 +1008,12 @@ "\n", "Thanks to Python and Matplotlib, making histograms is easy. We recommend that you always read the documentation, in this case about [histograms](https://matplotlib.org/devdocs/api/_as_gen/matplotlib.pyplot.hist.html). We'll show you here an example using the `hist()` function from `pyplot`, but this is just a starting point. \n", "\n", - "Let's import the libraries that we need for plotting, as you learned in [Module 1 Lesson 5](http://go.gwu.edu/engcomp1lesson5), then study the plotting commands used below. Try changing some of the plot options and seeing the effect." + "Let's first load the **Matplotlib** module called `pyplot`, for making 2D plots. Remember that to get the plots inside the notebook, we use a special \"magic\" command, `%matplotlib inline`:" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1022,21 +1021,83 @@ "%matplotlib inline\n", "\n", "#Import rcParams to set font styles\n", - "from matplotlib import rcParams\n", + "from matplotlib import rcParams" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 1., 0., 38., 699., 857., 486., 159., 104., 1., 3.]),\n", + " array([0.001 , 0.0137, 0.0264, 0.0391, 0.0518, 0.0645, 0.0772, 0.0899,\n", + " 0.1026, 0.1153, 0.128 ]),\n", + " )" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pyplot.hist(abv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have a line plot, but if you see this plot without any information you would not be able to figure out what kind of data it is! We need labels on the axes, a title and why not a better color, font and size of the ticks. \n", + "**Publication quality** plots should always be your standard for plotting. \n", + "How you present your data will allow others (and probably you in the future) to better understand your work. \n", "\n", - "#Set font style and size \n", - "rcParams['font.family'] = 'serif'\n", - "rcParams['font.size'] = 16" + "We can customize the style of our plots using parameters for the lines, text, font and other plot options. We set some style options that apply for all the plots in the current session with [`plt.rc()`](https://matplotlib.org/api/_as_gen/matplotlib.plt.rc.html)\n", + "Here, we'll make the font of a specific type and size (sans-serif and 18 points). You can also customize other parameters like line width, color, and so on (check out the documentation)." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, + "outputs": [], + "source": [ + "#Set font style and size \n", + "rcParams['font.family'] = 'sans'\n", + "rcParams['lines.linewidth'] = 3\n", + "rcParams['font.size'] = 18" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll redo the same plot, but now we'll add a few things to make it prettier and **publication quality**. We'll add a title, label the axes and, show a background grid. Study the commands below and look at the result!" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1052,7 +1113,7 @@ "pyplot.figure(figsize=(10,5))\n", "\n", "#Plotting\n", - "pyplot.hist(abv, bins=20, color='#3498db', histtype='bar', edgecolor='white') \n", + "pyplot.hist(abv, bins=20, color='b', histtype='bar', edgecolor='white') \n", "#The \\n is to leave a blank line between the title and the plot\n", "pyplot.title('abv \\n')\n", "pyplot.xlabel('Alcohol by Volume (abv) ')\n", @@ -1061,12 +1122,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1082,7 +1143,7 @@ "pyplot.figure(figsize=(10,5))\n", "\n", "#Plotting\n", - "pyplot.hist(ibu, bins=20, color='#e67e22', histtype='bar', edgecolor='white') \n", + "pyplot.hist(ibu, bins=20, color=(0.9, 0.5, 0, 0.9), histtype='bar', edgecolor='white') \n", "#The \\n is to leave a blanck line between the title and the plot\n", "pyplot.title('ibu \\n')\n", "pyplot.xlabel('International Bittering Units (ibu)')\n", @@ -1093,7 +1154,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### Exploratory exercise:\n", + "In the previous two plots we set the colors in two ways:\n", + "\n", + "1. A string 'b', which specifies blue. We could also choose from: a string representation \n", + "one of \n", + "\n", + " {'b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'} = \n", + " {**b**lue, **g**reen, **r**ed, **c**yan, **m**agenta, **y**ellow, blac**k**, **w**hite}\n", + " \n", + "2. A RGB or RGBA (red, green, blue, alpha) tuple of float values in [0, 1] (e.g., (0.1, 0.2, 0.5) or (0.1, 0.2, 0.5, 0.3));\n", + "\n", + "Check out the other formatting options use can use in [Matplotlib colors](https://matplotlib.org/3.1.1/tutorials/colors/colors.html) and [Matplotlib hist command](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.hist.html?highlight=hist#matplotlib.pyplot.hist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploratory exercise:\n", "\n", "Play around with the plots, change the values of the bins, colors, etc." ] @@ -1107,6 +1185,7 @@ "A **normal** (or Gaussian) distribution is a special type of distrubution that behaves as shown in the figure: 68% of the values are within one standard deviation $\\sigma$ from the mean; 95% lie within $2\\sigma$; and at a distance of $\\pm3\\sigma$ from the mean, we cover 99.7% of the values. This fact is known as the $3$-$\\sigma$ rule, or 68-95-99.7 (empirical) rule.\n", "\n", " \n", + "\n", "#### Standard deviation and coverage in a normal distribution. Modified figure based on original from [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Standard_deviation_diagram.svg), the free media repository.\n", "\n", "\n", @@ -1117,15 +1196,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##### Discuss with your neighbor\n", + "## Discussion point\n", "\n", "* How do you think that skewness will affect the percentages of coverage by standard deviation compared to the Bell Curve?\n", "\n", "* Can we calculate those percentages? \n", "\n", - "##### Spoiler alert! (and Exercise)\n", + "## Exercise\n", "\n", - "Yes we can, and guess what: we can do it in a few lines of Python. But before doing that, we want you to explain in your own words how the following piece of code works. \n", + "We can calculate skewness and standard deviation in a few lines of Python. But before doing that, we want you to explain in your own words how the following piece of code works. \n", "\n", "*Hints:* \n", "\n", @@ -1135,7 +1214,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1163,7 +1242,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1218,7 +1297,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1234,7 +1313,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1262,7 +1341,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1271,7 +1350,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1334,180 +1413,6 @@ "* [Describing Quantitative Data: What is the variability in the data?](https://youtu.be/hlFeEQF5tDc)\n", "* [Variability in the data, continued: examples, bell curve](https://youtu.be/qeG0uNI3DBQ)" ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Execute this cell to load the notebook's style sheet, then ignore it\n", - "from IPython.core.display import HTML\n", - "css_file = '../style/custom.css'\n", - "HTML(open(css_file, \"r\").read())" - ] } ], "metadata": { diff --git a/notebooks/.ipynb_checkpoints/02_Seeing_Stats-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/02_Seeing_Stats-checkpoint.ipynb index 3f26a3d..1193c0a 100644 --- a/notebooks/.ipynb_checkpoints/02_Seeing_Stats-checkpoint.ipynb +++ b/notebooks/.ipynb_checkpoints/02_Seeing_Stats-checkpoint.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -101,7 +101,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 22, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -303,7 +303,7 @@ "[424 rows x 8 columns]" ] }, - "execution_count": 23, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -314,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -325,7 +325,7 @@ " dtype='object')" ] }, - "execution_count": 24, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -365,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -377,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -444,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -501,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -526,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -587,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -609,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -656,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -690,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -758,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -767,7 +767,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -776,7 +776,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 36, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -787,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -830,7 +830,7 @@ " 'Low Alcohol Beer'], dtype=object)" ] }, - "execution_count": 37, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -855,63 +855,63 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "American Double / Imperial Stout 9\n", - "Schwarzbier 9\n", "Herbed / Spiced Beer 9\n", + "Schwarzbier 9\n", + "American Double / Imperial Stout 9\n", "Belgian Strong Pale Ale 7\n", - "Doppelbock 7\n", - "Bock 7\n", "American Dark Wheat Ale 7\n", "Bière de Garde 7\n", - "American Wild Ale 6\n", + "Doppelbock 7\n", + "Bock 7\n", + "Belgian Strong Dark Ale 6\n", "California Common / Steam Beer 6\n", + "American Wild Ale 6\n", + "Baltic Porter 6\n", "Foreign / Export Stout 6\n", "English Dark Mild Ale 6\n", - "Belgian Strong Dark Ale 6\n", - "Baltic Porter 6\n", "Dortmunder / Export Lager 6\n", - "Dubbel 5\n", - "Irish Dry Stout 5\n", "Euro Dark Lager 5\n", - "Maibock / Helles Bock 5\n", "Mead 5\n", - "Quadrupel (Quad) 4\n", + "Irish Dry Stout 5\n", + "Dubbel 5\n", + "Maibock / Helles Bock 5\n", "English Strong Ale 4\n", - "Dunkelweizen 4\n", + "Quadrupel (Quad) 4\n", "Munich Dunkel Lager 4\n", + "Dunkelweizen 4\n", + "American India Pale Lager 3\n", "Keller Bier / Zwickel Bier 3\n", "English Barleywine 3\n", - "Radler 3\n", - "English Bitter 3\n", - "American India Pale Lager 3\n", - "Shandy 3\n", "Chile Beer 3\n", - "English Pale Mild Ale 3\n", + "English Bitter 3\n", "American Barleywine 3\n", + "Radler 3\n", + "English Pale Mild Ale 3\n", + "Shandy 3\n", + "Rauchbier 2\n", + "Abbey Single Ale 2\n", "English Stout 2\n", + "Euro Pale Lager 2\n", "Old Ale 2\n", - "American Double / Imperial Pilsner 2\n", "Roggenbier 2\n", - "Rauchbier 2\n", - "Euro Pale Lager 2\n", - "Abbey Single Ale 2\n", - "Other 1\n", + "American Double / Imperial Pilsner 2\n", + "Grisette 1\n", + "American Malt Liquor 1\n", + "Low Alcohol Beer 1\n", "Braggot 1\n", + "Flanders Red Ale 1\n", + "Wheat Ale 1\n", "Flanders Oud Bruin 1\n", "Kristalweizen 1\n", - "Grisette 1\n", - "Wheat Ale 1\n", - "Low Alcohol Beer 1\n", "Smoked Beer 1\n", - "Flanders Red Ale 1\n", - "American Malt Liquor 1\n", + "Other 1\n", "Name: style, dtype: int64\n" ] } @@ -923,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -932,7 +932,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 39, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -943,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -952,7 +952,7 @@ "99" ] }, - "execution_count": 40, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -979,12 +979,12 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - 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\n", 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\n", 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" ] @@ -1025,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1034,7 +1034,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1043,7 +1043,7 @@ "1405" ] }, - "execution_count": 75, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1055,7 +1055,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1064,7 +1064,7 @@ "1405" ] }, - "execution_count": 76, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1090,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1140,7 +1140,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1156,7 +1156,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1165,7 +1165,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1174,7 +1174,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 80, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1185,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1194,7 +1194,7 @@ "90" ] }, - "execution_count": 81, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1223,7 +1223,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1232,7 +1232,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1344,7 +1344,7 @@ "[90 rows x 2 columns]" ] }, - "execution_count": 83, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1371,7 +1371,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1380,7 +1380,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1399,7 +1399,7 @@ "Name: style, dtype: int64" ] }, - "execution_count": 85, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1417,7 +1417,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1455,7 +1455,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1465,7 +1465,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1506,7 +1506,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1578,6 +1578,33 @@ " scatter plot with `beers_filled`. What differences do you notice between the plots? " ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Gordon Moore created an empirical prediction that the rate of semiconductors on a computer chip would double every two years. This prediction was known as Moore's law. Gordon Moore had originally only expected this empirical relation to hold from 1965 - 1975 [[1](https://en.wikipedia.org/wiki/Moore%27s_law),[2](https://spectrum.ieee.org/computing/hardware/gordon-moore-the-man-whose-name-means-progress)], but semiconductor manufacuturers were able to keep up with Moore's law until 2015. \n", + "\n", + " In the folder \"../data\" is a comma separated value (CSV) file, \"transistor_data.csv\" [taken from wikipedia 01/2020](https://en.wikipedia.org/wiki/Transistor_count#Microprocessors). \n", + " Load the csv into a pandas dataframe, it has the following headings:\n", + "\n", + " |Processor| MOS transistor count| Date of Introduction|Designer|MOSprocess|Area|\n", + " |---|---|---|---|---|---|\n", + "\n", + " a. In the years 2017, what was the average MOS transistor count? \n", + " Make a boxplot of the transistor count in 2017 and find the first, second and third quartiles.\n", + "\n", + " b. Create a semilog y-axis scatter plot (i.e. `plt.semilogy`) for the \n", + " \"Date of Introduction\" vs \"MOS transistor count\". \n", + " Color the data according to the \"Designer\"." + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/notebooks/.ipynb_checkpoints/03_Linear_Regression_with_Real_Data-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/03_Linear_Regression_with_Real_Data-checkpoint.ipynb new file mode 100644 index 0000000..21438c1 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/03_Linear_Regression_with_Real_Data-checkpoint.ipynb @@ -0,0 +1,974 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Modified under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2020 R.C. Cooper" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear regression with real data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Earth temperature over time\n", + "\n", + "In this lesson, we will analyze real data of Earth temperature over time.\n", + "\n", + "Is global temperature rising? How much? This is a question of burning importance in today's world!\n", + "\n", + "Data about global temperatures are available from several sources: NASA, the National Climatic Data Center (NCDC) and the University of East Anglia in the UK. Check out the [University Corporation for Atmospheric Research](https://www2.ucar.edu/climate/faq/how-much-has-global-temperature-risen-last-100-years) (UCAR) for an in-depth discussion.\n", + "\n", + "The [NASA Goddard Space Flight Center](http://svs.gsfc.nasa.gov/goto?3901) is one of our sources of global climate data. They produced the video below showing a color map of the changing global surface **temperature anomalies** from 1880 to 2015.\n", + "\n", + "The term [global temperature anomaly](https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php) means the difference in temperature with respect to a reference value or a long-term average. It is a very useful way of looking at the problem and in many ways better than absolute temperature. For example, a winter month may be colder than average in Washington DC, and also in Miami, but the absolute temperatures will be different in both places." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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\n", + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('gGOzHVUQCw0')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How would we go about understanding the _trends_ from the data on global temperature?\n", + "\n", + "The first step in analyzing unknown data is to generate some simple plots using **Matplotlib**. We are going to look at the temperature-anomaly history, contained in a file, and make our first plot to explore this data. \n", + "\n", + "We are going to smooth the data and then we'll fit a line to it to find a trend, plotting along the way to see how it all looks.\n", + "\n", + "Let's get started!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Read a data file\n", + "\n", + "We took the data from the [NOAA](https://www.ncdc.noaa.gov/cag/) (National Oceanic and Atmospheric Administration) webpage. Feel free to play around with the webpage and analyze data on your own, but for now, let's make sure we're working with the same dataset.\n", + "\n", + "\n", + "We have a file named `land_global_temperature_anomaly-1880-2016.csv` in our `data` folder. This file contains the year on the first column, and averages of land temperature anomaly listed sequentially on the second column, from the year 1880 to 2016. We will load the file, then make an initial plot to see what it looks like.\n", + "\n", + "\n", + "Let's start by importing NumPy and pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To load our data from the file, we'll use the function [`numpy.loadtxt()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html), which lets us immediately save the data into NumPy arrays. (We encourage you to read the documentation for details on how the function works.) Here, we'll save the data into the arrays `year` and `temp_anomaly`. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "fname = '../data/land_global_temperature_anomaly-1880-2016.csv'\n", + "\n", + "temp_data = pd.read_csv(fname,skiprows=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercise\n", + "\n", + "Inspect the data by printing `temp_data`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Plot the data\n", + "\n", + "Let's first load the **Matplotlib** module called `pyplot`, for making 2D plots. Remember that to get the plots inside the notebook, we use a special \"magic\" command, `%matplotlib inline`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "#Import rcParams to set font styles\n", + "from matplotlib import rcParams\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `plot()` function of the `pyplot` module makes simple line plots. We avoid that stuff that appeared on top of the figure, that `Out[x]: [< ...>]` ugliness, by adding a semicolon at the end of the plotting command." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#Set font style and size \n", + "rcParams['font.family'] = 'sans'\n", + "rcParams['font.size'] = 16\n", + "rcParams['lines.linewidth'] = 3" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#You can set the size of the figure by doing:\n", + "plt.figure(figsize=(10,5))\n", + "\n", + "#Plotting\n", + "plt.plot(temp_data['Year'], temp_data['Value'], color='#2929a3', linestyle='-', linewidth=1) \n", + "plt.title('Land global temperature anomalies. \\n')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Land temperature anomaly [°C]')\n", + "plt.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Least-squares linear regression \n", + "\n", + "In order to have an idea of the general behavior of our data, we can find a smooth curve that (approximately) fits the points. We generally look for a curve that's simple (e.g., a polynomial), and does not reproduce the noise that's always present in experimental data. \n", + "\n", + "Let $f(x)$ be the function that we'll fit to the $n+1$ data points: $(x_i, y_i)$, $i = 0, 1, ... ,n$:\n", + "\n", + "$$ \n", + " f(x) = f(x; a_0, a_1, ... , a_m) \n", + "$$\n", + "\n", + "The notation above means that $f$ is a function of $x$, with $m+1$ variable parameters $a_0, a_1, ... , a_m$, where $m < n$. We need to choose the form of $f(x)$ _a priori_, by inspecting the experimental data and knowing something about the phenomenon we've measured. Thus, curve fitting consists of two steps: \n", + "\n", + "1. Choosing the form of $f(x)$.\n", + "2. Computing the parameters that will give us the \"best fit\" to the data. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is the \"best\" fit?\n", + "\n", + "When the noise in the data is limited to the $y$-coordinate, it's common to use a **least-squares fit** [2], which minimizes the function\n", + "\n", + "$$\n", + "\\begin{equation} \n", + " S(a_0, a_1, ... , a_m) = \\sum_{i=0}^{n} [y_i - f(x_i)]^2\n", + "\\end{equation}~~~~~~(1) \n", + "$$\n", + "\n", + "with respect to each $a_j$. We find the values of the parameters for the best fit by solving the following equations:\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\frac{\\partial{S}}{\\partial{a_k}} = 0, \\quad k = 0, 1, ... , m.\n", + "\\end{equation}~~~~~~(2)\n", + "$$\n", + "\n", + "Here, the terms $r_i = y_i - f(x_i)$ are called residuals: they tell us the discrepancy between the data and the fitting function at $x_i$. \n", + "\n", + "Take a look at the function $S$: what we want to minimize is the sum of the squares of the residuals. The equations (2) are generally nonlinear in $a_j$ and might be difficult to solve. Therefore, the fitting function is commonly chosen as a linear combination of specified functions $f_j(x)$, \n", + "\n", + "$$\n", + "\\begin{equation*}\n", + " f(x) = a_0f_0(x) + a_1f_1(x) + ... + a_mf_m(x)\n", + "\\end{equation*}~~~~~~(3)\n", + "$$\n", + "\n", + "which results in equations (2) being linear. In the case that the fitting function is polynomial, we have have $f_0(x) = 1, \\; f_1(x) = x, \\; f_2(x) = x^2$, and so on. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear regression \n", + "\n", + "When we talk about linear regression we mean \"fitting a function to the data.\" In this case,\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " f(x) = a_0 + a_1x\n", + "\\end{equation}~~~~~~(4)\n", + "$$\n", + "\n", + "The function that we'll minimize is:\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " S(a_0, a_1) = \\sum_{i=0}^{n} [y_i - f(x_i)]^2 = \\sum_{i=0}^{n} (y_i - a_0 - a_1x_i)^2 \n", + "\\end{equation}~~~~~~(5) \n", + "$$\n", + "\n", + "Equations (2) become:\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\frac{\\partial{S}}{\\partial{a_0}} = \\sum_{i=0}^{n} -2(y_i - a_0 - a_1x_i) = 2 \\left[ a_0(n+1) + a_1\\sum_{i=0}^{n} x_i - \\sum_{i=0}^{n} y_i \\right] = 0\n", + "\\end{equation}~~~~~~(6) \n", + "$$\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\frac{\\partial{S}}{\\partial{a_1}} = \\sum_{i=0}^{n} -2(y_i - a_0 - a_1x_i)x_i = 2 \\left[ a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i \\right] = 0\n", + "\\end{equation}~~~~~~(7) \n", + "$$\n", + "\n", + "Let's divide both equations by $2(n+1)$ and rearrange terms.\n", + "\n", + "Rearranging (6) and (7):\n", + "\n", + "$$\n", + "\\begin{align}\n", + " 2 \\left[ a_0(n+1) + a_1\\sum_{i=0}^{n} x_i - \\sum_{i=0}^{n} y_i \\right] &= 0 \\nonumber \\\\ \n", + " \\frac{a_0(n+1)}{n+1} + a_1 \\frac{\\sum_{i=0}^{n} x_i}{n+1} - \\frac{\\sum_{i=0}^{n} y_i}{n+1} &= 0 \\\\\n", + "\\end{align}~~~~~~(8)\n", + "$$\n", + "\n", + "$$\n", + "\\begin{align}\n", + " a_0 = \\bar{y} - a_1\\bar{x}\n", + "\\end{align}~~~~~~(9)\n", + "$$\n", + "\n", + "where $\\bar{x} = \\frac{\\sum_{i=0}^{n} x_i}{n+1}$ and $\\bar{y} = \\frac{\\sum_{i=0}^{n} y_i}{n+1}$.\n", + "\n", + "Rearranging (7):\n", + "\n", + "$$\n", + "\\begin{align}\n", + " 2 \\left[ a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i \\right] &= 0 \\\\\n", + " a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &=0 \\\\\n", + "\\end{align}~~~~~~(10)\n", + "$$\n", + "\n", + "Now, if we replace $a_0$ from equation (8) into (9) and rearrange terms:\n", + "\n", + "$$\n", + "\\begin{align*}\n", + " (\\bar{y} - a_1\\bar{x})\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &= 0 \\\\ \n", + "\\end{align*}~~~~~~(11)\n", + "$$\n", + "\n", + "Replacing the definitions of the mean values into the equation, \n", + "\n", + "$$\n", + "\\begin{align*}\n", + " \\left[\\frac{1}{n+1}\\sum_{i=0}^{n} y_i - \\frac{a_1}{n+1}\\sum_{i=0}^{n} x_i \\right]\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &= 0 \\\\ \n", + " \\frac{1}{n+1}\\sum_{i=0}^{n} y_i \\sum_{i=0}^{n} x_i - \\frac{a_1}{n+1}\\sum_{i=0}^{n} x_i \\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &= 0 \\\\ \n", + "\\end{align*}~~~~~~(12)\n", + "$$\n", + "\n", + "Leaving everything in terms of $\\bar{x}$, \n", + "\n", + "$$\n", + "\\begin{align*}\n", + " \\sum_{i=0}^{n} y_i \\bar{x} - a_1\\sum_{i=0}^{n} x_i \\bar{x} + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i = 0 \n", + "\\end{align*}~~~~~~(13)\n", + "$$\n", + "\n", + "Grouping the terms that have $a_1$ on the left-hand side and the rest on the right-hand side:\n", + "\n", + "$$\n", + "\\begin{align*}\n", + " a_1\\left[ \\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_i \\bar{x}\\right] &= \\sum_{i=0}^{n} x_iy_i - \\sum_{i=0}^{n} y_i \\bar{x} \\\\\n", + " a_1 \\sum_{i=0}^{n} (x_{i}^2 - x_i \\bar{x}) &= \\sum_{i=0}^{n} (x_iy_i - y_i \\bar{x}) \\\\\n", + " a_1 \\sum_{i=0}^{n} x_{i}(x_{i} -\\bar{x}) &= \\sum_{i=0}^{n} y_i(x_i - \\bar{x}) \n", + "\\end{align*}~~~~~~(14)\n", + "$$\n", + "\n", + "Finally, we get that:\n", + "\n", + "$$\n", + "\\begin{align}\n", + " a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})}\n", + "\\end{align}~~~~~~(15)\n", + "$$\n", + "\n", + "Then our coefficients are:\n", + "\n", + "$$\n", + "\\begin{align}\n", + " a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})} \\quad , \\quad a_0 = \\bar{y} - a_1\\bar{x}\n", + "\\end{align}~~~~~~(16)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's fit!\n", + "\n", + "Let's now fit a straight line through the temperature-anomaly data, to see the trend over time. We'll use least-squares linear regression to find the slope and intercept of a line \n", + "\n", + "$y = a_1x+a_0$\n", + "\n", + "that fits our data.\n", + "\n", + "In our case, the `x`-data corresponds to `Year`, and the `y`-data is `Value`. To calculate our coefficients with the formula above, we need the mean values of our data. Since we'll need to compute the mean for both `x` and `y`. \n", + "\n", + "It is good coding practice to *avoid repeating* ourselves: we want to write code that is reusable, not only because it leads to less typing but also because it reduces errors. If you find yourself doing the same calculation multiple times, it's better to encapsulate it into a *function*. \n", + "\n", + "Remember the _key concept_ from [02_Working_with_Python](../../CompMech01-Getting-started/notebooks/02_Working_with_Python.ipynb): A function is a compact collection of code that executes some action on its arguments. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise \n", + "\n", + "Calculate the mean of the `year` and `temp_anomaly` arrays using the NumPy built-in function, `np.mean`.\n", + "\n", + "Assign the means to `mean_x` and `mean_y`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have mean values, we can compute our coefficients by following equations (12). We first calculate $a_1$ and then use that value to calculate $a_0$.\n", + "\n", + "Our coefficients are:\n", + "\n", + "$$\n", + " a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})} \\quad , \\quad a_0 = \\bar{y} - a_1\\bar{x}\n", + "$$ \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We already calculated the mean values of the data arrays, but the formula requires two sums over new derived arrays. Guess what, NumPy has a built-in function for that: [`numpy.sum()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html). Study the code below." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "xi = temp_data['Year'].values\n", + "yi = temp_data['Value'].values\n", + "\n", + "x_mean = np.mean(xi)\n", + "y_mean = np.mean(yi)\n", + "\n", + "a_1 = np.sum(yi*(xi - x_mean)) / np.sum(xi*(xi - x_mean)) " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.01037028394347266\n" + ] + } + ], + "source": [ + "print(a_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "a_0 = y_mean - a_1*x_mean" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-20.148685384658464\n" + ] + } + ], + "source": [ + "print(a_0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercise\n", + "\n", + "Write a function that computes the coefficients, call the function to compute them and compare the result with the values we obtained before. As a hint, we give you the structure that you should follow:\n", + "\n", + "```python\n", + "def coefficients(x, y, x_mean, y_mean):\n", + " \"\"\"\n", + " Write docstrings here\n", + " \"\"\"\n", + "\n", + " a_1 = \n", + " a_0 = \n", + " \n", + " return a_1, a_0\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def coefficients(x, y, x_mean, y_mean):\n", + " \"\"\"\n", + " Write docstrings here\n", + " Arguments\n", + " ---------\n", + " ??\n", + " Returns\n", + " -------\n", + " ??\n", + " \"\"\"\n", + " # your code here\n", + " \n", + " return a_1, a_0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have the coefficients of a linear function that best fits our data. With them, we can compute the predicted values of temperature anomaly, according to our fit. Check again the equations above: the values we are going to compute are $f(x_i)$. \n", + "\n", + "Let's call `reg` the array obtined from evaluating $f(x_i)$ for all years." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "reg = a_0 + a_1 * xi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the values of our linear regression, we can plot it on top of the original data to see how they look together. Study the code below. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "\n", + "plt.plot(xi, yi,'s', color='#2929a3', linewidth=1, alpha=0.5,label='Measured anomoly') \n", + "plt.plot(xi, reg, 'k--', linewidth=2, label='Linear regression')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Land temperature anomaly [°C]')\n", + "plt.legend(loc='best', fontsize=15)\n", + "plt.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Apply regression using NumPy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above, we coded linear regression from scratch. But, guess what: we didn't have to because NumPy has built-in functions that do what we need!\n", + "\n", + "Yes! Python and NumPy are here to help! With [`polyfit()`](https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.polyfit.html), we get the slope and $y$-intercept of the line that best fits the data. With [`poly1d()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.poly1d.html), we can build the linear function from its slope and $y$-intercept.\n", + "\n", + "Check it out:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# First fit with NumPy, then name the coefficients obtained a_1n, a_0n:\n", + "a_1n, a_0n = np.polyfit(xi, yi, 1)\n", + "\n", + "f_linear = np.poly1d((a_1n, a_0n)) " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.010370283943472659\n" + ] + } + ], + "source": [ + "print(a_1n)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-20.148685384658457\n" + ] + } + ], + "source": [ + "print(a_0n)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "0.01037 x - 20.15\n" + ] + } + ], + "source": [ + "print(f_linear)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The assignment `f_linear = np.poly1d((a_1n,a_0n))` creates a 1D polynomial. This means that the function only has one independent variable i.e. f(x) = (some value). You can create your own polynomial functions in a similar way using _anonymous functions_ i.e. `lambda`.\n", + "\n", + "```python\n", + "f_linear = lambda x: a_1n*x + a_0n\n", + "```\n", + "\n", + "In the line of code given above, we create the same assignment for `f_linear(x)`. One benefit of writing this out yourself is that you can see how each input is used directly. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "\n", + "Use the `lambda` function to assign `f_linear` to our 1D polynomial instead of the `np.poly1d` assignment. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "\n", + "plt.plot(xi, yi,'s', color='#2929a3', linewidth=1, alpha=0.5,label='Measured anomoly')\n", + "plt.plot(xi, f_linear(xi), 'k--', linewidth=2, label='Linear regression')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Land temperature anomaly [°C]')\n", + "plt.legend(loc='best', fontsize=15)\n", + "plt.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## \"Split regression\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you look at the plot above, you might notice that around 1970 the temperature starts increasing faster that the previous trend. So maybe one single straight line does not give us a good-enough fit.\n", + "\n", + "What if we break the data in two (before and after 1970) and do a linear regression in each segment? \n", + "\n", + "To do that, we first need to find the position in our `year` array where the year 1970 is located. Thankfully, NumPy has a function called [`numpy.where()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) that can help us. We pass a condition and `numpy.where()` tells us where in the array the condition is `True`. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'numpy' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1970\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'numpy' is not defined" + ] + } + ], + "source": [ + "numpy.where(year==1970)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To split the data, we use the powerful instrument of _slicing_ with the colon notation. Remember that a colon between two indices indicates a range of values from a `start` to an `end`. The rule is that `[start:end]` includes the element at index `start` but excludes the one at index `end`. For example, to grab the first 3 years in our `year` array, we do:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "year[0:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we know how to split our data in two sets, to get two regression lines. We need two slices of the arrays `year` and `temp_anomaly`, which we'll save in new variable names below. After that, we complete two linear fits using the helpful NumPy functions we learned above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "year_1 , temp_anomaly_1 = year[0:90], temp_anomaly[0:90]\n", + "year_2 , temp_anomaly_2 = year[90:], temp_anomaly[90:]\n", + "\n", + "m1, b1 = numpy.polyfit(year_1, temp_anomaly_1, 1)\n", + "m2, b2 = numpy.polyfit(year_2, temp_anomaly_2, 1)\n", + "\n", + "f_linear_1 = numpy.poly1d((m1, b1))\n", + "f_linear_2 = numpy.poly1d((m2, b2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "\n", + "plt.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1, alpha=0.5) \n", + "plt.plot(year_1, f_linear_1(year_1), 'g--', linewidth=2, label='1880-1969')\n", + "plt.plot(year_2, f_linear_2(year_2), 'r--', linewidth=2, label='1970-2016')\n", + "\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Land temperature anomaly [°C]')\n", + "plt.legend(loc='best', fontsize=15)\n", + "plt.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Discussion exercise\n", + "We have two different curves for two different parts of our data set. A little problem with this and is that the end point of our first regression doesn't match the starting point of the second regression. We did this for the purpose of learning, but it is not rigorously correct. \n", + "\n", + "How would you fix this issue? \n", + "\n", + "What would your new function, $f(x)$, look like?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## We learned:\n", + "\n", + "* Making our plots more beautiful\n", + "* Defining and calling custom Python functions\n", + "* Applying linear regression to data\n", + "* NumPy built-ins for linear regression\n", + "* The Earth is warming up!!!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "1. [_Essential skills for reproducible research computing_](https://barbagroup.github.io/essential_skills_RRC/) (2017). Lorena A. Barba, Natalia C. Clementi, Gilbert Forsyth. \n", + "2. _Numerical Methods in Engineering with Python 3_ (2013). Jaan Kiusalaas. Cambridge University Press.\n", + "3. _Effective Computation in Physics: Field Guide to Research with Python_ (2015). Anthony Scopatz & Kathryn D. Huff. O'Reilly Media, Inc.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Problems\n", + "\n", + "1. There is a csv file in '../data/primary-energy-consumption-by-region.csv' that has the energy consumption of different regions of the world from 1965 until 2018 [Our world in Data](https://ourworldindata.org/energy). \n", + "We are going to compare the energy consumption of the United States to all of Europe. Load the data into a pandas dataframe. *Note: we can get certain rows of the data frame by specifying what we're looking for e.g. \n", + "`EUR = dataframe[dataframe['Entity']=='Europe']` will give us all the rows from Europe's energy consumption.*\n", + "\n", + " a. Plot the total energy consumption of the United States and Europe\n", + " \n", + " b. Use a linear least-squares regression to find a function for the energy consumption as a function of year\n", + " \n", + " energy consumed = $f(t) = At+B$\n", + " \n", + " c. At what year would you change split the data and use two lines like we did in the \n", + " land temperature anomoly? Split the data and perform two linear fits. \n", + " \n", + " d. What is your prediction for US energy use in 2025? How about European energy use in 2025?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "energy = pd.read_csv('../data/primary-energy-consumption-by-region.csv')\n", + "CAN = energy[energy['Entity']=='Canada']\n", + "USA = energy[energy['Entity']=='United States']\n", + "EUR = energy[energy['Entity']=='Europe']\n", + "plt.plot(USA['Year'],USA['Primary Energy Consumption (terawatt-hours)'],'-o')\n", + "plt.plot(EUR['Year'],EUR['Primary Energy Consumption (terawatt-hours)'],'-s')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. We plotted Gordon Moore's empirical prediction that the rate of semiconductors on a computer chip would double every two years in [02_Seeing_Stats](./02_Seeing_Stats.ipynb). This prediction was known as Moore's law. Gordon Moore had originally only expected this empirical relation to hold from 1965 - 1975 [[1](https://en.wikipedia.org/wiki/Moore%27s_law),[2](https://spectrum.ieee.org/computing/hardware/gordon-moore-the-man-whose-name-means-progress)], but semiconductor manufacuturers were able to keep up with Moore's law until 2015. \n", + "\n", + "We can use a linear regression to find our own historical Moore's Law. \n", + "\n", + "Use your code from [02_Seeing_Stats](./02_Seeing_Stats.ipynb) to plot the semilog y-axis scatter plot \n", + "(i.e. `plt.semilogy`) for the \"Date of Introduction\" vs \"MOS transistor count\". \n", + "Color the data according to the \"Designer\".\n", + "\n", + "Create a linear regression for the data in the form of \n", + "\n", + "$log(transistor~count)= f(date) = A\\cdot date+B$\n", + "\n", + "rearranging\n", + "\n", + "$transistor~count= e^{f(date)} = e^B e^{A\\cdot date}$\n", + "\n", + "You can perform a least-squares linear regression using the following assignments\n", + "\n", + "$x_i=$ `dataframe['Date of Introduction'].values`\n", + "\n", + "and\n", + "\n", + "$y_i=$ as `np.log(dataframe['MOS transistor count'].values)`\n", + "\n", + "a. Plot your function on the semilog y-axis scatter plot\n", + "\n", + "b. What are the values of constants $A$ and $B$ for our Moore's law fit? How does this compare to Gordon Moore's prediction that MOS transistor count doubles every two years?\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + }, + "widgets": { + "state": {}, + "version": "1.1.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/.ipynb_checkpoints/03_stats_and_montecarlo-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/03_stats_and_montecarlo-checkpoint.ipynb deleted file mode 100644 index b1bea65..0000000 --- a/notebooks/.ipynb_checkpoints/03_stats_and_montecarlo-checkpoint.ipynb +++ /dev/null @@ -1,19908 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Statistics and Monte-Carlo Models\n", - "\n", - "Monte Carlo models use random numbers to either understand statistics or generate a solution. \n", - "The main element in a Monte Carlo model is the use of random numbers. We can generate random numbers in many ways, but most programming languages have 'pseudo'-random number generators. \n", - "\n", - "In Python, we use the numpy library as such\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "x=np.random.rand(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([2., 0., 1., 0., 0., 2., 1., 1., 0., 3.]),\n", - " array([0.13831169, 0.22124331, 0.30417493, 0.38710655, 0.47003817,\n", - " 0.55296979, 0.6359014 , 0.71883302, 0.80176464, 0.88469626,\n", - " 0.96762788]),\n", - " )" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.hist(x,bins=10, color='b', histtype='bar', edgecolor='w')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `numpy.random.rand(10)` function generates 10 random numbers between 0 and 1. The pyplot function `hist` then displays a histogram of these randomly generated numbers. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise\n", - "\n", - "Try generating more random numbers and plotting histograms of the results i.e. increase `10` to larger values. \n", - "\n", - "What should the histogram of `x` look like if Python is generating truly random numbers?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Examples of Monte Carlo models:\n", - "\n", - "Monte Carlo models have a wide array of applications. We are going to use Monte Carlo models in later modules to explore how uncertainty in measurements can be incorporated into computational models. Here is a brief list of Monte Carlo model use cases in real-world applications:\n", - "\n", - "- [Eigenvlaues in supercritical systems](https://link.springer.com/chapter/10.1007%2FBFb0049064)\n", - "- [average time between failures for reliability](http://www.egr.msu.edu/~mitraj/research/pubs/proc/singh-mitra_em_stdby_ias95.pdf)\n", - "- disordered materials (physics)\n", - "- [Calculation of the energy output of a wind farm](http://www.mdpi.com/1996-1073/9/4/286/pdf)\n", - "- [US Coast Guard rescue missions](https://en.wikipedia.org/wiki/Search_and_Rescue_Optimal_Planning_System)\n", - "- [Radiation shielding](http://www.sciencedirect.com/science/article/pii/S0920379612000580)\n", - "- [Predict number of asteroids that hit body of water](https://cneos.jpl.nasa.gov/sentry/intro.html)\n", - "- [Financial modeling](https://en.wikipedia.org/wiki/Monte_Carlo_methods_in_finance)\n", - "\n", - "We will explore Monte Carlo modeling through the use of three examples:\n", - "\n", - "1. Calculate the value of $\\pi$\n", - "\n", - "2. Calculate the integral of a function\n", - "\n", - "3. Propagate uncertainty in manufacturing into uncertainty in failure load" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "\n", - "\n", - "# Example 1: Calculate $\\pi$ with random numbers. \n", - "\n", - "Assuming we can actually generate random numbers (a topic of philosophical and heated debates) we can populate a unit square with random points and determine the ratio of points inside and outside of a circle.\n", - "\n", - "![Unit circle and unit square](../images/MonteCarloPi.gif)\n", - "\n", - "![1/4 Unit circle and 1/4 unit square](../images/MonteCarloPi_rand.gif)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "The ratio of the area of the circle to the square is:\n", - "\n", - "$\\frac{\\pi r^{2}}{4r^{2}}=\\frac{\\pi}{4}$\n", - "\n", - "So if we know the fraction of random points that are within the unit circle, then we can calculate $\\pi$\n", - "\n", - "(number of points in circle)/(total number of points)=$\\pi/4$" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.65636947, 0.58895963, 0.82791297, 0.19407244, 0.6149529 ,\n", - " 0.07214425, 0.51728665, 0.74773674, 0.35505096, 0.72987358])" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.rand(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "def montecarlopi(N):\n", - " '''Create random x-y-coordinates to and use ratio of circle-to-square to \n", - " calculate the value of pi\n", - " i.e. Acircle/Asquare = pi/4\n", - " Arguments\n", - " ---------\n", - " N: number of random points to produce between x=0-1 and y=0-1\n", - " \n", - " Returns\n", - " -------\n", - " our_pi: the best prediction of pi using N points\n", - " '''\n", - " \n", - "\n", - " x=np.random.rand(N,1);\n", - " y=np.random.rand(N,1);\n", - " R=np.sqrt(x**2+y**2); # compute radius\n", - " num_in_circle=sum(R<1);\n", - " total_num_pts =len(R);\n", - " our_pi = 4*num_in_circle/total_num_pts;\n", - " return our_pi" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mean value for pi = 3.123600\n", - "standard deviation is 0.056948\n", - "actual pi is 3.141593\n" - ] - } - ], - "source": [ - "test_pi=np.zeros(10)\n", - "for i in range(0,10):\n", - " test_pi[i]=montecarlopi(1000);\n", - "\n", - "print('mean value for pi = %f'%np.mean(test_pi))\n", - "print('standard deviation is %f'%np.std(test_pi))\n", - "print('actual pi is %f'%np.pi)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Exercises\n", - "\n", - "1. Why is there a standard deviation for the value of $\\pi$ calculated with a Monte Carlo method? Does it depend upon how many times you run the function i.e. the size of `test_pi`? or the number of random points `N`? Alter the script above to discover correlations\n", - "\n", - "2. How well does our function `montecarlopi` converge to the true value of $\\pi$ (you can use `np.pi` as a true value)? Plot the convergence as we did in [03-Numerical_error](https://github.uconn.edu/rcc02007/CompMech01-Getting-started/blob/master/notebooks/03-Numerical_error.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example 2: Calculate the integral of a function\n", - "\n", - "One way to calculate the integral of a function, $f(x)$, is to approximate it as a Riemann sum as such\n", - "\n", - "$I=\\int_{a}^{b}f(x) dx \\approx \\Delta x \\left(f(a)+f(a+\\Delta x)+f(a+2\\Delta x)+...+f(b)\\right))$.\n", - "\n", - "Another way to integrate this function is with a [Monte Carlo approach](https://en.wikipedia.org/wiki/Monte_Carlo_integration). We can approximate the integral as such\n", - "\n", - "$I=\\int_{a}^{b}f(x) dx \\approx \\frac{1}{n}\\sum_{i=1}^n f(x_i)$. \n", - "\n", - "where $x_i$ are uniformly random values of $x$ over the interval $[a,b]$. We were actually doing this in two dimensions in Example #1 when we compared the area of the circle to the area of a square. \n", - "\n", - "Visually, this approximation can be represented by the following figure.\n", - "\n", - "![Integration approximation](../images/integrals.png)\n", - "\n", - "The figure above shows the exact integral on the left as compared to two approximations, the Riemann sum (top-right) and Monte Carlo method (bottom-right). \n", - "\n", - "## Exercise (Discussion)\n", - "\n", - "Why does the Monte Carlo method work? Is there a benefit to random numbers as opposed to using an ordered set divided into equally-spaced intervals?" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.0821347195510826\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "f= lambda x:4/(1+x**2)\n", - "\n", - "n=10\n", - "xi=np.random.rand(n)\n", - "\n", - "plt.bar(xi,f(xi),width=1/n,color=[0.1,0,1,0.5],edgecolor='white')\n", - "plt.plot(np.linspace(0,1),f(np.linspace(0,1)),color='k')\n", - "#plt.hist(f(xi),cumulative=True)\n", - "#plt.hist(xi,cumulative=True)\n", - "\n", - "mypi=sum(f(xi))/n\n", - "print(mypi)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Example 3: Determine uncertainty in failure load based on geometry uncertainty\n", - "\n", - "In this example, we know that a steel bar will break under 940 MPa tensile stress. The bar is 1 mm by 2 mm with a tolerance of 1 %. What is the range of tensile loads that can be safely applied to the beam?\n", - "\n", - "$\\sigma_{UTS}=\\frac{F_{fail}}{wh}$\n", - "\n", - "$F_{fail}=\\sigma_{UTS}wh$" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "N=10000;\n", - "r=np.random.rand(N,1);\n", - "wmean=1; # in mm\n", - "wmin=wmean-wmean*0.01;\n", - "wmax=wmean+wmean*0.01;\n", - "hmean=2; # in mm\n", - "hmin=hmean-hmean*0.01;\n", - "hmax=hmean+hmean*0.01;\n", - "\n", - "wrand=wmin+(wmax-wmin)*r;\n", - "hrand=hmin+(hmax-hmin)*r;\n", - "\n", - "uts=940; # in N/mm^2=MPa\n", - "\n", - "Ffail=uts*wrand*hrand*1e-3; # force in kN\n", - "plt.hist(Ffail,bins=20,)\n", - "plt.xlabel('failure load (kN)')\n", - "plt.ylabel('relative counts')\n", - "plt.title('Failure load is {:.2f}+/- {:.2f} kN'.format(np.mean(Ffail),np.std(Ffail)));" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Normally, the tolerance is not a maximum/minimum specification, but instead a normal distribution that describes the standard deviation, or the 68% confidence interval.\n", - "\n", - "So instead, we should generate normally distributed dimensions." - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "N=100000;\n", - "wmean=1; # in mm\n", - "wstd=wmean*0.01; # standard deviation in mm\n", - "hmean=2; # in mm\n", - "hstd=hmean*0.01; # standard deviation in mm\n", - "\n", - "\n", - "wrand=np.random.normal(wmean,wstd,size=N);\n", - "hrand=np.random.normal(hmean,hstd,size=N);\n", - "uts=940; # in N/mm^2=MPa\n", - "\n", - "Ffail=uts*wrand*hrand*1e-3; # force in kN\n", - "plt.hist(Ffail,bins=20)\n", - "#plt.xlabel('failure load (kN)')\n", - "#plt.ylabel('relative counts')\n", - "plt.title('Failure load is {:.2f}+/- {:.2f} kN'.format(np.mean(Ffail),np.std(Ffail)));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this propagation of uncertainty, the final value of failure load seems to be independent of wheher the distribution is uniformly random or normally distributed. In both cases, the failure load is $\\approx 1.9 \\pm 0.03$ kN.\n", - "\n", - "The difference is much more apparent if you look at the number of occurrences that failure will occur whether the dimensions are uniformly random or normally distributed. \n", - "\n", - "For the uniformly random case, there are approximately 500 parts out of 10,000 that will fail at 1.9 kN. \n", - "\n", - "For the normally distributed case, there are approximately 17,500 parts out of 10,000 that will fail at 1.9 kN. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Thanks" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Using data from Dart board experiment" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "Gnuplot\n", - "Produced by GNUPLOT 5.0 patchlevel 3 \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t \n", - "\t \n", - "\t\n", - "\t\n", - "\t \n", - "\t \n", - "\t\n", - "\n", - "\n", - "\n", - "\n", - "\t\n", - "\t\t\n", - "\t\n", - "\n", - "\n", - "\n", - "\n", - "\t\t\n", - "\t\t-20\n", - "\t\n", - 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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### Modified under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2020 R.C. Cooper" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Linear regression with real data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Earth temperature over time\n", - "\n", - "In this lesson, we will apply all that we've learned (and more) to analyze real data of Earth temperature over time.\n", - "\n", - "Is global temperature rising? How much? This is a question of burning importance in today's world!\n", - "\n", - "Data about global temperatures are available from several sources: NASA, the National Climatic Data Center (NCDC) and the University of East Anglia in the UK. Check out the [University Corporation for Atmospheric Research](https://www2.ucar.edu/climate/faq/how-much-has-global-temperature-risen-last-100-years) (UCAR) for an in-depth discussion.\n", - "\n", - "The [NASA Goddard Space Flight Center](http://svs.gsfc.nasa.gov/goto?3901) is one of our sources of global climate data. They produced the video below showing a color map of the changing global surface **temperature anomalies** from 1880 to 2015.\n", - "\n", - "The term [global temperature anomaly](https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php) means the difference in temperature with respect to a reference value or a long-term average. It is a very useful way of looking at the problem and in many ways better than absolute temperature. For example, a winter month may be colder than average in Washington DC, and also in Miami, but the absolute temperatures will be different in both places." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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6WNyC5xpgeS+2sLNJfprcn7TNlYeDcDugBQ6ADYDyAHIJ9LwmHDyvpU5McMuI4GZxLceN2MdbxKQdOt0TwQXD/u8gscjoXcqik/zFa+ZvZsPsZxaW2gk/FX3+VMbIDo2PjnyIzofFC5kkrXGtpADUYpzTb6WpcMCd/dMcw4kZjOS9pLjM4/0gxAR4Qyu7c472Hhtbq7YvHWRANbpY0V4WgNGwobNFcqHuWDxThtedHklHaNLFvdXXZ72j7HmcDvo4Rp8WzpJGhxbTjyaHN3cPF8FUouC4YbrimikDyR3neNeXll+FtHcAEkNbtvtzWwcTtO140uotcCHA8iCKI+Ccdn+EY7Iu5gPdx63v8LI+81SPdI4awNJbbq3BNAbr53xD0Ntqi3VLp8D0eJxtpmq8ngTXN1DS5pvcbg0aP3gj3KHyOEhvTl0W3eK8KZEHNaPtOc4klznOduXEnr/sFQeKPGkOcO6ura8ttpcdIa4tdp1WQKHmF4WfPXNxfkz12Fnu1FRyYWt6Kpdq+ImMBkIY7IffdtfdU0jW4gEeENPn1bzJpWHtnxPuGsput8sndxtvSNQY+QlzgCQNEbjsCeSpWZE9ru8leHyuaBbWljGhtOLGNJJa3Vv66W3yWxgU71Of5fM1lJ2e5Hv5v4f5I7Kx6GuaQyu+s5rnOEId5MjaQ0N9HWozJLAfBbdq8FBpO29HbkAFjxfILiQS0BsjQLFmzQvfa7dt7kg6gALryHUm728za9DXBpbY7VTCLf7+YNkve/iNJ/UvC8nYeXP/AKL10YJvcH0KyaK2/wCv61ZtDSUn0Yn3Zu6b6mjd8rsL1jDRBN+7p70ohc5mSVaEpDQF2T0IaT+dpFAe1eFljSRbTR28JBBu6Bv12SyF3mOeHsAhCFAsBCEIAEIQgBSKZzeRUnhcWI2d/wBPvUQhRlWpdzjimXrhnEmnehfsFqexJNW+p1Vs3wUPUW279vktY4OSWkb7K4cFzLpZGVi8vVCltC7osbsG99biPIkfdp2+KpPb/wCTjGzgXEd1kgeDIY0WdthK0D+kb7d/VXzFn5CiSQTQrkKvmfUfFLxlshc2qq9P5QFaiARtRIHxSNGXdjy54PWjMvqqsXh2La+Zxx2u7L5PD5O6yGUDfdytt0UgHMsfW/sO+4UIuw+1HAcfIYYZmd8yQ6e71OI1aSQRRqNwAJ1bclz38pXybT8PJnjDpsIn643fDfITAD6vTXy9hpe84XxuvJShPpL9H+B4jinBJ4+51dY/qvxKAhCFunnwQhCABCEIA2/8zSQN7W8IcdgHZt//ALXmK3/O64rDB23izZWGXGxzwfImja1rzJDB3ckkYZIdDiWscKdtutefNgm0douHv/B+mn/4blD/AFVv+cXwd3Eu00cPfQwiePhuOZZHtcWuyHOhYWQsPeznVQ8A2sai0bq3w/4fP89EOf3uX5GsflC7SjJyzNjzZDwcHGw8nLkuGfiDoYI45p8hjZCSHPjGzySRGwu3un3ysdqcbNkjbj4+NTMLhEb89oy25csuHwXEw54ZGzT9yIxPE9vgYL7lhvc2w4h2Lki4TDxp8lCfMbjtxzGWkMkjyJGTd6Xb39GcdNcpIzZvaqKomehbd+S75LHSBudnsLYvrQYjgQ+Tq2SYHdsX5PXbpzsvyG/I+9oj4pn473E6X4eI7Q0AHduRkNkcDXIhleRPkt2ZPDXuBtrWnf7RePQnYemy8hxn0gUG6aH+L/ov7nqeC8JhJq2/8l/c1xxSGhpApoFAAUAANgANgK6Ko52lpp1bnYHqegpbT4hwBxJLnEjo1vgYOX4PiPLqeqhZ+zzG76Gje+Q5+ftWNi5sEurPfV3dNI18A7cta72EAX7quktBhPdu+udgN1BrfTnv/wCit02AxvMBMciVreS0Fl838qLlFP8A3oQj8JrQfCOdnYc+h9uwUHxecxyMa3w959b1GqvPbqrFlTWoDi7aLSXNBMzdNjlGG72a28e9/wCFOYr3L3hfiMWqfd6dUSgKEA3uhVM0IvoCVxh4kkFIYGOeahN6QN6LFwQ8le+zsrmkuolp08iCRQokNqyK6ehVK4RAdle+zzKpebz5LqZmXpo2p2Tzmga9QIaNR0+IgAWdm7k7HYKQYz6aBlCOaNj4g8RztLXxySb6ixw1MmMbnNLXXpDY6qyFTcVkpc1jGgskMbZXUBphDnPm1OJ3toDdIHN7egNbP4VnN0hh+r0HQexaHo1xSGDb7z6S/Q+d8XxnOTNTdr+yxe5pBLSxxds0OuxpIN/klw9/sVPwuAGLPhgLXn6TjSuMrxs+bHkYWxh/Iy6JpT3YHJhrYbdHZuBE4aiWgeZIH61Vu0vB8V4cybXKxsZcYIBqc1102QyM8TH3yFi9LjRo19dp4vXZDmi19TycsNp9SP4B2YDaJH3J9xXBaxp26VX+itHBcSRmPCJd5hBH33If0mga+QA+tfRV/teaaVKrJd0zs6VCJr3Onjgb3cTGRRjUQyNrY2DUS40xooGyT7yq1l8YdfNZ9pJnFx9qrzY3EreqrSRlWTbZa+EcUdY3W0+w2cXVa1V2e4W5xGy2/wBieEloBKz+IShGD2N4ik5D/tlsxz9r0XuaGw6u6D1Wh8PikuT/AEmQAHMnyIXQsa7uGyY8oDZdTh4iQyN7QTt3p8rW3PlNjdM+Fmgy4zRL37NTRE4juzCHsLwS4SNDw8XXdu2sgjUvE2sbLK0nTTdOxDre8teSZL1veHVVgVuvgma63fY49dv6dz6NwiMtLZDdqYWShodzjlZLGd/DJHu12x3G5FepVC4plt7028WW0Rfm46SByZ9Vwr8oKycd4g8gBuoOvxu0htAO01TrokkbiwKdZVW4jgxglxHitxMl6XW9xLjba6k/FPYEOWOpfkevri29wS8t7ITLjDnOc4ahRrUOQHKmkbc3c0jFEAACL36iifcNj7B5JzIGuq9zQu7u/Ufx0WDRROx25b9D6E87BW2pdNDqqW1IUBQsLN3W1ctrJ8/gswbVbQ0mCELEP9DXn/G9I0G9GSCf+iTc6yKJq9+Yvy9efVZ6R5e1d0cUt9j1CxDPV3puf9efvXpdXP8AUVzR3fxPUIC8BB6hB3Z6hYtd7lkCjRxMEIWLL8/P2bGuiNBs9caF+SmOBSDZri7lWznD28iohovb4hP+G4dfVJbY2/BaT5BV2pOOmV2LZsXgjW2HW7bkNbqF1Yq+XLblsFZ4sTvNV/VcxoH+Uk+/xEfcqT2WmNBruY2vzrb47X7wth8GbdD/ANQvKZu4TMjIS1tCUOAK0V4mDnXO78VgbE0fvTLM4UHAgtDmuBBBALXNIogjkRSteNj3Np2sQaiPTWA13PYHxCvyCpD+R2kbNAvyFfq9p+KS8WUXszlmJdH2OQflh+R90OvP4ewuh+tPiNBc+LqZIQBbofyeY36ctKlfRufgjrsVp67HVe+93RHLb2rnP5wfyJvHecV4dCdrkzMSMAgitT8iBg387YB0JC9zwL0j8RqjIfXyl8fk/wC55nivDoP+LR+a/qjm9CEL2h5wEIQgC/fN9k08bw3eTMz/AOn5KkfnLy6+Kh3nhQfc6Uf6Kt/JLxKPH4pizSnTHc0RdYbRyMeXHYS5woDXK3c+qtPa/tPg/wAtfSJY3Tw40ccTXFmNnQuc2KV0rJsKYBmQ3vZhH4Xtru3OBJqmlJeBrz5v6CzT8ffly/1NWEron5rHyLnNLOO58f8A2GN5ODjPbYypWGu/kBG+M110PtOb5Ahx8iXYTB7SPGJDhyY+FicSObxDMdFAyV+LJ9KdFw9mWx/ed4deNEImBrQ3Elk+s4NHa+BwZkUbIIY2RQxRtjiijaGRxxxtDWRsY3ZrA0AUPJeP9IOJTrh4FP8AM+7+C/uzVxIR5uaRVn8M9E0yOF+ivn8mHyTHPwaB2XzqzDtgtyTN+viHXWzWnE8EC9lS+OU21s3tDDVrWnaKI7oxX73U9Pg2c62yh8YyDuq7K8kqycTxSSUwZg9SvV0TjGJvwaSIruUy4jj2NIHipwB6C2kb/k7/AKlYZYQFGZI6+f8AFJuq3rs7OKsXKxjjwBrQ2yaA3JJsgc+eyVZHe2/xKVihJUvw/h91su2W66s6oxgtIaYPDr8/iVYuHcK5fW+JT7hvDuWyseBgeixsnN+ArbaiKxsGRu0YJGhztb9DmtcOTd3h3Xr5Ke7NZbGmnSlw0tcXP07PeQNDXRtAO5FCr3FJ79CBbo0gh3hcCARVH6wOxbYGycNxfE5jIvCBydTIi5xEmsFos+OnE8yQPK1lyuVi1IxsmyW+hbOGy0AQdqsG+YPVSX8sBgO4Ja0uI1UA0C7J6KmueyBuqaS2UGNYRUVgbBkLebqbyA6FINmEocS2SOMPc0QkGBkgDdJe9oAe9h1uGl23hG3VJKtLr5GdOjxH8y0cR7WwiZ0eQ9hjjjEjKDtLJHeH+llYSY5QORFf1hUrxPiUk8YLIop45mRtGPkPfjCON4a6Rz9LNQl2G/MaW1W6oOC1kdfacAPGQ27aKaQ0DS0+oCm8Tim/NNLNnVrw2/qKz4Qn1ZsTsFkSsxIYskgzDWX07Vp1yOe1mr7QDXNbf5ISnazhxe0kb2FW+GcWG26tnD+KNc3S7cea9V6P+lDqs5b30/Y8/n8MaXRGouM9n3Fx2PPySHDey7i4eH7lumXhsL9wWrKLh0LN7avpX/UmNyb519Tzvs6W+xWuzXZgNAJH3Kz5UrYmaG863KTy+JNaNLNh5qu8R4jzXzz0i9K/HTqpfTzZuYPDdNNoZ9oMmwVqrtO0h+ou/oS8U1tM0ONNbq0tt4Mh52K1D2q3dp+IvAbosguIfp0d4GhjnW3vDp5trfzVS4wHO5vtux0uYOjtQstOx2r3LxFG0+Z+Z7bBp5VrRT+ORgggtBaOTa8vL1VSzi4girFWXknbfatYBd7R6K55rHvJrSG1z3eHXd8625cvNQj8bdzObWBoO51crDgKraqr0XoMWxRXU3lL4FTkh1GwSRtRO9k+Wn2j4rKLDeeR2J2sEny8/RTR4XRJZo7uidJH2gdTjE+/CdneAjmU8ZjNBJcSwHoPF4gXaumx2WlLJXkShel/MVqOA73fhNOIFgbHc+lghKPwSSGgG73OwoDc7c62r3q2cPx2OuvE47lopx08m7jbp19U5PDWj7JYfwq3P+YH7il5ZqTJK9tdymTYBr+DuEz9xoHTe2m7ra+Y9it+TjloN06uul7QdruwKv2eRUXJiDamkFoOnUCCWkXp9dqHuV1WRtdS3xuZ9CDldp3P/r7AvQU7yMe/s89JHSqOq9QH3JMYx6WPQj+CQmOZaLVZt/IQQlJIiK2NnaqP8H3Lx0Lqvwm/ab/3QiXiR7CBbZ9PMHny2KzHka9PL4FZRwuA+rXp+r31Sy7gn7P3LrkgivMwAXhCWGK/oPivHQPH2fh/1XNr4ktrzEi0fxv+tYsPQDbzGw/6pU47j0rrvz+FUnOPim+SHJJEfwMsDE1FWTC4aQAbFWBy3NmvZaacOgogbWeQ6n3Kz4jXOADGsc4PA1vB0NJIbtX1neLkPI8ll5N730Fsi3lW0GFw8gmtjTTtYvc+W45D4K7dmZLa0+L/ADc/iOaj8HDINmtRobXQA9vtJ96eYmM5xkgbcT3DVHI0uA0nbUADRIIurB3NLCus8TozJybemy+cBp7nEbtDWM1D6pcNTiB57ObuPM+SsePiKD7LwhjGRCgGNDRQDRTRX1RsPYFbcRo2S1UFOWjyeXY4swHCdQ5JvNwYjorPDVbL2166n0brtrUoy6mV67OLODvnafIgcFz+O8Pj/wCwSPBzsVjaGJM8138YA2xXOIsfZc7yIDebF9euJcNhyI5IJo2SwzRvimikaHxyRSNLXxvY7ZzC0kUfNfOH50HyRP7O8R0xB7uE5mqXh0zjqLQK7zEkcd+9YXDc8w5h53XqeGSthHwbntrs/iv7iVum+ZGo0IQtUpPU+7P8JmzMiDCx4zLk5M0cEEbeb5JXBjRfQWeZ5bpgutv/AGf/AMnIfLP2lyGeGEvw+Gahzmez/teU0Eb1E8QhwP8AazjmFTkXKqDk/wDWditvR0z8i/yeQcC4Zj8Lh0uewd5mThuk5OZIG99OetbNYAeTY4x0V1oBejkkpFm4mLGb5592WzlpaRkJUz4wwabTiKNM+NTCtPkkvSPwoYzXTZZjbciidoo+a11xzGu1snjjwbVI4sBuvmdctSPecNk0ig5+H6KKyog1WfiVbqrcVl5rdxpuR6KptkLxJ9B3sP6lHE2VnxCa7CaYTiDRs1W/ofP4LdrhqOxn+Xqyc4di3WytHC+H8tlHcDjBrz8gDf5vNXrhGICARyIse9YubkOPQVvuSE8HBqtlLQY9J1BjUlXR0sOVjkzNnbsSZslRIkXGk0d/SgggiNwI03TntIINkbtbVHbf2clFL4lb6jfs9LK6ITyv1ySF7mhrrYyN7yWNaGnT9TSNr+rz5krZMyX7sBoa0BrQKa0CmgDYADyTOdqslLmk2SpgooamcpXHyT5pCSNYtCs5UxnSZYcDPI6qx8N4qfNUWAlSuFIUtOGhK/HjI2HjcV25rOfOcdOl+kAkubQOvybqP1W8+XoqthyFSDXlRVkl5mNZhxTHs+aSBexoWLujW4ut1EZ+Tz3Sk7lEZrzvYoDkbuxXOum+3uUe4zRSkyG49M8kFup2zmuaNO+rT4rc4b00jb8IqEz80ObbeRH1nW1gHUl1fqUlxCUkkDysnyHIV67Hf0UHlBuoAN3BBNAbA2Nz8TXon6UtLZr1w12G0rReqnuNUDRoCyQGjl1qx5BNWt8Rprrcat2nSHBgIBo3WmjYtShSIiOoVu4P1Udg4OBaNx9WgK3/AAfVORmMPoug1xcDW4l4aQCAG0K1ULJN79Ezy+FysfHTO9ZH3pDQwl5icWhrA8g6pQ1zhvVgOu7Vtw28g4BrugsEH/Cev/VOZYGuB1sDowSfMgtsatNeh3G6hHMlGXyFrkpLuVvs82o+7ex7ZI3aHHu3APA3Y9pA38BaaO43T2dzRQ3JOwprjZAsiwOdfqKlg6NjfCWta3oBRrl9WrUVkuZbXRFrm6nPkLCCfqGr39Dt6KHP4km9MITcYpbIjirdQLRG9xrUCC1nI8gS4OB2I+Kh5cMDS5pL2tFAO3IPmNtnAWKVikfvvYcdqIoUCaq+fO90wkFEjzLnA+02QfWz+pO1WNLQ5DvtldOMRYA2snxX1NkAdB/uvG498xpIBN9K6m+Vbjmp3uwV4MUW0kAi63A5mqP3V7016wM82kQ44WX1ceoEUCTQ6USNVj3eQTiHhu/9W7QPC55rS0tsbEnVp5hWnHxwBZ9gFbk+QA3J9ikY+GkMBNkjxPDTpLtV6gD7T/5Qlp57XQUss5XtFQPBfRZs4J6K8w8KDXBgbTS0kcq1A70LsuIN/wCVPmcIHklJcQkjvrhr5vBPRJy8F9Fsr+SvRN8jhnooLiMtnFmbNZv4T6LFvD66K9ZPD/RM34PomY5zZesjZVzgGrOqtQOoHS4cwGChZsurbzU7w+B8boqb/RPlYLc63QPe0inNcacwvptg7GQUFI42EDsfbfUEbg/FO48VszHwO7xhBaHObqjcCCHMljd5amg2L5KueVzdH28xHIfwJ3EhsXVb6T1o2BX3j4hPMeJznPDRTWMIDxRt/geNIB5jfn/qqxm400ETIopCIiypMiVxnk+klrtEknfXqjL2sHXd8W1CjYOCcIAYI5pJJQWPa9hedLjLo169ADZd49rA+u/ajQRlXCK5ub8DIttnL3dFz4ZsQRyO/wAVZMWXZVvC2AFVQAAHkNh91KWx5dlRVPlZj5UeYnYMyvYnjMtpVaMyybkLZxuM209EzOliplnEzfNUz5aewuPx7heRwubS17x3mHkEajjZsYd3M4614nMIHNskg6qRbklLMyStBekNjaZS8XR8qOP8Jmw8ibCyGGLJxpnwzxnm2SJxY4XyIscx6Jgurfn4/J+GyQdpIGbTFmJxPSP7ZrKxclwA2uNhiLjt/RQDmVykveYWVHJpjZHz/fzEJw5Xoc8NwpJ5YseJpfNNKyKKMfWfLK4MYwX1LnAe9fU35NezkXCeHYfCoa7vEx2Rlwod5Nu+eY19p07pH/51wZ8zvs+MvtFjSOAMeBFNnvBFguhAigI8iMmeB9/kLvx2T6rzHpLxHwrI1L4bf9P9+Y1i0uS2T7ckLGTOYFW5M6uqj8vioaCSdvifgNyvP/8AUN0VqI7DBcizZfFfLZV7iPEOe6hcniwIsOBFkWCCLBIIsdbBHuUNncT9Vj5ObbkPc2a2Lw3XkOuK5vNVLimTzWfEM/1Vf4hlWoU1bZ6TGx+UY8WyOaqPFpeam+ISWoDOZa9BiQUTYqjogJjZSuKw3z0ivrVY62K/wlyVfiEnkpbhuASNPKyOl8jdexak7VFFl3WDLb2XjsCxRuiPIj/Tr7wrrh4+lpcCfDb9O1VdvGw32vn5qrdmsfRo1fWcAHC9tTRWoC/8INfkq8YWy8nmT9/p2MXJk2jCHVKXaXBkLSGhzSO8kc0nXR5Ni5Cxuady2tnxPg7XvY58kz2MLiIC4d0dTNBDw1uqVvWnk72pLAAaxoHUaifNz/E4/nErDJmbemwD0BIB3HQcylVY0/dEIx3/ADERNA1hYGgtbI5zC0E6ADE93hZ9VviYNx5lZ4mQ17iwOGtrWue3ewHjY+zY/BIcTzWOJgDZHOHiIH9GHBhJ2efqt1MPi2Hh5rLAYXPM7C8gOp0JY5jmM7tg0hp2eNYLw4beI0Sr/DbjuRZ4qT0iTc1N5Ik+YWvFtIcAaNdDQNHyNEbHzWQgSu9FqsId+OsRjKeZh2lo8D0XfE0deQkQcOIpPDxFKQcP9FI42D6KDnsVty0M8XH5J82FPosako6JR0Zs79shsmNVjib5Rdhn1m1R8ThvbdJ2DtgOfmVcc1iqnFZBbjbfD4QLq3k0b61yH5ynX3G8eWysZbyXb+AuG5Gk1pIpt3+Udz5poYq5f7kk9SSpTMgDWkAAEMIsDyamwx9Owst95cPfzcE7GS10Nit6GgiSsMPjbtvpd7di3/dPY4R7R6clk6Lcafrjp0o/h7bN2+5c59k52dBxBF0PLqnDohpLeQojbbnz96Tje4GtB2AJIcDzJHhHN1VvdelpR0gqwb/0PkfIpd7F3LbGzn2AfQH4hQudq7yjfd07xggECQtGnldBzDuOWtnuePywGt5/Vb0d5exR+fM17a0l92HNrnG7aQEOI20/fSapi0yfL02Nc9oquigOJRk6TbiGO1aQS0nY7eE7n2qclJotJvTVO/CaRbXe3/YqJy1oUPQ9BKSE8LIFAk+GrD+hA338nV5qZwWCRgc0mnUWkgjcG92uF8xyKgMHHcHHQSGudqc0Bt2frUS4UD578yrRwbDaAwUKcAAW3Gfq3vpO4oH7lLJcYraZVOcl0ZLcGx9QD7JNEdK50aoCxtzU5iwajVeFrhfq4U4CudXR38gmePgjTTXPa6vC7U52kgeFwa86T7CnmBOWue6Xw6SxhcG6Y3F76j076j9Zos7eI+SxpvmbaM66x9iVOEHsc3lYIvy9dt79iWjxyyvD4eRBNxg19mQDU0bfaFcuSWxndE/gcl1LXQzrZMiG5TXaw1gcWODS1rg4+IgB1MFaNwefK004ix7Hf3jdJdJQDdFAm2mt7oinH7KO1fFxA/TG1080hjaYou9M8YkBZ3oEO3dgsjJ1eu+wBcSO72JzrrvbDTR2aPCLs24GibFfXTEq+WKlroymm2TlrZEZcITB8Kc5meB4Xf1llpY3nYNBwBOzDbSL/CCafTWihIWxE1pD3MAde9NN0TtyUYwkbVdnQVggSUGK12YHt5xYrxKATu7Iki7sltUTpxX7+vqlGcSZ3ghY18z9JJ7oB0bPSSW9EZ60T0KdYeE9jtbiS+Z7TK5rnFrS13giDaoQiIvGrqdzuVZHcdt9NroU3WqXYdZDW02M6SZHta1jjQeB43jlZAja416Kax4d7TfDhI5ku3J5AUL2AA9NlKYzP4+7/RKyfkK2zHeMKF+Q/Un7CkMZqdsauIyrZdTAlFpQMXojUtFe0DCnEZSbGJZjFZFMqk0Q/b7s1FxTh+ZwuatGXjujDiAe7l+vBML+02Zsb/8AIvmJxLCkglkx5Wlk0Mr4pWHmySJxY9hrqHNI9y+qwXAfzxOzow+0WTI0AR58UOewAVTpgYpyfMnJgndf5a9t6J5b5p0P8V+z/wB+Rl5kOnMbN+YpwwR4/EuInnLPDhsPkIGGeUX6nIg/NC6Mmz/Vab+bZj/RuA4bSAHTmfJfXUyzvDCfXumRK9ZGcvNcdud2dY/g9fTp/Q9Hw/B/gx38N/UmsniPqorK4j6qKyMxMMjKWXGts2qsVIXkn0ve8EAPrU0CreNtZPnpoe5M8rN9U0nyEymlTcK99x+ulIzyslReTJaUlem0idrgkNRQ0nFpq/HtSBasmRplT0Wp6I6LBtS/DcOq2SuPCFJ4sYVF17K52dB9hQbAirabF8jz2P8AHkp7EnHLk78E7GvPyI9QonHcnRlBFEX/ABzHkVlT6vqZ1sWx1JO9jTTQ4NDi0lxaNIsgGmkgjYbJjHPqlEYb4Wt72R52tzj4BQ2Isk7+Q8knKB9kAE1qu7kA+y93M+0ppjC3lpjAc6nB+o91pZ4GtDWnd4bXQX6KyEVpi8oNFliFjn7dr2TNmFTX6I3ghx0R95GI3B2xAcWktj+0WHbms+HSU2i7Vu4A/k6iACb3Pr7E6fOAFSpOL0iEq+Yb4TpmtaHN1HxB4JjBto8GgsAFbAbjqE+wskmtcZj1ODW7h9k8r0bAb/ceSi/5RYeT2mxY3HLz9ieNlDmFuxJLNNixr1tLCR1GrSfciXV9UQlU4x2mWTHgtP4cNJcMcDX8fqU7DHsqYx2ZN9zi9DGPF9E5ZAAnBYsHGlZy6FHY5GJam+VK1o1OIaLAJJoeIgDf2kD3rKWZNJ5bBF1YIvbr1o7fFRbROEGxtnuvl/H8bqq5MLQ6TZtmTWBTbFsYXV1+tbr/AC1YJzQq75myALJNknSKVSyMtrnvjc3S9srgJvCTrAJY4hviaO7bVO6A9N1KuLe9GnR7utiWRFdpLHitrT+SD91rOANdVPcXaQSNe49oaed/6LOJpPhZ4WN8JefETQ5Ms+6z5FT1roanidBL6OWkaSAHHcEWAac620duXJOI4gP9T1J8ysIXbhpJdKAbY4gVXN+zfq7jcfhrMynloff+WvztWn3LktnFIHDxD/C/9bEx4oNnOBLXBpOptXsORBFO96dwSWXA7PGklpokMN6OW3R3vtMuLC6bvTnU6rGwa51WOW4A95XYL3iUOpE5j6FeQAs/Dek1inSnEuqjBLV30+PuHmtCuG0aMYrRlLIA3XYaI9nnkx0IsB2+23P/ACuTedrXCwbFX7b5VfNDsQu0SEjVGxzdLbaHMc2nNcboivPqnmDGHMYAx1taANQLQKBZ9YjfkeSZbUVtFcJuL0xniREEaqY09bujzG52Bq/grRwSEHS7fSG0zcjbq4tujtXPyTPHxg3xOO4HKthfkALPL9asGHHSSyLtroQtlsfwhZZMYe1wppcWOaNQsWQQL95WAcvdaz1tCUo7JHEnsA+YB+O/xT1uTVAfWPIegq3H8ncfcoLEfRLfI23/AAu3r87UPgnWJILceuqifMUCB6DeqHquOItOvZORP0tqydySTzJJsnbbmTyVek/oHSgv/wCzkGaMOJPc1/XMB59zbmODemp4G1ASPf8AqoXiBillpzA5+Ppexzq8JkDuVH8kbHyClW97T7eZVGnT6EZObqXeUvaA76pAoXTSdmtuxsmeNCC4hwBl0OZG9wbegkGRjL+qdx8G86XkOewGaNvi7qZ7XaBsyo2yBprkAx8TbHmEsyEuLXOdEWOLCKBDi4mg5hLq+o7n+SE7px7/AJDq04k1wPH7uKOP8Boa0GiQ0fVbYFGm0L9FJTkhh0gFxGkAkhtuIaLI3rfomnDH6mNd5gX8N/vS3FCBE91tGgd40v0hjXxnWxzi7YDU0c0l1lPr8SifRdCRwTbQSKNbjycNiPiCpfEYqN2W4+XSDFkaxliQwvDt3hk3dhjmhta9zvfRX7CCLqZVy0xCyzceg8gjToM2/j4LyJqXOwNCzRpu2/pvsuwiZk5bZgG9fgvdCyYKAB5gAXtzrnsK+CC9T0kQ2etC9tJmRJSzI5tHOVsVfIuXfn78ID8fhnERzinmw5D5ieMTxD3HHn/OK6QyMlaj+dPh/Sez+aAAXwGDJZ6d1Oxrz7e5klWjwTJ8LOrfxevr0JX4zlVL8Br2J/oeH4MHWPAxWH/EIGatum9p7NlqKgk0sYz8FjW7cvC0Db02SUkyosj4ljm/Nt/U9vTQoRS+A/kyk2kmTKSSxXT4fqWBkU41DChocvkTd7lgXrAuVsY6JpA8pFxWbikXK1ImjwlZMekHuSJkVnLslolYpgnkWUFXDkI+mV1UXj7OOvZbGZo80oM31VQGf6rNvEPVVPDIugthy15JKXCg7Q4EFrtzVdCAQaqx71WWZ/qneNneqg8Zx7Fc6emiZh4zI1pZ3XjYdo9VuMQ8ifA40K2P4NpxLnd4wyEHTVsa6xsCdT/UkXR/w8rUV9OAFk0ALJJ2AG5PsSeNkHQzp4G7e5RdS7paFlj9dNkjiloDarUBRc0g3tVF1bigPgE/xg4DvGySGjrewuGl7QADGOjBTefqVXAac0jbxUQCQD4XdBt5fBTGDI51t0gUbLnG2jTTmnQN3ez2qNkGupG2qPLo2XwHL1AHceh2IPUEdCrbiu2C1hw7Ka0Oe53dksa4DUWAuc0m7Dqc/Vt/kCuHCeKblhdqDa0P2t45OBDRVg0LHms/l5Xs85mUtvoWV6Z5BWDc9vmkpsgFclJNCMKpJ9RtPImUsqWndaZPVJo1wEp39FX+J4TI3vy42NbM/Q2Z9E6meGMPeAdy1tG/Jrh1U88JF7VOubi+g2kivvcXPY3xB4c9veN0eKItcS9u58OtkbfbXRKxYpBDC8uZodVamOHibWpzHU47nfbkn0uGbBYQADyN02/raaPI7eH0Xhx33esDajTPeCCXbHnz81c5ryLE35kdwyLYcvCy3Hq90rrLief2OZ806kAG52Hn7rSv0JrR4BTtqNk8jYB3+rzFepSBYS+pGNLdALT9ZjXAm93AEurSbA+yVxtSeyUZ8qGzGlrtTrGtpIaG2eYoHTvYBA28ym+dA5wJqqBLG2bLgbaX7bch4fUqVeadfPWA1tVu5upxAs19Wz/kKScx7gCAynAOskgtsXWn7XtsLvM+5OE0uhV8uLULHsI6gjmCPNQuTB4mto7nVfTw7gX53Rr8kq35WDICXAscSN2kFgscjdnoK9/oonLxi4CmltuFOdoIa4cjQdv4hXvTlVuh+F3TTI+GO9jyOx9hTzCxwwBpDtAutJNCze4b4id/1rN0Tm/YJPoRR6CievIUU6jjIouq/stH4XSz15XyROeyU5JjnDx2gnSKAptAVvzJ9eY+CkGbJpAKH6/adz96V1JOb2yjQuXrEvSBesC9cUQ5RxrqiN9NiuZ0noPeG/Be4GQ+3NdX1nFteVMJB8zbufomGA6mny7yU+8yOJ+8lOGkXq6+0106XV7Df0U2tbRW699SU75NJZAHnbd41X56Kb8aLVgJEllbtO+k0adQtu3PcKuKDkSIrGgDMud17zlr9JBAAEUcYc131XbxkHqNUfmnuHglr9nP7sAafF9RpLi+NnUCwzc7jkPRtOGyBr3UQHNLJGkfVcAXttv5ID9uYAUlDMG+Cy52ssFAuIJYZG63AeHwD6zvTqU1ZKWvy19CtJLoSMBAAaNgBQ9AkeJMEunHJphLZJd6c5kbw5rAOZBe1t9KBHVIx5Apv4TgTo5m20Ht222cavlyTOGOYvY7TGJXSSN78OcQ2NhvudJbbxXS+bHnZU1we9ld2tHvEOEh2S10UgZJDGJBqFsY+MW0vc93Lu9XI34nbjmtocNcHNa4cnNa4exwvp7VVeFcJjjgc1xc/wALpJpSSZJH93pc91ne2DTp5VsrZgCgG0Glv2AQdLLIYaHIFrf1rt9viJL4GNcuVv5kjG4bgEagBY8rur+CWuuf8BJMPxKHOUdpCGtg96R767roaO4O9A9D6okem8j1XKRbCBlLICCDRFURz59CPYmOZL9qgSPM1+ob7F3xRI4CztZ3PqareuewA9wTHJlVfMOVU7Y3kzd6JBsammx4gTv4efOvzgqr8pA7/hvEIOsvD8pg6+I479O3+KlLZr633Jv1PPatvs8tlCcWfqZIzbxxvab5U5pG/pur6JclkZryaf0NOGMpQa+RWDdD2JJ4Kk8XH1sY8faY11jl4mg7fFBwjv8A7J7xEmbasWiHc1Y6f+iln4Z8lj9E9FNWonzojNKO7UkMX0WQxfRHioOdEX3SxdCphuIfJZ/Qj5LnjJB4iK5LAmk0RVsfw/0TWfhvorYZKJRtRUZmFNZAVap+GHyTKXhh8k3XkRLo2orptehxUw/hp8kk7h58kwrolimiLlmeB4RZsbE1t196dwZBSj8MpP6OQuuUWjnQdifUQ0nnvz50RQ/19yfY+QQfEQW7DVypx6O3ry3/ACgobuLe3pQNGvwtnURydX+qmcbYAADcgNHTf/SrPuKWtSSF5Lux7jQNe4uvV9UtI0nTXVprnYU7w2PT52dyTzJ9f9lGcOg02SdRc7UTQb0AoAdNuqlYjSzL5b6IWaJLDLGfZF1VnxHSfsgu3Da6JPinFpY5IpGGPumBzXM0PfNI+QhoYxzNo2cjZ2sC+Sjp8qlE5fFa6quqpt77lEsRTLpB2oGvun6o5CLaHUWyC6PdvBp25G2x3Gym8bid9Vp/+VWucCQ0kAtBIBoOrUBfnQ+CtHA8sbFlA9RyaR7uR9Vy/E5VtELcHS6mycebUlZI1GcAl1gH2gjnuDR39oKn+7We1oxrfclojSxYmNSToVg6BcOK0jHRJJ0aknxJvKxBdGzYyc1MZse3s1Hwtje52w0ucXsOrxfVA0//AMiknBN8uIPaWnqDXvFfCjXvKshLTLO6GUEUfjLA12px7tzSHU6RhLgHE+HfUaHQqREW1KOwp3nS+QUDpLaa1rWueNPiPeFx5kf5lKQzNJLQQSOYBFj2jmFK3eziekM86Gmud5A1/i5NH51D3qMkxgWsIa7RYcXHSbG5BOl1nxUb9qseZEHMcKB2sWNQtviHh+1uOSxMLQ0Bv1Q0Addq25LkZaR2Nz2VXPiDW6ugLCKrxeNtBt7FxOwHqFgyCtyPERv1r8keikcqMiZrAPAWSSHnQeCxnhNVye7b1tYSMVm9JDtc+YaUsHFKyiv489v9UgQd7N7mtqodBz39vqhFyZg5yRdIs5U3kKsiixIzidV8uZO23M/rSzHpo0paNdkjuh2wpZoSMSdxNVLKpEeIA2RzXBro5WjYtsE6twSdqDnNNf8AvHeSQ4NiOx55onTve2YRux2SOD3M0NeH1TKa3SIx4uZaTzO82cdrq1C6Nj0NFtivyXOH+YptnRNc6OCaLvWu1PDqa9vhaRRBOrvBr6DzPsvhbtOPxXX8hKxJPZjgYjXukcRI13ePafE6PUNVteKOoCtIv8jyUiYhs0A/0Le96fW8QjBLtzZEl15HfdIiOAbu0sDWsGsu7qmbtDS8uBFeR9FIY0DS1zmObI5+kAmQE6oxqjaHN5nVR381Btt76lNk1rR7w2YiQQzuyAytZfp1xukiMLnxudGzVGdUgOkGqaeSefSsiF+ROyMyCXLx2MB1ASY3csPeQu0+Kbu9exuy0C72WXZeGZspf4for4Iw0B39I2Ul8kmttVsXEWPwgpI8MhY502oskDQ50kkr3aI2B1Gnv0tj875hptXKcIvTXkYlqbexQ9pYQQ29XjYyQwnvWwmVpfH3tDXGTy0uF2QpBuW1wsG6qxR1NJ28TK1N3vmq1wyHTLN3MT4pmRRSd1OG9zcrZI2QxzwfV0shDdtQAkFDc3Ixzxz6ZBRHdk213iaXGnN1xnZwLXAgHoVVdCK7HK4t9x4/NZYaTTy0uEZ/rC1vMiP6x5dFi6SwCNwRYPoVGzZUsZMel0xNmN3h1Bm39YAbdTtrA5aeZtMcWAwmXI0NijeWudAwABoDrkyCQB4vE55Fb15lVeGmv2+ZfFaJadyjctyemQOAc0hzTfiBsbGq/X8E0yWqjWmPU6ILiA1At8//AFtQ2USQfPe1YsiFRfEotLJHn7Eb3b8qa0u39NldW/I1K5pIT+T/AB+/4bw/IreXh+I93+J2Owu3rfxWph3CfRQ/zUsn6X2dwXEguxzkYslcwYch7o22f/cPi+IW0Dwokkg7VWktFAjfUHDc7ED3dE3xCiVeTZD4Sf7mHRxT3I7fkigS8L9Eg/hnp/H8fqWxJeFeibS8K9EjuSHIcSTKCeHeiBw/0V0l4cBd0ABZJ5AeaaZWIWiw2xV6uYAsXsNz4b5Lqk2XevoqruHHYjmOm1EVyPlvW/onTOH+inseFrvqkHZpOnetXK65cjsU7Zhei5KT7M48wrjeGei8fwj0VviwgnDeHjyXE2UPiGjX8vBvRNJOCei2WeGDyST+FDyUlOSJx4oawk4J6JtLwX0W0ZOEjyTSfhA8lNXyQxDiaZqvI4P6KPyOGV0W0cvhPoobM4X6K+GY0PVZil5mupcDy59Pb0+9LQQWARsbtpI5EdD9495VnyeHUmJxNBN/VO9gbNPXV6db9qaWTzIZdqY3xnnkW+K+QvTXnrI5J02S2313seoNH7wvRD4m/wCB9fFiMjH6jwu8x12rxDk4e1VNple+pHZ8irPE3ndWfIbqFj3+hq69u6g+IYtp7GaT6jdTRWWSu1e9Xbs7OQBzPKh5k7AfEqtxYXi5K2cCxQHs9Gu9nNm9cr/3KYzJxcSeRJcpsvsmaAv2n2k2fdZVvjCqXZ1tUrRBIvLyfU8hmr3+g60LF0ayY5ZErpnbaY0kjTOeNSciavF/D2c/Q7hRaL65sipY01kClpI1G8Q8ILjyAJPuFoQ9XPZFNqnNP4br6c3F4quWzh8F61zWFrgNm6gSAXOAcHE78yNX602fbQAedeI+bvtE++00knBLWkmiTdOc29jtbTfnt6K5LqOeFuJYIc5rw0Ndu4jbdrwOvhPiaduq8yZ+7LWlw7oj7X1m7gNGq/EN+u+x3UdJIxzQHC9wG0AXX5NtevyyS2S28w0M3D2up5DTqF2dWmq/BXYxQrKtxZ7lTNL43N8V64w5v1AXN13q5H+prbzSUhvdpB3IPtFtIvodQ+4pzmG3RjmdZd7Ghjml3xc0f5kyzjRcyMHvn7aw0lsZP9o9xGiwDqo7mghLehiEuUHNSL41ngQta0saCGNe5oJ+0RWt3qe817+dpZ7Vx9HoYhLZGysTWRik5Y01kYpxkXxkMgz+P9EvEF6Y1mwKbeybYvCnkaZRlOI3qplMh7EUuADsQCPUAj4FMmPSrZVWUThsejFjIojw/ggnRYNg6L03YBSEvB3GnMyspr2f1fjjoCgCyzFu00NzvsN14ydLxZKlGyUewrZjqQvFgPLgO8yG7AySGZwZz+o2KEhryQHW94vdvXk9zTiwxtEx8HQO7yTV3REu0bBvRYHUAoTJ4w4ytxccsM9h073jUzHgG5c5rXAvkJLWhgPUk7BSOPrkNzsYWx33e4cHOJe0ymMimHu9Nbn+skCtbl0cn+XmZ8qVvoSTuKwyBgbI13fMcWNDiHuaB4yGjxNIuj5H1UPxDFaNGOwvEUx0yM1ybMja5xIk16mgktaRv9YeqR4qQMmKZ3hDIpWh2lha50mm9cn1o3AMAA5HvJElkT6i57C0uaxmhzt2WD3h3abojR9y4vdacX/9Jwo2upJ5bXs3gYw6W+CO9FUP6toJ092Rp8NiiwHdK8FneWhsoc2XQ1zmOb9UuFuAkaS17dRI9K5nmkcPJ1Bp5agDXOrF1fvUnE7ZVOzppr8zs6uVkHPwowEHGc6zI6R0DnanSs1tL2NL3AFovYvsjWa8lKTN3Da2de97WK28y7TqP+Up6E2zJdL4GeKpXltjTpvbwODjZ21O2/uyu8zs0mV8ygInFvooD5R29xwviORW8XDst7enibjyFu9beKlfYcRaw+djxSPE7PZrNbGz5XcY0TC4B7xJkRul0tvUf6BkqZ4bju3JrjrvJfuVZGbywf4FN/8AZ2cTa+HinDzQdFPBljay+OeN0D632p+PDv8A+8XWGO1pdKCBTHtaOXWNjze/5XIr55fMq7SjB7S4rHENi4hFNw95JoB0zRNAB5uOVBAyvy19D5XL61PhlFs3OUVtnk1a4rR6cVhHr6cr/wBAkJ+GitiPehkydRSJLJ9Hcaa6LROGTIhpuHnqK/j0TF2CASaO/Pc1ttsCab7lbXNBHJRWbDXL77PReL4lwf1fquw/VktlakwAKoaa6CwNj6fxuUMiA25bnbzN3tYslSU4TKR3T+B/1XnpLRowk2KxMCdRtCj2zpePJRCSRGcJEi2MIMITePJSrchNKUBZxkD4Am8uOE575YueoyUWdUpIicrEChs3BCtMrVH5UKUnDQ/Re0UrMwVGTYSueVjqMycVRUmjaqyehTJcTQ8EXpO1dBrPi26eIR/FGRFQ5eQ8uZA/1U1xLGona/AduX2m73Vg7j4JjJw9o/xCvF+UN9VctV/FMqaaTY3C3fYhJMcl5sAeAEUSQfEfTmNvzgmeThX0U9kQWdJFOo6Hj0q9rsb1t7EgwBw6WCWmgRu01yPx94V0bWuqGa7ddCuR8P3U1wnH8d/ggN9LPiP3aViMcyUWudGy+bWjW+j07xpDWc+l8qIUxgY4aOp3uzzPqf46KV1u11Z2y1yJ3hLqU3DOoDENJ9HKs1mTdDmZOR5CVE6h2TJUTLgnKkkjKvNVpi2VLRvQQdehfSo3isQLXNPIgg+w7f6qTYUz4kNvchHan7xSMyBlfVaCNiAKAI2NAexQ8zLcW6ngANIpx527ezv0CnuIt8Tx/hNe0V/9v3KuvkAc49dZbsCSabfTeqv709Vtm7Uk49ST4a/SdJLidywucXHTsSLcbu6/8qkBOdVAXqcyMEjwtcGvk1HqeYHtIULit1kOcPCPqDkb5a9t/YFI8PkD2hrfEGybu5gFj9VEk2XVXxXJLrsrtiS8MQF1zO7nH6zj5uPVJREAOPQOcR6gc/8AzAj3BLNKbsOzWUdqDjRAAZuKJ2O4by8yl11KTOKKgB7z/iJt33krDUDyst0tcHfZOq9h1vb7wldSxe5c2WJMbvamsrE7eUhKpIuixo4LAlKSlNZXq1LZchXvFk2RMHSr1sqlyEuUk2ypQTKPjelwVBxIOKHEuWGguJoAWT6LGDOLvqtd/ie0sF7j6rvEfh1CjnTMfKY7BfC1ry38EyWGurldA/Ep1G5ScEl1RDl2THDy1vIC3VqdQBcR1dXM8/ipFuQoTHcnJk2VElti8qkK584ILSAQQQQeRBFEV1CYxTgbDl7bPxO5SOVImzX7qcY9C6FS0SfBMs39HcD3kTI7dsGSMNtD2Vy3Ybb09easuPLt7OSpVBkjMirIb3LgLJc2R40AC62kI5/hlWfElXLl5oStr8iYfPpa59F2lpdpbWpwaLIbZrV/0TvFlbLXduDi10Um+oDQ4/W/KGjvK9W+YTHFepjhGM1gpjQ0Ek0Lq3GzQ6CyTQ8yoQa/MyclaRMYkNrln5/07ceHh2C0kuysrJzpBZpjYWNhiGm63flZRv8AJA6LrThsXJfP3563aUZvaXKjaQ6Lh0UPD4yDduhaZpwR0cMnInZX5C9v6MYe7PEfkefy7PI05w3NkgliyIXGOaCVk0Mja1MlieHseL6hzQfcvqV8n/aqLivDsPikNd3l47JS0G+7l3ZPCa21NmZIz/IvlYurvmGfKLofP2byH+GYvzOGajyma3/teM23dY2NmDQP7Kc8yvf1vqZsux1+nmKmAen2I5W29iEe48JoWonOnT/PdTVXcyVfMvSHMfiOBr4texDLlUTk5KyzplBZuQvHyltnoMbHJB2Z6r1ud6qszZaTGb6o5WaXqey4x5/ql2Z/qqWzNPmnEed6o00VTwS5x5n8fcnMeTap0GcpLGzF3mYpZh6LKJLWEoTDGyE8DrUubYk6+VjSZiY5UalXhM8ph/j/ANPJQaGKplY4tGNQBBpw07XydLGHDb0r702yGbqQ4w0ij1aHu/NAd8LAHvKZ5A3Un2RqUSIbizWgazqsbN0l4PiI2pm5bsD/AJVHySMiZq1W3w+IeLUXENbWnpdDb0UrxWwA4Au0uBIFXW4NWauioXKiDnB2k6Wu10QQO8AIa4DkdiTf+FMVdV1Hod+gvit0gN62T5/WcXUPyRde4KSxyodkm6ksR6jYi6cdIlYSnUaaY6fRNS7EZ9BRiVavYo0qAoi7Z41OYQk2BO4GWgonI9akczknehN8lmyCmDWyn8bidYc2rJDXagaLd63G4Nn/AMygji0bIbrJNkepurqyOQ9wVt4vAS12n61bDYXW9Wdhyq1DTRuPJhA/KLWn4Nv70zXN6NmixEbGSHadNtDbcaPry6c9q57qV4fDpHKiSXEXe59Tz5c1hj4xsF1bcmiyAaqySNzzUhHGuTn5InOW+pkOSTc5KlqReFUiuOjEuWLnLErwrvcsSPHFN5Sl3BIytUkTQznKj53p/O1R87UxAZiN3OWcZWOlLwRFXNpIsbHEATprV7jQp4yBKyl1F5TI4R+NxoWWs3rfYvG56paONLtZ4j4XaiaAo6dLSaOs+HqT57paLHeTRDWj8JrtZI9jmUESZV4qMYWLKQpU4Q6jV/iJd9x2UVkTGN5g8dvkZ3bn+INjcGh2lzj4gCHc+Woc1yEefsVuxLuZypJjU9kiSUjCOQt5+oDYBNcyQNm+q6mMc6SBrLLW77ODzRrZvIGumrp6FTeIeSj8TGrmdRPM8rPoOjfRS2FCq5yFbZIlcAclZeGs5KI4Zj8lYODU9z2C9UbgHAgjmLDmnk5vMWOrXKWPU5y6I89m2pCfbftPFwjhmZxaau7w8Z8oaSB3kxpkEIv7Tp3xM/zhfLDiebJPLLkyuMk08sk00h+s+WV5e95rqXOJ966s+f58ooc/H7MY7/DAWZnFNJ2Mz2XiYriHb1G90xaRX9LjnmFyUvrHBsV0Y633Z5i6XNIE+4DxWbEyIczHeYsjGmZNDI3m2SNwe01yIscj6pihaxUfS75Je38PGuHQcSi0tc8aMqAO1HHy2Ad9CetWWuBPNsjD1V6wcpfOX5vHynv4FnXIXO4ZlaY86IDUW1fd5UbefeNLjsOYc8c6rvHhHGGyhskZEkEkccsOQxzXwzRyN1NdG5p3FEG/yhS0atWx+YtPcGX941sr0Vb4jERafcK4hyFp9n4okGpvOl4L0o4JKX8WC/E1cLJSKFntKgM9pV24jheir+dg+i+byi4vTPWYl8SnZVppan8zBPko2TDKsjJG1Xamho1xS8bys2YqcR4h8l1yRKUkELypHFlKbx4hTyDHKrbFbJRZK4cql8Z9qFxY1MYYUUZOQkOw1EsFhOYI7UjDhWE3Vjys6JGZK7lKDx3GqyboxytroTp7y69kZHvTPJxSpb5WR9GwZ8oukibjhsr5Imue9kbXASODWm3DQ51+lrLh0bZYmyNJLdLfE5rmFw0Nc15DwCLY5rv8y5ZjThBNrzaHsfNWyq5Ebi5zAy9LWu1lwDSH6ug8XNh6KKysMtAHkK/gdFfOC8I/ozuHBz36CGNZUbXFrG+H61AfW62kOI8F57KttxNLHzo76muTCbT7DaVM5PCCDyXkGBXRErNmk8iLRlhsUrBEscPE9FKwYypb2Zl1yEYYkoYN0+igShh3RoRd3UZQ4/v/AF+0p9i46XhhT/FgVtdXMxW2/oRz4E0yIlPTQphkRLtlTiQqu2VnLhUbNjqyZEKZSQKnsa1VxCtx0uyFSH0f0Sjcdc2WyvIp8SbSxKdfjJtLjLuzsLiEdEsO6Us7GXgxvRd5i9XEV3STkhU19FWD8X0RzEleV2WBM5cdWaTD9E3fheisVmi+N6K63FT3Gw/RS8XD/RSeJw30RK0jZlJIicbD9E8ZieinYeH+iXGF6KttiE8xEA3D9Eq3E9FOtwvRZHE9FzqUvLK+/F9EwzcKwaoHbci9g4O0nrp2r3q0y4yZy4vohNplkMhMp8jn6mmo+7c8xF1uLmzatLRprxCwdvfdJ7Dgm7O5O3KgAOgF+/4KQ4dwdwdrkEWsDbuxZLiKfI+QtBc6y7YAUHHmpWLC9FbZJdok1k9NsisfEUzw/B9E6gwwAXOIa1oJc5xAa1oFkknYChd+im+G4hNFrTR3BIokDqGEg16urmFPHxrL5KMUIZWckjDBxQ0E1ZDdWkburlYbzIVa+VDtPi9m8PN447T3sojayIu2zeIBj2Y+PDbthoDS54H1cZx3PK45sWLDH9NyhDF9GidM+aZzaxmNYXyvfNyoNBs8tjWy+enzm/lbf2h4hqiL28Kw9UXDoXDTqBI7zLkb0leWjY8mtYOd39J4PwCONHns7s8xkZLsZrTtBxefMyJ83JkMuTkzSTzyO5vllcXuNcgLPIeiYIQvSCgIQhAAuhPmtfK/9Eezgee+sKV5GFkvNDFmeb7iQk7YzncnfZc7yJLefF4rK7HCW0RlFSWmfULFnLSrLwriHQrjz5svy4B3dcE4rLTvDHgZ8jtj0Zi5Lz15Bsh9AehXU8JLStJ8l0BVbrZbp8dsgsbFQmdwyuiXwM4jZS8U7XiivD8Y9F4Wtzr6M1cbOcSjZfDPRRU/DPRbIyOHtdyUVlcNrovAZfCb8d6kjdo4l8yjt4b6fcnMXDfRWQ4PolosL0SCpkxmXECus4b6fclRgeitDMA+Sxkwq6K54ViW9C7ztldjxqT7HjpO5MelixiqVbTOSu5kP8CK1MtAaFD4D6KmH7he09GoVuXXuZGS2Q3bDCGTiZMHdNmkdA8xQvOmOWVjdcUUhqu6MjWNIO1E2qv8j2bHxLhkGVpZHqjbDPjs1aYJ4I2wT4p1tDw9rmaDYG4KuMtgrVfaDgmdwnOm4twwSZHD817peKcMjY2R8GQ5sbZM/EhbH3kttia4wtJNh1A6tvWZXBMe7q0JwyJRNpcMx2BpeXB7S492bBuPkCSBR3BPsI3WUuNDJsKB+5a+7KfKDjZUDR38fftAa+BwdDksOm9EuJIO9ieNLxRH2LtN8UPZluyxlZRZIL+iOkBxLLGtDmxllt+qXVfN5Ktr9GMSdfLykJZ04y3sumfwDqACPMKLfweuimeE8ZJq1Nt0PG4XlOJehjre6n+TNKji0mupT4uHV0TuPDU7mxsjbrLXltgHu2OlLQftFjBqLfUJY4YWC+AXQ7oYlm8xAtx16ca1KyQUV42NKvBafKznjsZwYaf42NpTmGFLaFtYnCdLm0Lzu2RmWxReQ1T2TDfwUZkY6y+IYsoy7FtM0Qk7E1dEps4trEcPJ6FZHgTfZGhHISRDCFKNg+7p03/1/wB1MfyY7yKVZwt/krI4Vz7Rf0OPKj8SEMCQlxQd/wDUjzG/nzVnbwl38UvHcIcrvZeTrfI/oRWZFeZVDio+iKzHhTvJZx8Id5V9yrjw69vSg/oWevRS7lN4O/v4mTd1NDrv+iyGGKZulxaQ5h5bg79diE6diK1t4Qb3LQKFb73vdiuXL717/JHq1NPguU+qrZBcRivMp7sJYjh/p9yuP8jerVkzhA6uauR4HmN/9t/Ql7TivMq2Nw30UnjcP9FYIsBg5utOmRsHJaWP6MZMusloUt4lvsV8YPovfoforFoajuWpiXo1Yhf1wr30X0WD8ZWPuWrF2K0qqfo9al0R1ZRV5cVIHCVpdghYnh4SU+B3r/iXRzNeZWW4ScQYXop1mAP4+CX+isAIIDgQQQRYIOxBB5jmrsb0fuslprRyeb0IiXgDJo9EtiMujedJpxMbxI2nVbTqa06hvttSkJHNjFNHoSSXONcrc46jzSk0xquvoK+AvZce/Ov+cF/XcC4RNZ8UXEeIxO/yvxMV7T7Q6UeoHUr6BwvhFWHDb7mVdfKxlf8Anf8Ay4HOkk4Bw+S8CJ4GflMdYzJmEHuI3A0cRrxu77Tm/ggF3M6ELTlLbKkgQhCidBCEIAEIQgAXTPzb/nCfR+64RxmQuxfqYvE3kufj2QGQ5ZO78bmBLzbsDbd28zIU4TcXtHHFPufVeFgIa9pDmOAc17SHNcxwtrmuGxaQQbCfY0hC4I+bv8vuTwNzcHL7zM4K539TeqfCJPikxC813fUwHY7kUSb7n7J8fxOI40efgzx5OLKLZLGTseZjkYRqilF0WOAI6ptWqaKeTlLJjzJ1seajIn0ncUwSGVixtWmi6E2jybEHREEAThsoXuoLzFnA+WfNFDau2jIABeFoOyxfKPNNJs4N6rXq4epw5XEplbrzDLxBzCjZISCn0fFmcjSV/o38jS89xL0Zt3zVoYqy0RsMZtTOIDW68ja1u9gptmcRa3qruDcBtrlzy6Eb8iLFshoTUpuOIg9U4ikBXtlW4LqIcyZGZ2AxzhIWMMg+rIWt1jYjZ9WNiR71DZnDN7AVy7kFR/HZY8eF88nJjSQANb3uALgyNgNyPoHwjfYq2vJUCEquYhuHYpBVkwjQ32A5k7AKtcFOVJJGDjudGXl00uQ5kRxy0OaI2Y8Td3fWF26jz9LmyAVRAIqiDVV7FTkZUWiddTRkCvda9DABQAAGwA2AA6AeSa5UlJCtRtL22hYEEkHltv6nmB6cviV7paFCvzwDzSUnEh5rr4NVKXM4kPWdE5JkALGPIBVZm4j6rPF4gPNPxwVGOkirx+pamvBWMkIKioMxORlrNyeEwt6NF8L9DpsLW81hLlNb5KI4hxOlWOJ8bPmr8LgVUO0Su3LLrJxVo6pnPx5o6rW+b2grqoHO7SHzW3XwqHwEpZrNq5Xado6pBnaxvmtJ5vaQ39b76TU9oXeadXDIa7C7zXs307tg0DmozO7ctGwd960dkdo3fhfeoybjrj1KlDhdS66IyzZM3i/tuPwvvXje235X3rRP8snzSkfFz5/er/Ua/gV+tTN7N7aflfelou1t9fvWjYeLmwC6ieQJolTGDxEmjex6qLwofA6sqRuaHtJfVPIu0I81qTG4kfO/enA4ufNUSw4stWSzbcXaAeaew8cb5rTkfGSNyeXM+Xv6LN3aA1YdsRt7FVLh8WWLLZuKXtDGOoTGXtU3kCtPS8cc40CnOBkPceZQuG1x7g8yT7G3cbtAHdVK43EgVrrg0TjXNW7huOa3Sd+NWhiq2TLE3JBSWTkta1z3Oa1jWlz3OIa1rWi3Oc47NbQuyoPtT2hxOG40mfnTx42LCLfLIeZrZkbGjXLKa2YwEnouFfnGfOByuOudg4ne4XBWu/qdWmfNLT4ZMwsNd31EA2GxNkAjNcIw7DSbZdfnO/OSOT3vB+CSOZibx5fFGEskyaJD4cQjdmLyBl5u3Apu7uWUIUG9ktAhCFwAQhCABCEIAEIQgAQhCABXH5K/lJ4jwLI+lYE2kOoT4suqTEyWjkJoQ4WR0e0hws0RZVPQu7A+jvyJfLdwztAxsUbvonFA25eGzOGskC3OxZaAyotidvEOoHM7TYvklBM5jmyMc5j2Oa9j2Etex7Tqa5rhu1wIBseS6X+Rb51uViaMPjbH5+KKa3PjoZ8Qsi5gToy21W+ztjZcVbGz4kHE7baUPVd7D9tOH8Wh+k8Py4cuKhr7t1SxE7hs0DwJYXejwFPkqejg1yXFQnEJ3bqenIAJcQB1JNAe0lVrtNxjFxw0ySDxkNaGeOySBvp2aN+vkUzVJLuUzi32IXN4g5vmkcbtK5polP58YytD42sdE5mpkmvUHWBpruwQW7ne+igeIcBncbaWt23G7m3fMAssfHoFpQlXJdRSSmuxZMftTYolNc3jd9VV28Iym8yw+uh3kR0f50fcUscSaiHBvSiNTem5og17N1JVVrqjjnPzJaHjJBq//VWLhPFrrdazxuyo7wvIIsjTpcWuiLeRaWt3PPn5q3cF4O4EHvJjXK3mveK3681CyMWiUJPZsXEzPCSAXkCw1unU70GsgX7Sofikjcp7HxvI7h12x4fEInFrXyyxiMlszTuBsQWNPK0rh4j9Nd69o6uAYHVpI2dp287PklMbAhc6zkue4gttphY42HA6pImBxJDj186WDk1rfQ0a5dCQ4Y1mPphMcUbZHeCWFumOWQgDxg7tlNDmTe26lbURxKJpjOP3T5AQPDboxd6g5sxI0uDvFbNxQS7HuDWhx1ODWhzqrU4AW6um+/vSSw1J72W+JoeSyKE4nk80vPOVD8QcStTGx1AXts2QPE80glRjuKHknnE2i6J8XPSPE+rAvSN63G6jXYLz0LB6gF366HTna2YKOjPlvYu3PurcBfKzV/H3fFOsTiEYIHeNtxpovqBfu2I5+YTSDh9EEgkjkT6iia5XRPxKk8WGhVbcq6V5V5Ino7ElsXMA/CPq1rnD4tFJ4c0eT/8Aw5P/AMaTLGaU9bGUlNIYiyM4jkA9H/8Ahyf/AIqrcVBN0159wb9zyCrxNikqLz+Gk9FfTYkV2Q2as4prG2nfzBGke3e/Pl5KtcQDt/ER/hA/+61tHivBSb2VY4hwA+S06rExCyDNdSM08rPmXEuJoVuXbpkWuGwLq6C7r3uFq9zdnHHok29lnn7KY54lXLIoNvJI3I5D8InqdhQHt9VkMZ59PvP+wWxYeyDz9lSGN2Kcfs/co+JFeZ3w5PyNXs4c419bb1I/+XmPan2Pwdx6E+2yttYPYUn7Kn8DsMBzb9yqllVxLI482acweAnamddvD5qWxuCOG+j7tj6kciVuSLso1vRE3Z4DkFQ86D7FqxZI1FJw5w3og+Ytt8hvpO/IJBuBW1Hc3zdd3zsm9Xqtqz9nSeib/wDCpPRSWTA54EjWhg/JB9SASfaTzWH8nPds0Ob/AISWj20NltaDskOoUhD2ajZRdQ6DbcmiaAG5NAmh5FQlmQR1Y0mas4VwGWgPrHzddnfqb8lb+BcODb7xroy00S5rtFXs4SadJb6q58O4VdlrY2sBpryRKTXO2xnS3f1Tbtl2i4bwmE5HEsyLHhI8LJS3VKWiy2HHib3uQ78kApK7P+AzXi67kjwnhbWbjezq8xyA29wVG+Wr5beGdnmGKRwy+KFtxcNhcA8Ei2vypQCMWLcHxW49Aea57+Wb51eVlB+HwRjsDFNsdnyAfT5W7C4Wg6MRtXuLduKLeS5pnmc9zpHuc973F73vJc973G3Oc47ucSSbPmsq29yHoVpFu+VX5SuJceyfpWfNqDLGPixao8TGaebYYS40fN7iXGhZNBU1CEsWAhCEACEIQAIQhAAhCEACEIQAIQhAAhCEACEIQBI9nuN5OFMzKxMibFyYz4JoJHRSDcEjUw7t23adiujvkx+d3mwaIOMYzc+IUDmYwZj5rRv4nw7Y+Qfqiho67lcwrxdUmjmj6c9g/lV4JxlrRh8QhM7t/okj/omaDVFv0eUh0o3+xqHJXODHjYK1eE8mksDR6BoFV1XyVa4gggkEGwRsQRuCD5raXYT5wXaLhgayPPflQNFDHzx9NjroGySHv2NFcmOAU/EOcp9G5ICTTTGRX1nN1uv1awgV7FmcOIC3Fg8yaYL9jjYXDJ+eFx7rh8EPtxs0/wD+9Dfnh8eH/cuB/ouZ+/rjsl5M7yo7l+gQHkWH2Fp39x5pOTg8B8lxF/PH4/8AifBP0bN/f1i/54vHjzw+Cfo+b+/pK63NX/bkvzJKFfmjtn+QoehanEHCWt5ELho/O+47+J8G/R839/QPnf8AHfxTg/8A4Gb+/KpZXEuzUPqzvh0+R3nFjAJOfhzXHUDodYNhsbgSDqvTI0gG+oXCbfnh8fH/AHXg/wD4Gb+/LIfPH4/+KcG/R839/XHLLl3S+pJKCO58rh7XeINYXi/q64rs8nFjvFy5FN54cg7U1t14mgEgeVOfRK4h/nk8f/FOC/o+b+/r3+eV2g/FOC/o+b+/q2h5Ef5tEZcrO0ZsBzrtpdYILn02gQAQwsfbBt0TPG4VIzULBaTbWl73BnoC9uoj3rjo/PK7QfinBf0fO/f1gfnjcf8AxPgn6Pnfv60o5El5FTrTOxXcFPRxb4tRDQdJ3sinO9vxXruF+i44PzxeP/inBf0fO/f15/PD49+KcF/R839/Vyy2iDpR2OOGeiWj4f6LjH+eFx78U4L+j5v7+vR88Pj34nwX9Hzv39Dy2c8FHbEOGnkWIuHR88bj/wCKcF/R839/WX88jtB+KcF/R879/VMr2yxVpHcww147h4K4bHzyu0H4pwX9Hzv39e/zy+0H4pwX9Hzv39UO63yJckTtmbggd0TSTsu0+S4w/nldoPxTgv6Pnfv6P55PaD8U4L+j537+uet5S7JfU46a2dmN7JM9EvH2WiHQLiz+eV2g/FOC/o+d+/r3+eX2g/FOC/o+b+/rvrmW/gHg1/A7bj7PxjoE4j4RGOgXDf8APL7QfinBf0fO/f0fzy+0H4pwX9Hzv39Rd2Q+7JckF5HdrMNg6BZmJoXBx+eT2g/FOC/o+b+/rE/PH7QfivBv0fN/f1xKf/KR3p5I7vewJB8QXCp+eJx/8V4N+j5v7+vP54XH/wAV4N+j5v7+r4y15kWjuZ0DViYguGv54PH/AMV4N+j5v7+j+eDx78U4N+j5v7+rPFI8p2w2Z2tzXMkYzbQWt16x1JdHeg3Y0+gN9BTflE+UvgPChfEMyD6RGPDiNJys2yCK+itJfHe+8lDnuuIu3XzgO0XEg5kme/FgcKOPgD6FHXVrpIz372+j3ELVziSSSbJNkncknqfVRdoKJ058pnzucyfXBwbGbgQmwMzJDMjNI28TId8fHdzFHX03C5y7Q8byc2Z+VlzzZWTJ9eaeR0sh3JDdTzs3fZo2HRRyFU5Nk9AhCFwAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCABCEIAEIQgAQhCAP/2Q==\n", - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('gGOzHVUQCw0')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How would we go about understanding the _trends_ from the data on global temperature?\n", - "\n", - "The first step in analyzing unknown data is to generate some simple plots using **Matplotlib**. We are going to look at the temperature-anomaly history, contained in a file, and make our first plot to explore this data. \n", - "\n", - "We are going to smooth the data and then we'll fit a line to it to find a trend, plotting along the way to see how it all looks.\n", - "\n", - "Let's get started!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Read a data file\n", - "\n", - "We took the data from the [NOAA](https://www.ncdc.noaa.gov/cag/) (National Oceanic and Atmospheric Administration) webpage. Feel free to play around with the webpage and analyze data on your own, but for now, let's make sure we're working with the same dataset.\n", - "\n", - "\n", - "We have a file named `land_global_temperature_anomaly-1880-2016.csv` in our `data` folder. This file contains the year on the first column, and averages of land temperature anomaly listed sequentially on the second column, from the year 1880 to 2016. We will load the file, then make an initial plot to see what it looks like.\n", - "\n", - "\n", - "##### Note:\n", - "\n", - "If you downloaded this notebook alone, rather than the full collection for this course, you may not have the data file on the location we assume below. In that case, you can download the data if you add a code cell, and execute the following code in it:\n", - "\n", - "```Python\n", - "from urllib.request import urlretrieve\n", - "URL = 'http://go.gwu.edu/engcomp1data5?accessType=DOWNLOAD'\n", - "urlretrieve(URL, 'land_global_temperature_anomaly-1880-2016.csv')\n", - "```\n", - "The data file will be downloaded to your working directory, and you will then need to remove the path information, i.e., the string `'../../data/'`, from the definition of the variable `fname` below.\n", - "\n", - "Let's start by importing NumPy." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To load our data from the file, we'll use the function [`numpy.loadtxt()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html), which lets us immediately save the data into NumPy arrays. (We encourage you to read the documentation for details on how the function works.) Here, we'll save the data into the arrays `year` and `temp_anomaly`. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "fname = '../data/land_global_temperature_anomaly-1880-2016.csv'\n", - "\n", - "year, temp_anomaly = np.loadtxt(fname, delimiter=',', skiprows=5, unpack=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Exercise\n", - "\n", - "Inspect the data by printing `year` and `temp_anomaly`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(137,)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp_anomaly.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Plot the data\n", - "\n", - "Let's first load the **Matplotlib** module called `pyplot`, for making 2D plots. Remember that to get the plots inside the notebook, we use a special \"magic\" command, `%matplotlib inline`:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `plot()` function of the `pyplot` module makes simple line plots. We avoid that stuff that appeared on top of the figure, that `Out[x]: [< ...>]` ugliness, by adding a semicolon at the end of the plotting command." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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gUHfZ48Jxqy+xm6ApBJlcnlg6JzGCCogQCIKw4rg6GuePv3mSjNllrH/CEILOxlr+89le0tl8yfPGzYVj9T4XQdM1ZFkJYhGUR4RAEIQVx7eO9/GvT13h7IDhBuozLYLfevl2xmJpfvTcYMnzCi2CgJk1ZO2r90mMoBxVEQKl1KuUUmeVUueVUh8q8fmvKKWGlVJHza9fq8Z9BUFYnVhxgEsjMQB6w0ncNQ7eeMt62uu8fPVIT8nzCktJBDwucnnNwETK3ieUZtELypRSNcAngXuAHuCQUuohrfXpaYc+oLV+/2LvJwjC6uf5oSgAl00h6AsnaK/34qpxcMe2Jp6+OFbyvPFpFgFAz7jRj1hcQ+WphkVwO3Bea31Ra50GvgzcV4XrCoKwBsnlNedNIbg0aghB/0SCjpBRP6gl4GEkmipqTG8xkcjgUBD0Ogl6LCEw3EohSR8tSzWEYD1QGMbvMfdN501KqeNKqa8ppTqrcF9BEFYhPeNxUmYweMoiSNJhVgdtCrhJZY1MoOmMx9PU17pwOBQBT7FFUC8WQVmqIQSlqnRMl+pvAV1a65uAHwJfKHkhpe5XSh1WSh0eHh6uwtAEQVgOMrk8b/3MQX76/NTf8deP9PAb/35k1nOfHzSsgV3tQS6Pxsnm8gxEknTUm0LgN9pNjkZTM84NxzM0mEHhoHfKInAobAtBmEk1hKAHKHzD3wD0FR6gtR7VWlu/tX8Ebi11Ia31Z7XW+7XW+1taWqowNEEQloOBiSQ/uzzGkSvj9r6nLo7yw+eGZj333JARKL5ndxtjsTTnh6Pk8rrIIgAYiaZnnBuOZ+w3fytG0D0et60EoTTVEIJDwHal1GallBt4G/BQ4QFKqXUF374eeK4K9xUEYYUyEEkCU/0ArO10Nl92DYDF+cEo7XVeXrC+HoAnzo8C2DGC5kB5i2A8np6yCDyGIAxGUjM6kwnFLNpW0lpnlVLvB74H1ACf11qfUkp9FDistX4I+G2l1OuBLDAG/Mpi7ysIwsplYMIQgmiBEFh1f2KpLG5n+Yn53NAk29sCdmP5J8+PAMywCEZjpS2CnW1BYMoiAKiX1NGKVMVpprV+BHhk2r6PFGx/GPhwNe4lCMLKZ9CyCFKF/YON7WgqS0OZbmB5M2Pol27fRGejD6Xg6UtGqui6esMisDqJTbcI8nnNWCxtv/37PVPdyCR1tDKyslgQhKpjWQTTXUMwZRmUomc8QTKTZ0dbAK+rho76WqKpLHVeJ0GvMZl7nDUEvc4ZMYLnh6IkMjluWBe0j3M7jSlOFpNVRoRAEISqY8UIIgVCYG3HKgjB82ageHtbAMB2D3VMayzfHPDMcA0dvmJYDrd1Ndr7rEwhiRFURoRAEISqY7uGkjNdQ5MVhOCcmTq6rdV4q+9q9gEzhaDJ757hGjp8eZzmgJtNTT57n5VCKjGCyogQCIJQdaZnDaWzeXuRWEWLYHCS1qDHnrg3NxuWgZUxZNEUcDManWkR7N/UWNSE3goYS4ygMiIEgiBUFa01gxHjbd22Agosg8JMoumcGZhk17o6+/vNpkWwrn6aRRDwMBqbsggGI0m6xxLs72ooOi7gESGYCyIEgiBUlfF4hnQ2T4PPRTKTJ5PLFwWIywWLM7k854ei3NAetPftaq/D6VDsKtgH0Ox3MxZLk8sbRQwOXzYWru0viA8ABMy1BFJnqDIiBIIgVBUrY2i76eePJrNF2UPlhODySIx0Ls/Ogkm/I1TLkx9+Ba/Y1Vp0bFPAQ15P9R84fGUMr8vBno66ouPsGIFYBBURIRAEoapYgeJtZubPZDJLpMA1VC5G8JzZhGZXe/Fk3hr0Fvn9YeaissOXx9nXGcJVUzyl2a4hCRZXRIRAEISqYgWKt7caQhBJZuZkEZzpj+B0KLa2+me9h1V4biSaIpbKcro/wv5NjTOOmwoWi2uoEiIEgiBUlYGJJErBlpYpi8ASAqdDEU3NLB8NRqB4a0sAj7Om5OeFNFsWQTTNM1fHyeX1jEAxGBVMNzb6JH10FqQuqyAIVWUwkqTJ76HRfAufTGbsrKG2Oi/RAjdRIWcHJrl108zJvBRNBYXnTpuWxG1dMy2C+/at5759pdqjCIWIRSAIQlUZiCRpr/fYbploKmunjLbXe4mVsAgmEhl6wwl2rQvO+KwUoVoXDmXECJ68MMq+zhB+6TewYEQIBEGoKgMTSdrrvHbGzmQyy2Qqi8fpoMHnKrmy+KwZKL5hWqC4HA6HotHv4dJIjBM9Ye7Y2lS9H2ANIkIgCEJVGYwkaSsSAsM1FPS6CHicJbOGzg5EAIpSR2ejOeDmJ2eGyGs4sLW5OoNfo4gQCIJQxL8evMzJ3okFnZvM5BiPZ2iv89rVP430UaOCqN/jLJk19NzAJHVep11qei40BdzE0jk8Tgc3bwwtaLyCgQiBIAhF/Pm3n+NrR3oWdO6QWVqizZzQ67xOImbWUNDrJOAtLQRn+iPsWlc3Y71AJawU0v1dDXhds2caCeURIRAEwSadzZPO5Ymny9cDqoS1hqC9zhCCoNdV7BpyO0u2q7wyGmdry+zrBwqxFpXdIW6hRSNCIAiCjeW/j6VL5/rPxoVho4y0VQo66HXa6wgsi6DwPmC4k0Zj6RmF5WbD6l18QALFi0byrQRBsImZlkBigUJwqm+CoNfJxkZDCAJmTCBqCoGV4lnYrtIqSdE+j/gAwD272xiYSHKT2eReWDhiEQiCYGPl+FfqGVCJk70Rdhf4+g2LwHANBTwuu2NYYZyg3yxS1zFPi2BHW5A/f8ONOGtkGlss8gQFQbCxJuj4AiyCbC7PmYEINxa8oQe9LsLxDLF0rsgiKBQaq1rpfC0CoXqIEAiCYGMFiWMLCBZfHImRzOSLSkEHvU6GzZaShTGCyRIWwXxSR4XqIkIgCAX87leO8U//dWm5hzErR66M86q/eZyJeOm6PQvFelOPlykMB0YHsnd87im+e3KgaP+pPmPtwXSLQBu9Y6gzF5QV3gegfyJhrzEQlgcRAkEwSWVzPHi0lyfOjyz3UGblEz84x5mBSa6Mxap6XasyaCWLIJXN88T5Ub5/qlgITvZG8DgdbGmeSgOt805N7kGv0xaCwnaV/RPJeWcMCdVFhEAQTJ4fjJLNa7vZyUrlRM8E/2WKVaX+vwvBcg3F0zm09So/DSuOYDWSsTjVN8EN6+qKgrfBIiFwFWUNWQxMJCU+sMyIEAirlny+9ERWDquswvgKF4J/ePyCvV2uyctCsa6Xy2tS0xZ9WVhunfNDk2RyxjFaa071RWa0irR6BsM0i2Caa6gjJEKwnIgQCKuS030Rdv/Jdzk3ODn7wSan+ozCZ2NVFIIP/+cJfv3fjlTteldGYzxyop9X7WkHFhbUrUSh775c5pA1iWdymksjhmuqeyzBZDLLno7inP5CiyDgdVLjUNS6auz7pLI5RqJp2uvENbSciBAIq5KTvRMkM3m+c2Jg9oOtc8xgZzSVJZVd2IKq6Zzqm7BLLFeDf37iMk6Hg9+6extA2W5fC6WwV0C5tQSFxzzXb4jnSTtQXLp5fOF2Yb0hqzaRZAwtLyIEwqqkN5wA4Cdnh+Z0fC6vea4/YrsuxmPVycYZnkwRTlTnWslMjm8828urbmxnS7PRBnKhC7/KUXi9RKa0yBQec8YUuWevjuN0KHa0FZeRDnqnXEN15rax2ti4tp06Kq6hZUWEQFiV9E8YQnCsJ8yomcdeiYvDUZKZvF23phruIa01I9EU4Xh6TvGKVDbH0GSy7OffOzXARCLDL97WidflwKGqHywudDWVExnrbd7jdHB2YBKtNd89NcCd25pnVAG1soZcNQqP05huAh6n3a7S+j2JRbC8iBAIq5K+cJKgx4nW8Ni54VmPt+IDL91uVLKshhBMJDJkcpq8hugcfPmf++kl7v7rx8rW+fnK4W46G2s5sKUJpVTZ2v6LodDtY8UI0tm8bWEZxxj33NsZ4kx/hJO9EbrHErz2BetmXM+yCIJel112wu+pse/Tb68qlhjBciJCIKxK+sIJXrKjmeaAh5+cLRaCSDJjBzktTvZO4HE6uG2z0QB9LL54IRienLJE5rLw67n+CJPJLAcvzlzH0D0W54nzo7zl1k4cDmNCLdftazHEUln7Ld669peevsK9//cxu3S0JT77NzXQN5HkSz+7itOhuHdP24zreV0OnA5VHDT2TLWrHJhIFmUTCcuDCIGw6tBa0xtOsD5Uy107W3j83DDZ3FQq5P/69mle/bePF73lnuqLsKs9SItZ2nhsDu6k2SgUgvAchKB73IxrnJlpwXzlcDdKwZtv3WDvC3ic1c8aSudoNXsJWBZBz3iCWDrHhBnrsN7mb9nYAMBXD3dzYGsTIZ97xvWUUgSmTfQBz1TWUF84IW6hFYAIgbDqGI9nSGXzdIRqefnOViYSGZ7tDgOGm+O7JwdIZvL8n0eeA6wc+An2rK8n5HOjFIxVoXTDcIGYhBOzWxg9Y3HACHBPX8z1vVMD3LG1iY7QlAvF7zFq/VeTWCpLa9AQQ0tkrGB3xPTrx9JZvC6HXUoim9cl3UIWQa9zRhqpZVUMRJLiFloBiBAIq44+802/I1TLS3Y0465x8I1newF44vwIkWSW27oa+Pbxfp48P8KHvn7C3lfjUIRqXYzFltYiiKWyjMbSdDbW0jOesBu8gCFeF4dj7N1Q3Jf3WrmGWkwhsOoNWWOP2BZBloDHSVudh5DPRY1Dca+5rqEUW5oDbGkJ2N8Xxjb6J5J0iEWw7IgQCKsOy+XTUV9LndfFL9yynq8d6WF4MsXDJ/oJepx87t230VHv5V2f/xkPHO7mt+/ezhv2rQegwe+uSvposUVQ+Xo9plvonS/cBMCPz0ylvV4ZjZHNa7a3BYrOKQy6VotYOmu7xyyLYMK0ZiKm9RFLZfF7nCileNHmJu7d3Uajf6ZbyOJz797PR1+/x/4+6DHaVcZSWUaiKSkvsQIQIRBWHVMWgTHB3P/SLWRyeT7304t8/9QA9+xuo77WxZ+8fg9eVw1/9Za9/M49O+yslia/m9FpFoHWmn9/+gqf/Ml5PvmT81wseGMvx/Bkym6nODFL8LnbdAvdvrmRXe3BojjBuUHjXttbi3P0q501lM3lSWbyBL0uvC6HHSOYbhFEUzl8bsPV86l33MLf/9ItFa/rqnEU1R+y6g2979+OoDXsXldX7lRhiZBQvbDq6J9I4nE67LfULS0BXrm7nX/86UXyGl5j+rNfuaede/6kzc7CsWjwubkyGi/ad/DCKH/0jZP296f7I3xylglweDLF+oZa4unsrK6h7nHjfp2NPu7a2crnfnrRbvj+/NAkSsHWlmKLIFjlYLHVp9jvqcHvnnI7zYgRpLIEPMZ6genPbi5YgeP/Oj/CR163u6JbSVgaxCIQVh294QQdoVr7DR/gfXdtJa+NyfMlO5rt/aUmsqaAe0YF0odP9FPrquH4n97La17QzjEz+FyJkWialoCbUK1rVtdQ91iCWlcNTX43L9/ZQjavefLCKADPD0XZ2Oij1l28WMvvcRJNZstWCZ0vVuVRv8eJz1NDwqxAOmFbBFNNaxbTO2BHW5D2Oi//8M5bec+LNy9+4MKiqYoQKKVepZQ6q5Q6r5T6UInPPUqpB8zPn1ZKdVXjvoJQir7wzGqW+zpD3Levg3e8aBMeZ02ZMw0afG7G42l7gs3lNd87NcArdrVS53Vxc2cDPeMJRmZJMR2eTNES9FDvc8/JIuhsNMTr5o0NeF0ODlpCMDjJ9tbAjHP8HifZClVC54tlAfg9TsMiSGdJZHKkzdRbyyKIphYnBHs7Qzz1h3eLJbCCWLQQKKVqgE8CrwZ2A29XSu2edth7gXGt9TbgE8BfLPa+glCOvnCiZCP0v33bzXzo1btmPb/R7yaX1/Yb8M8ujTESTdsupb2dRvbO8Z7yVkEurxmLpWgJeAjVuuyAazm6x+J0NvgAcDsd3NbVyMELo2RyeS6NxNg+rYYPULLb11w5eGGUB4/2Fu2z6v/43TX43DXE07kiASvKGnKLV3k1UQ2L4HbgvNb6otY6DXwZuG/aMfcBXzC3vwbcrQrtdmFNkMnl+fh3zzAUKV9Ppxr3GJpMFeXbzxcrtmCtLn7kRD+B0eJdAAAgAElEQVRel4OX72oBjAqbDgVHuyfKXmMsliavoSVopFhWsgi01vSMJ+hs9Nn77tjazNnBSY5cGSeT02UtAmBBmUOf+OE5fv9rx4tWPMcLLQIzNbVICOysoZy0lVxlVEMI1gPdBd/3mPtKHqO1zgITQFMV7i1cRzx7NcynHr3Awyf6r9k9BiaSaM2iGp3YQhBLkctrvnPScAtZmTI+t5MdbcGKcQJrDYEtBBViBOF4hmgqy4aGKfG6wyx+968HrwDMqOoJlGzyUkj3WJw/+9Ypu3mMRT6vOd0XIZ3N8+CxKavAuk7A45yyCAosmUgig9aaWHoqWCysDqohBKXe7KdHr+ZyDEqp+5VSh5VSh4eHZy8UJlxfWBPn5ZHq9tktpHAx2UKZEoIMhy6PMRJN2W4hi32dIY71hMsGaq01BIYQuJmIZ8oeW5gxZLGno46g18l3Tw2UzBiC2YXgn/7rEv/8xOUZgnV1LE40lUUpeODQ1DuclYHkc9fYMQLLYmgOeIgkM2YLS8QiWGVUQwh6gM6C7zcAfeWOUUo5gXpgbPqFtNaf1Vrv11rvb2lpqcLQhJXEUdOnfmlaamYlzg1O8pEHT/LFg5ftfROJDB/48rMly0tb1SyrIwSpKbfQztaiY/Z2hgjHM1wdK/2zWBZBsxkjSOfyZev7d48Z4mXFCACcNQ5euLmJXF6zoaF2RsYQGGmeUDpGkM9rvnPSsLyOThMCq9LqW27dwKm+iN2i03IxBcysoXgqZ1syGxtriSQyRQFlYfVQDSE4BGxXSm1WSrmBtwEPTTvmIeDd5vabgR/rauW8CdcN1pvppZHZF2NprXn/l57h3k88zhcPXuHfnrpSdJ0Hj/bxozMzm84UripeKJYQjETTfOfkAHftaJ0x8VnlHqZPshZWRlFzwHANQfkyE1MWQfGYLffQjtaZbiGobBE8c3WcQbP71/Ge4ljGyb4JnA7F771yJ26ng68cNqyCwkneZ1oE1pg3NvqYSGSL3EfC6mHRQmD6/N8PfA94DviK1vqUUuqjSqnXm4f9E9CklDoP/A4wI8VUWN2MRFP0jCeo8zrpHU/YJY3L8fjzI3z7eD/vuXMzr3lBO+MFk+i4GcQ91TszWHu0O1wy534++NxOvC4HPzg9yPBkitfcNLOg2o62AF6Xg2NlAsbDkyn87hr8Hif1tYawlBWCsTghn6uomxfAHdsMIdjWNtMtBIXB4plC8PCJftxOBy/Z3syxnpkWwfa2IK1BL6++sZ1vPttrl3wAqHUZWUPJTJ6xWAq300Frndd2DRXeW1gdVGUdgdb6Ea31Dq31Vq31/zb3fURr/ZC5ndRav0VrvU1rfbvW+mI17itcP1iplq+9aR15TVmXisVnHr1Ae52XD716Fxsb/YQL8vqtcsiWi8Mil9c8dXHUfpNeDI0+N0e7w3icDu7e1Trjc2eNgxesr58xyVpYawiAKYugTAppz3iiyC1ksbMtyAfu3s5bbu0scZZRxRNmWgT5vOY7JwZ46fYW7tzWzJXROOPmAjmtNad6J7ixwyjrcPcNbUSSWS4MR4mlc/jdNTgcCr8ZGO+bSBKqdVFf6yKdzdsL7fwSLF5VyMpiYVGcH4ryxk89MWvjlaPdEzgUvO6mDqBywPhYd5iDF0d574s343Y6aPC5yOS0/TZqFYQ73R8hV9AC8nSf0djlQBWEoMF0D921s6Xs229Xk5/e8UTJz0oJQblnNBqbOrYQpRQfvGcH20qkjgL2ZD1dCJ7tHmcgkuS1N7XbLixLsAYjKUZjabuE9A7T2nh+KGoXkwPwmRN9fzhByOeym9X0m643v6wjWFXIb3ONo7XmTZ9+kueHDL/9227r5I9eO309YHmevjTKs1fDnOid4MXbm8sed6w7zI62oF1g7PJoeSH4zGMXqPM6efsLNwJTE+l4PI3f47TfrOPpHJdHY3ZGzZMXjM5e1RACK04wPVuo6JiAUZxOa41SirFYmrd99iC3bGzgymjMXngWslxDZVJII4ks21rm/6dY41DUumpmuIa+fbwfd42Du29ow6EUSsGx7gnu2tlqB4b3mBbB5mY/NQ7F+cHJohXDtkUQTrKx0UddrfE76DOD8eIaWl2IRbDGiaayPHM1zA3r6mjwue36NnNl0JwYrIBnKbTWHOsJs68zRIPfTX2ta0arSIv+iQTfPTXALx/YZAckrc5Xlo89HM9glQg6WRAnePLCKNtbA7QGF1/WuDngwe00JtNyNPndZHLabrt4ZiDCucEoDxzupm8iaTd4mS1YHElm7Il2vhgVSKeykdLZPA8e7ePuG4xyGAGPk+2tAdsiONUXQSm4wRRkj7OGTU0+zg1GiadztsvHZ8ZYBieT1Ptc9vgsi0CCxasLEYIVRP9Egjs/9mMePTszG+ZaMRo13q5/cX8nt3U1zrtp+4C5Sri7gs//ymiccDzDTaaboqvZbwuB1roov757LIHWcGDLlHXRME0IxuNpdrQFcdc4OG3GCdLZPIcuj1UlPgDwG3dt5TPvvKXihNfkt9paGs9sxPz3gfsP8L/feKNdUM3rqsHjdJSMEWitiSQy1HkXJgSFbR8BfvjcIGOxNG+9bSqusHdDiGPdxpqHk30TbG72F73Rb28N8PyQaRGYloD1udYQqnXZ4+u3LQKJEawmRAhWEP/nkTP0hhNlM1GuBVbd/aaAm6aAm7FYel7VLAfMFMXuMr5ygOPmW/tNGwy/9OYmnx0j+J/fPMl7/uWQfayVEWS9RRduWxNpOJ6hJehhZ3uQk33GtY/3hImncxzYWt49NR+2twV5xa7y1gAYriHADqCOmGsHtrUGeMcLN7GpyV/0M5SKEcTSOfIa6moX9oYd8BZ3KXvgUDfr6r28dPvUOpy9nSFGY2n+6Jsn+enzw7zAjA9YbG8Ncnk0Tth0vcGURWCNvb7WCh5LjGA1IkKwQjh0eYyHjhnr8Iaj164Wz3Sst9jmgIcGn5tUNm8HZeeC7RqqYBFcGIqiFHbQc3NzgL6JJOeHJvnyoW5O9E5l/1iTZSkhGLddQ2lCPjc3rq/jVF8ErY2SzUrBi7Y0znnsi6XJjCNYC9tGoim71eV0QrWlK5BahdwWahH43U7bNdUXTvD488O8+dYN1BSU1755o2GJPXCom1fsauX37t1ZdI3tbQFyec2F4ViBEExN9CGfe8oiCCfxmZlFwupBZH0FkMtr/uxbp2iv8+JxORiKLL5f7lyxXENNAbc9sY3F0nMOBlquoZ4KMYJLIzE66mvxuoy3zK5mI1Xyj795ilxe26mhSinbIrDcQVAQbDXfvMOJDA0+F9vbgvzHz7p54vwoDxzq5saOejuesBQ0FjwvMJ5lk99dcpKs97lsiyaSnHIFWaWd6xcYIwh4nPbv4GtHetCaGemmezrq+ad372fXujrWl1hxbXU+y+W1XUOo0CKor52KESQyuZIZTsL1jVgEK4BvHevjZG+ED79mFxsbfQxNLqUQGPdq9LvtlMnxWdoqWiQzOSYSGYIeJyPRtN3YZDqXR2Nsbp5yk1jbBy+O4nU5yOanAq7j8QyuGlU0EbmdDvzuGsKJDLm8ZiKRIVTrsnPh3/X5p0lmcvz5G26c50+/OKwYge0aik61ppxOqNaoQPqNZ3vY+2ff54S52tcqdb2YYLHlGvrm0V4ObGliY9PMNQl339BWUgQAtrT47eC7b1qMAAyLzON04DbbTUqgePUhQrDMaK35zGMX2N4a4Odv6qAl6LHr1CwFo7E0Qa8Tj7PGfsOd3p2rHAOmW+iWTQ3AVAP2QrTWXBqJ2VYAGMFii1+5wwioWgueJhKG22d6lfKQ2SxmMpkxApg+N7va66h11dDV7Ocbv3En+8x0zaWi1qzbP1YoBGXelkM+F91jcT78nyfQeip9dtGuITNrKBxPc3E4VtR9ba54XTV2PKNkjKDW+H1YcQwJFK8+RAiWmUfPDnNmYJL/9rKtOBzKFoKlKsVU+BZrCcH4XIXAdEnc1mUIQak4wVgszWQyS1dB4LTO62J9qJZX7mnj9s0N9nHGvTOlfexmsNWKEzT4XdS6a/jOB17CQ+9/ccm34KWg0e8uEII0zYHSrqmQz00snbO7o1lWl+UaWmiwOGgGi4+ZFsa+DQsTQyt+Y7mGPE6HHWewYjSWWEmgePUhQrDMfPqxC6yr9/L6vcaK29agl3Qub5dRuNZYfm2Y6fOejUFTCPZ3GQHaUkJgvfkWuoYAvvK+A/z1W/fNSA0NJ9JF8QGLkM/FeDxN2MoqMuMGXc3+ZXVVNPndjEQN4R6u4BpaV+/F6VB85p23AlOxmWoEixOZHEeujKMU3LihfvaTSmCtMLZcQ0pNuees+EXQ/FdcQ6sP+Y0uI89cHednl8b4n6+9AbfT0GRrEdLQZGpJAp+jsZQ9Sdd5nTgdas5CYLmGdncYLppSKaSXRgxxmC4Elr/amvSte4bjGTY2zny7D/nc9IcjtmAUZhUtJ41+N0OTKSZTWdLZfFmL4B0v3MS9e9pZH6qlvtZVYBEY/v2gd2F/ipab5uCFEba2BBYsKFbAuHCS97udTCazBRbBzPiBsDoQi2AZeehoHz53DW+/faO9zxaCJcocGo2maTLfYpVSNBS4OmZjIJLE766hzutiQ0NtaYtgJEaNQxU1XSlkeoB6PJ4uOck3mF2+rMybpcwOqkRTwMNYLG2/4ZezCNxOhy1+TX63HYeJJDL43TU4axb2p2hN3M9eDdt1hRbC3s4QTocqKoXt89RQ41D2PayAtsQIVh8i7cvI1bE4m5qKV3m22BbB7GsJTvZO0BL00Fa3sJIKubxmLJ4umrwafXMXgsFIkrZ6496djb7SFsFojA0NtbjKTHR1Xic1BVZIOJ4p7RqqdROOpxkzC841rBCLwJrUC/sPzEaD323HYRZTXgKm3s6zec2+zoW5hcCw2I788T1Faax+t5NQrcsO3FufSYxg9SEWwTLSO54o6lML0GpO6nPJHHrPvxzib3/0/ILvPx5PozVF7ozG+VgEE0nazfF2NtTSMxZHa82zV8dt6+DySKwoUDwdpRQNZkZQIp0jlc1TX2KSD/lc5LURh1CKGbX7l4tGv5t0Nm+vlJ6LEBQ+40giu2B3DkyVogbsIncLZfpahlp3TdHvwg4Wi2to1SFCsExorekNJ2bkdluNw2dbS5DK5hiaTNl1bhaCvZjMX2AR+N2MzXEdwWAkNSUEjT4mU1kePTvMW//hIPf/6xHyeSN1dHp8YDqNfhfjsSm3T+lgsbHv0kiM+lpX0crZ5cQKsJ8bnASgOTi7y6rQ6ppIZBacMQRTriF3jYNd7XULvk4pdq+r48aOKSvDGqcEi1cf8htdJiJm27/pFgEYcYLZhMCKIUymFp5dZC0ma1qARZDP6yLX0Aazscpv/Psz1DgUz/VH+PozPcTTObpmSe1s8BniY/UZKJU+armCLo/GSgrFcmE9u7ODRhmNxjmMrTHgNq0xTSSZscV0IVhumt0ddXbCQbX409fvKfpeLILVi1gEy4RVtrmUELQEPQxFKscIrBz+aLL0at65MBKzApxTk1eD381EIkM2V7mV5GgsTTavCywC4+fIa82X7z9AW52Hj33nDFC8gKwUDT7DZ14pEGwFkHvGEwsux3AtsKypcwOTNPjccwr6FpavXmyMwHo7X4rFdBIsXr2IECwTVpP19aGZb8utQS/D0coWgZW6ObkIIbAtggLXUJPfjdblm6hYWGsIrED1luYAO9uCfOxNL2BfZ4j3vniznRmzpbl0hy2LBr/bXCNQPjXUEodcXq+YQDFMuYYGIsmyqaPTsVNmo2kzRrDwN+zWOg97Oup41Y3tC77GXLHGKa6h1YcIwTJhtThcX8YiGJ4lfdSaiCOLEoI0NQ5V9IbdMMfVxZYQtZuuoVp3Dd/74Et5480bAHj77RsJep24ahQdocquj0a/i/F4xnZJlc4aKqxGuvJcQzC3QDEUlq9OMblIi8DrquHh334JL9pSnT4Mldi9ro6bNtTbTW2E1YMIwTUmmsryF989Y1sAFr3hBLWumpJvt611HiZTWRIVykFPWQSLiBHEUjROq5bZNMd6Q5Zrqpx/O+h18fuv3Mkbb14/q7ukwecml9e2u6yURVBfJAQrxyLwuZ14XcbPN2chMIWseyxh9CJYIRlQs9Fa5+Wh97+YjjLF64TrFxGCa8wPTg/w6Ucv8MZPPmFXnASjbPOGhtoZxdUAWgKzryWwJuJUNk86W9mfX46RgvISFtbb+GwWQf9EAoeiojvkXQe6+Pib9846Dsu9cnE4htflsMtVF+KscdiuCau8xErBcq3NWQjMn9cqv7GYrCFBqAYiBNeYM/2TuGscuGocvPUfDvLM1XHAsAhKuYVgai1BpcwhyyKAhVsFoyVq48ylAmksleVrR3rY1xla8IrYQix31KWRyhlBlkuowb+y3qAt91DTHGMEthCYaw+uF4tAWL2IEFxjnhuYZFtrgG/85h14XA7+7eAVwIgRlKsPb5WZqLSobCCStHPpo6mFxQlGY+kZk5c1yVayCD716HkGIyn+6LW7F3Tf6ViT/5XRWMWMIMuNtpJiBDA1sbfM0SLwuY0expdHDVfYYmIEglANRAjmwEe/dZqfnFlYQ/mzAxF2rQvSGvRy144WHj03zGTSKKdcziKwy0yUSSHVWjMUSbHJzM9faOaQUXm0ePLyOGsIeJxlLYKro3H+8aeXeOPN67nV7EOwWCyfeSanK1oE9eZnpdYZLCeWEMxlMRkYq6kb/e4p15BYBMIyI0IwC/F0ls8/ccnuJzwfxmJpBiMpbjBXfL58VytjsTTfOTkATC3Cmk6jz43Tocq6hsZiadK5PNvNGvKRBbiGkpkc0VS2pDuj0UznLMXHvvscTofiD161a973LEehq6dSINiyCFbSgjKYCrDPNUYAxjO20mUlRiAsNyIEs3DJ9ONWas5ejjMDRlP2ne1Gid+Xbm9BKfj3pwz3UDnXkMOhaA54iuIAhViBYqt0cDmLQGvNb37pGb5VQsRGSywmsyhXgfT80CSPnBjg11682U4brQYBj5FmCpXdPpYlsJKyhgC7eut8hcBCLAJhuZFXkVm4MGwKQYXm7OU4O2DUn9m1zpiwG/xubu4M8czVMFB6VbHFrZsa+PHZIVLZqa5WFtYagu1mM5FyQnDo8jgPH+8nkc7x82bjG4upXsUzJ68mv9u+RyH/8NhFvC4Hv3Ln5rLjXghW4TmjB0MFi8CcPFeaELx+bwdaG81n5kqhECy0F4EgVAuxCGbhwlAUMAqsJTPl8/ot/s8jz/GRB08CRsZQk99dFER8xa5WwCgSVim4+Jb9GwjHM/zg9OCMzwYmjEl8a4shBNEyrqEHDnUDcKw7PKP1pdVfuNRiL6vkQ/E9k3zzaC+/uL+zaBKrFtY1K60aftMtG/hfb7hxxVQetegI1fLrd20tmQpcDsu9tZheBIJQLeR/4CxcNF1DwIxFYaX40Zkh/vWpK1wYjnJmIMLO9mDRBHHXTkMIOkLeooVc03nJ9hY66r32ZF7IQCSJUrClxajhU8oimExmeOREP/W1LkZj6RmN5a+YGSubSpSIbgq4GYmli8Tj809cIq/h116ypdKPv2BCc8gI6mz08c4Xbbom919qrLjCSqqbJKxdRAhm4cJQ1P5jnUucYGAiidbwmUcvcG4wOqM08J6OOlqDnrIduyxqHIo337qB/zo/Qs80t9TgRJImvwef24nH6WCyRProt471k8jk+L1X7gTgWE+46POrYzGa/O6SdWM6G2pJZ/MMmmUuUtkcX3r6Kq99wbpZx71QLItgpWUEXSssN5ekjgorARGCCuTzmosjUV6yvRmgZAeuQiaTGaKpLAGPk68900Mik7PjAxZKKT79zlv48KtvmPX+b9nfidbwtSM9RfsHIkna6w23UtDrKrmg7IHD3exsC/KL+ztxOx0c6y4WgiujcTaWKQ+9xXQ5XRg23GKXRmJEU1l+bnfbrGNeKA32YrGVlRF0rbAsAgkUCysBEYIK9EeSJDN5XrSlCbfTQc8sFoGV5fPrd23FcvrcUKJZyK2bGtndMXvhrs5GH3dua+JrR3qK3DSDkanOYHVe54zCcxeHoxzrDvOW/RtwOx3s6ajjWPdE0TFXRuNsKvN2b8UeLppCcG7Q+HdHW+Uqooth7VoEEigWlp81IQTZXJ7YAlbfWoHiba0BNoRqZ80c6jOF4PbNjbzupg5cNcrO7Fko9+1bT894guMFdYoGIkm7/HPQ65wRI3j60hgAd99gvMHv3RDiRO+E3WMgnc3TP5FgY5kWkm11HvzuGjtj6vzgJA7FrJ3GFkNbndesXTT3FMzrGbEIhJXEmhCCD37lGG/81BPzPs9yjWxtCbCh0Uf3WGXX0MCE8Xl7nZc/f8ONfPn+AyULqM2He3e34XQoHjnRDxgLwcLxqa5WQa9rRtbQoctjNPnddmewfZ0hEpkcz5vC1jMeJ69hYxmLQCnFlpaA/fOfG4zS1eSfkcZaTd50ywa++r4Da8Y1JDECYSWx6oXgyQsjfOtYH+eHomRm6bo1nYvDMeq8TpoDbjobZrcI+ieMbJ62Oi/1ta6qlGAI+dzcua2Zh0/0o7XmqOnrt8pTlLIIjlwZZ39Xg52tZDU1t+IEV8asjKHygd+tLX4umhbB80OTi7ZsZqPWXcOtmxqv6T1WEg0+Nz53jW3ZCcJysqqFIJvL89FvnQYgrym7UrccF4ajbGkJoJSis9FHOJ6pWOmzP5ykOeCpeu/Y175gHT3jCY71TPD/PvIcbXUeXrnH6EgV8BQLwdBkkiujcfYXTKpdTT7qa1125tBVK3W0QgbQlpYAveEEE4kMl0fj9ipmoTrUOBTf/q0X8+47Vkc6rHB9s6qF4D8OdXNmYJJ3vmgjMP/VwReGo3bgtNOsC1TJPdQfSc5rdelcuXeP4R763a8c5XjPBB969S67gfj0rKEjl40y1/u7pqwRpRT7OkM8fWkMrTVXRuPUumrs4nalsH7uH58ZJJfX19wiWItsaQngc0uwWFh+Vq0QfPt4H3/+7dMc2NLE/2MuguqdJf2zkIlEhsFIyl60ZTVnryQmAxOJayIEIZ+bO7Y1c2E4xi0bQ7xh33r7s6DXSSydI5c3sooOXR7H43Swp6O+6Bo/d0MrF4djPD8U5epYjI2NvoorYa2f+7tmgTyxCARh9bLqhEBrzacePc/7v/QsezfU86l33MK6+lqUmtvK4MFIkr/+/lle8VePAnDzRsO/PmURlBeC/nCSdfXXpo3fL9y8HqdD8Sc/v6doArfq1ERN99CRK2Ps7QzNcE+98sZ2lIKHj/dXXENgsbnZj1Lw2LlhHAWrmAVBWH0syi5VSjUCDwBdwGXgrVrr8RLH5YAT5rdXtdavX8x9K3FhOMYnfnCO1+/t4ONvvsnO2mkNemaUWSjk0ohx3iMn+slpzd27WnnPnZu5Y6uxmCzkcxHwOMteYzKZYTKVrWpVzkLu29fBS3e0zKjzY6UfTqYyuJyKk30R3veymWUgWoNebu9q5OET/XSPxXnZjpaK9/O6atjQUEv3WILNzf5FZz8JgrByWayD8kPAj7TWH1NKfcj8/g9KHJfQWu9b5L3mxLbWAN/4jTvZ01FX9Oa8ocFX1jWUzuZ57xcOMRRJ8a4DXbz7jk0zavAopcyJsbRFYFXrvBauIev+pYq9WRbBZDLL1bE4ubwuChQX8tqb1vGRB08BlTOGLLY0B+geS7CtVeIDgrCaWaxr6D7gC+b2F4A3LPJ6VeHG9fUz/N/rQ7VlXUNfPHiZi8Mx/u7t+/jIz+8uWYgNYHtbkGM94ZJpqP0TlhBcG9dQOaxKnJPJLM9cMYyxWzaWTlt91R7DPQSUXUxWiBUw3i5CIAirmsUKQZvWuh/A/Le1zHFepdRhpdRTSqmyYqGUut887vDw8PAih1bM+oZa+icSdlDVYiSa4m9/+Dwv29HCy3eWG77BfXs7GImm+XGJtpX94WtrEZQjYFsEGY52T7ClxU99mVLOrXVebjOthUqpoxZbWw2x2NEmgWJBWM3MKgRKqR8qpU6W+LpvHvfZqLXeD/wS8DdKqa2lDtJaf1ZrvV9rvb+lpbIPe76sD9WSyWmGJovXEvz198+SyOT449ftnrWe/F07W2gNeviKWRo6lsryhScvE01lbYugtW5pSyQUuoaOdofZtyFU8fh33bGJXe3Bsv2SC3nh5ibWh2qLUlEFQVh9zBoj0Fr/XLnPlFKDSql1Wut+pdQ6oGSHd611n/nvRaXUo8DNwIWFDXlhWN3AescTtvsmns7y9SO9/OJtnXPygztrHLzp1g38w2MXGIwk+ei3T/Pw8X4uj8ZIZnI0B9zXtAxDKSwhODs4yUg0Za8iLsfrburgdTd1VDzGYltrgCc+9IpFj1EQhJXNYl1DDwHvNrffDTw4/QClVINSymNuNwN3AqcXed95YwtBQZzgyfOjpHN5XvOCdXO+zlv3d5LX8J5/OcTDx/vZ1OTjiwev8PTFsSWPD8BU1tAT50cAZhUCQRCE6SxWCD4G3KOUeh64x/wepdR+pdTnzGNuAA4rpY4BPwE+prVeciHoMBvFF6Z//uTsEH53zbxcH5ub/dy+uZFTfRHu2d3G13/9DnzuGi6OxK5Z6mglPE4HrhrFid4JXDWKG9aJP18QhPmxqPRRrfUocHeJ/YeBXzO3nwResJj7VAOf20mj320Lgdaan5wZ4s5tzfN253zw53bwz09c4i/fspf6Whcf/LkdfPTbp5c8UAxGWmnQ62Islmb3urold00JgnD9s6YKnWxomEohPTcYpW8iyW/fvX3e1zmwtYkDW5vs73/5wCaO94S5d3d71cY6H4JeJ2OxtLiFBEFYEGtKCNaHajk7OAkYbiGYaia/GFw1Dv7mbTcv+joLxQoY750lY0gQBKEUq67WUCXWh2rpCydst9AN6+qWxa9fbawG9ECmu7wAAAdNSURBVGIRCIKwENaWEDTUkszk+bn/+xiHLo/xil3VXauwXAS9LoIeJ1uuYStJQRBWL2vKNXTvnnaOdYdJ5/LcuL6et922cbmHVBXefaCLe3a34XBUXhAnCIJQijUlBOtDtcvqy79WvHh783IPQRCE65g15RoSBEEQZiJCIAiCsMYRIRAEQVjjiBAIgiCscUQIBEEQ1jgiBIIgCGscEQJBEIQ1jgiBIAjCGkdprWc/ahlQSg0DVxZxiWZgpErDWQqut/GCjHmpuN7GfL2NF1bXmDdpredVP2fFCsFiUUodNvskXxdcb+MFGfNScb2N+XobL8iYxTUkCIKwxhEhEARBWOOsZiH47HIPYJ5cb+MFGfNScb2N+XobL6zxMa/aGIEgCIIwN1azRSAIgiDMgetGCJRSn1dKDSmlThbs26eUekopdVQpdVgpdbu5v14p9S2l1DGl1Cml1K8WnPNupdTz5te7l2HMe5VSB5VSJ8wx1hV89mGl1Hml1Fml1CsL9r/K3HdeKfWhlTJmpdQ9Sqkj5v4jSqlXFJxzq7n/vFLq75RS16Rrznyfsfn5RqVUVCn1ewX7VuQzNj+7yfzslPm519y/JM94vmNWSrmUUl8w9z+nlPpwwTlL8pyVUp1KqZ+Y9z+llPqAub9RKfUD8+//B0qpBnO/Mp/heaXUcaXULQXXWpI5YwFjfoc51uNKqSeVUnsLrjW/56y1vi6+gJcCtwAnC/Z9H3i1uf0a4FFz+w+BvzC3W4AxwA00AhfNfxvM7YYlHvMh4GXm9nuAPze3dwPHAA+wGbgA1JhfF4At5s9wDNi9QsZ8M9Bhbt8I9Bac8zPgAKCA71i/p+Ucb8HnXwe+Cvye+f1KfsZO4Diw1/y+CahZyme8gDH/EvBlc9sHXAa6lvI5A+uAW8ztIHDO/Bv7OPAhc/+HmJonXmM+QwW8CHja3L9kc8YCxnyHNRbg1QVjnvdzvm4sAq314xgTetFuwHpzqgf6CvYHzTekgHleFngl8AOt9ZjWehz4AfCqJR7zTuBxc/sHwJvM7fsw/nhSWutLwHngdvPrvNb6otY6DXzZPHbZx6y1flZrbT3zU4BXKeVRSq0D6rTWB7XxP/OLwBuWe7wASqk3YPwxnyo4fsU+Y+Be4LjW+ph57qjWOreUz3gBY9aAXynlBGqBNBBhCZ+z1rpfa/2MuT0JPAesN+/3BfOwLzD1zO4DvqgNngJC5jNesjljvmPWWj9pjgngKWCDuT3v53zdCEEZ/jvwl0qpbuCvAMsE/XvgBgxhOAF8QGudx3io3QXn95j7lpKTwOvN7bcAneZ2ubGt5DEX8ibgWa11CmN8PQWfLfWYS45XKeUH/gD4s2nHr+RnvAPQSqnvKaWeUUr9D3P/cj9jKD/mrwExoB+4CvyV1nqMZXrOSqkuDOv1aaBNa90PxsQLtJqHrai/vzmOuZD3Ylg0sIAxX+9C8OvAB7XWncAHgX8y978SOAp0APuAvzf9l6V8qEudNvUe4DeVUkcwzL+0ub/c2FbymAFQSu0B/gL4b9auEtdYyjGXG++fAZ/QWkenHb/c44XyY3YCLwbeYf77RqXU3azsMd8O5DD+/jYDv6uU2sIyjFkpFcBwBf53rXWk0qEl9i3L3988xmwd/3IMIfgDa1eJwyqO+XpvXv9u4APm9leBz5nbvwp8zDSZzyulLgG7MJTxroLzNwCPLslITbTWZzDMfZRSO4DXmh/1UPymvYEpV1e5/UtChTGjlNoAfAN4l9b6grm7hykzFZZ4zBXG+0LgzUqpjwMhIK+USgJHWLnPuAd4TGs9Yn72CIav/t9YxmcMFcf8S8B3tdYZYEgp9QSwH+Mtdcmes1LKhTGh/rvW+j/N3YNKqXVa637T9TNk7i/397ekc8Y8x4xS6iaMee/VWutRc3eluaQ01yLoca2+MAJOhcGq54C7zO27gSPm9qeBPzW324BejAJNjcAljKBPg7nduMRjbjX/dWD4dd9jfr+H4mDxRYygj9Pc3sxU4GfPChlzyBzPm0pc4xBG0M0KZL5mucc77Zw/ZSpYvJKfcQPwDEbQ1Qn8EHjtUj/jeY75D4B/NsflB04DNy3lczbv/UXgb6bt/0uKA68fN7dfS3Gw+Gfm/iWbMxYw5o0YscQ7ph0/7+d8zf7TXIOH9B8YPscMhuK9F8NUPmL+oE8Dt5rHdmBkFJ3A8GO+s+A67zEf3nngV5dhzB/AyAY4B3wMc1GfefwfYUT7z1KQAYKR0XDO/OyPVsqYgf+J4Qs+WvBlTQ77zWd/ASNmo5Z7vNPO+1NMIVjJz9g8/p0Ywe2T1iSwlM94Af8vAhgW+ikMEfj9pX7O5tygMTKurP+br8HIuvoR8Lz5b6N5vAI+aY7rBLC/4FpLMmcsYMyfA8YLjj280OcsK4sFQRDWONd7sFgQBEFYJCIEgiAIaxwRAkEQhDWOCIEgCMIaR4RAEARhjSNCIAiCsMYRIRAEQVjjiBAIgiCscf5/yZvmv2Mz/jkAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(year, temp_anomaly);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "Now we have a line plot, but if you see this plot without any information you would not be able to figure out what kind of data it is! We need labels on the axes, a title and why not a better color, font and size of the ticks. \n", - "**Publication quality** plots should always be your standard for plotting. \n", - "How you present your data will allow others (and probably you in the future) to better understand your work. \n", - "\n", - "We can customize the style of our plots using parameters for the lines, text, font and other plot options. We set some style options that apply for all the plots in the current session with [`plt.rc()`](https://matplotlib.org/api/_as_gen/matplotlib.plt.rc.html)\n", - "Here, we'll make the font of a specific type and size (serif and 16 points). You can also customize other parameters like line width, color, and so on (check out the documentation)." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rc('font', family='sans', size='18')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll redo the same plot, but now we'll add a few things to make it prettier and **publication quality**. We'll add a title, label the axes and, show a background grid. Study the commands below and look at the result!" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#You can set the size of the figure by doing:\n", - "plt.figure(figsize=(10,5))\n", - "\n", - "#Plotting\n", - "plt.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1) \n", - "plt.title('Land global temperature anomalies. \\n')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Land temperature anomaly [°C]')\n", - "plt.grid();" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "Better, no? Feel free to play around with the parameters and see how the plot changes. There's nothing like trial and error to get the hang of it. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Least-squares linear regression \n", - "\n", - "In order to have an idea of the general behavior of our data, we can find a smooth curve that (approximately) fits the points. We generally look for a curve that's simple (e.g., a polynomial), and does not reproduce the noise that's always present in experimental data. \n", - "\n", - "Let $f(x)$ be the function that we'll fit to the $n+1$ data points: $(x_i, y_i)$, $i = 0, 1, ... ,n$:\n", - "\n", - "$$ \n", - " f(x) = f(x; a_0, a_1, ... , a_m) \n", - "$$\n", - "\n", - "The notation above means that $f$ is a function of $x$, with $m+1$ variable parameters $a_0, a_1, ... , a_m$, where $m < n$. We need to choose the form of $f(x)$ a priori, by inspecting the experimental data and knowing something about the phenomenon we've measured. Thus, curve fitting consists of two steps: \n", - "\n", - "1. Choosing the form of $f(x)$.\n", - "2. Computing the parameters that will give us the \"best fit\" to the data. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What is the \"best\" fit?\n", - "\n", - "When the noise in the data is limited to the $y$-coordinate, it's common to use a **least-squares fit**, which minimizes the function\n", - "\n", - "$$\n", - "\\begin{equation} \n", - " S(a_0, a_1, ... , a_m) = \\sum_{i=0}^{n} [y_i - f(x_i)]^2\n", - "\\end{equation} \n", - "$$\n", - "\n", - "with respect to each $a_j$. We find the values of the parameters for the best fit by solving the following equations:\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " \\frac{\\partial{S}}{\\partial{a_k}} = 0, \\quad k = 0, 1, ... , m.\n", - "\\end{equation}\n", - "$$\n", - "\n", - "Here, the terms $r_i = y_i - f(x_i)$ are called residuals: they tell us the discrepancy between the data and the fitting function at $x_i$. \n", - "\n", - "Take a look at the function $S$: what we want to minimize is the sum of the squares of the residuals. The equations (2) are generally nonlinear in $a_j$ and might be difficult to solve. Therefore, the fitting function is commonly chosen as a linear combination of specified functions $f_j(x)$, \n", - "\n", - "$$\n", - "\\begin{equation*}\n", - " f(x) = a_0f_0(x) + a_1f_1(x) + ... + a_mf_m(x)\n", - "\\end{equation*}\n", - "$$\n", - "\n", - "which results in equations (2) being linear. In the case that the fitting function is polynomial, we have have $f_0(x) = 1, \\; f_1(x) = x, \\; f_2(x) = x^2$, and so on. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Linear regression \n", - "\n", - "When we talk about linear regression we mean \"fitting a function to the data.\" In this case,\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " f(x) = a_0 + a_1x\n", - "\\end{equation}\n", - "$$\n", - "\n", - "The function that we'll minimize is:\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " S(a_0, a_1) = \\sum_{i=0}^{n} [y_i - f(x_i)]^2 = \\sum_{i=0}^{n} (y_i - a_0 - a_1x_i)^2 \n", - "\\end{equation} \n", - "$$\n", - "\n", - "Equations (2) become:\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " \\frac{\\partial{S}}{\\partial{a_0}} = \\sum_{i=0}^{n} -2(y_i - a_0 - a_1x_i) = 2 \\left[ a_0(n+1) + a_1\\sum_{i=0}^{n} x_i - \\sum_{i=0}^{n} y_i \\right] = 0\n", - "\\end{equation} \n", - "$$\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " \\frac{\\partial{S}}{\\partial{a_1}} = \\sum_{i=0}^{n} -2(y_i - a_0 - a_1x_i)x_i = 2 \\left[ a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i \\right] = 0\n", - "\\end{equation} \n", - "$$\n", - "\n", - "Let's divide both equations by $2(n+1)$ and rearrange terms.\n", - "\n", - "Rearranging (6) and (7):\n", - "\n", - "$$\n", - "\\begin{align}\n", - " 2 \\left[ a_0(n+1) + a_1\\sum_{i=0}^{n} x_i - \\sum_{i=0}^{n} y_i \\right] &= 0 \\nonumber \\\\ \n", - " \\frac{a_0(n+1)}{n+1} + a_1 \\frac{\\sum_{i=0}^{n} x_i}{n+1} - \\frac{\\sum_{i=0}^{n} y_i}{n+1} &= 0 \\\\\n", - "\\end{align}\n", - "$$\n", - "\n", - "$$\n", - "\\begin{align}\n", - " a_0 = \\bar{y} - a_1\\bar{x}\n", - "\\end{align}\n", - "$$\n", - "\n", - "where $\\bar{x} = \\frac{\\sum_{i=0}^{n} x_i}{n+1}$ and $\\bar{y} = \\frac{\\sum_{i=0}^{n} y_i}{n+1}$.\n", - "\n", - "Rearranging (7):\n", - "\n", - "$$\n", - "\\begin{align}\n", - " 2 \\left[ a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i \\right] &= 0 \\\\\n", - " a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &=0 \\\\\n", - "\\end{align}\n", - "$$\n", - "\n", - "Now, if we replace $a_0$ from equation (8) into (9) and rearrange terms:\n", - "\n", - "$$\n", - "\\begin{align*}\n", - " (\\bar{y} - a_1\\bar{x})\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &= 0 \\\\ \n", - "\\end{align*}\n", - "$$\n", - "\n", - "Replacing the definitions of the mean values into the equation, \n", - "\n", - "$$\n", - "\\begin{align*}\n", - " \\left[\\frac{1}{n+1}\\sum_{i=0}^{n} y_i - \\frac{a_1}{n+1}\\sum_{i=0}^{n} x_i \\right]\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &= 0 \\\\ \n", - " \\frac{1}{n+1}\\sum_{i=0}^{n} y_i \\sum_{i=0}^{n} x_i - \\frac{a_1}{n+1}\\sum_{i=0}^{n} x_i \\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &= 0 \\\\ \n", - "\\end{align*}\n", - "$$\n", - "\n", - "Leaving everything in terms of $\\bar{x}$, \n", - "\n", - "$$\n", - "\\begin{align*}\n", - " \\sum_{i=0}^{n} y_i \\bar{x} - a_1\\sum_{i=0}^{n} x_i \\bar{x} + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i = 0 \n", - "\\end{align*}\n", - "$$\n", - "\n", - "Grouping the terms that have $a_1$ on the left-hand side and the rest on the right-hand side:\n", - "\n", - "$$\n", - "\\begin{align*}\n", - " a_1\\left[ \\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_i \\bar{x}\\right] &= \\sum_{i=0}^{n} x_iy_i - \\sum_{i=0}^{n} y_i \\bar{x} \\\\\n", - " a_1 \\sum_{i=0}^{n} (x_{i}^2 - x_i \\bar{x}) &= \\sum_{i=0}^{n} (x_iy_i - y_i \\bar{x}) \\\\\n", - " a_1 \\sum_{i=0}^{n} x_{i}(x_{i} -\\bar{x}) &= \\sum_{i=0}^{n} y_i(x_i - \\bar{x}) \n", - "\\end{align*}\n", - "$$\n", - "\n", - "Finally, we get that:\n", - "\n", - "$$\n", - "\\begin{align}\n", - " a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})}\n", - "\\end{align}\n", - "$$\n", - "\n", - "Then our coefficients are:\n", - "\n", - "$$\n", - "\\begin{align}\n", - " a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})} \\quad , \\quad a_0 = \\bar{y} - a_1\\bar{x}\n", - "\\end{align}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Let's fit!\n", - "\n", - "Let's now fit a straight line through the temperature-anomaly data, to see the trend over time. We'll use least-squares linear regression to find the slope and intercept of a line \n", - "\n", - "$$y = a_1x+a_0$$\n", - "\n", - "that fits our data.\n", - "\n", - "In our case, the `x`-data corresponds to `year`, and the `y`-data is `temp_anomaly`. To calculate our coefficients with the formula above, we need the mean values of our data. Sine we'll need to compute the mean for both `x` and `y`, it could be useful to write a custom Python _function_ that computes the mean for any array, and we can then reuse it.\n", - "\n", - "It is good coding practice to *avoid repeating* ourselves: we want to write code that is reusable, not only because it leads to less typing but also because it reduces errors. If you find yourself doing the same calculation multiple times, it's better to encapsulate it into a *function*. \n", - "\n", - "Remember the _key concept_ from [Lesson 1](http://go.gwu.edu/engcomp1lesson1): A function is a compact collection of code that executes some action on its arguments. \n", - "\n", - "Once *defined*, you can *call* a function as many times as you want. When we *call* a function, we execute all the code inside the function. The result of the execution depends on the *definition* of the function and on the values that are *passed* into it as *arguments*. Functions might or might not *return* values in their last operation. \n", - "\n", - "The syntax for defining custom Python functions is:\n", - "\n", - "```python\n", - "def function_name(arg_1, arg_2, ...):\n", - " '''\n", - " docstring: description of the function\n", - " '''\n", - " \n", - "```\n", - "\n", - "The **docstring** of a function is a message from the programmer documenting what he or she built. Docstrings should be descriptive and concise. They are important because they explain (or remind) the intended use of the function to the users. You can later access the docstring of a function using the function `help()` and passing the name of the function. If you are in a notebook, you can also prepend a question mark `'?'` before the name of the function and run the cell to display the information of a function. \n", - "\n", - "Try it!" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "?print" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the `help` function instead:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on built-in function print in module builtins:\n", - "\n", - "print(...)\n", - " print(value, ..., sep=' ', end='\\n', file=sys.stdout, flush=False)\n", - " \n", - " Prints the values to a stream, or to sys.stdout by default.\n", - " Optional keyword arguments:\n", - " file: a file-like object (stream); defaults to the current sys.stdout.\n", - " sep: string inserted between values, default a space.\n", - " end: string appended after the last value, default a newline.\n", - " flush: whether to forcibly flush the stream.\n", - "\n" - ] - } - ], - "source": [ - "help(print)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define a custom function that calculates the mean value of any array. Study the code below carefully. " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def mean_value(array):\n", - " \"\"\" Calculate the mean value of an array \n", - " \n", - " Arguments\n", - " ---------\n", - " array: Numpy array \n", - " \n", - " Returns\n", - " ------- \n", - " mean: mean value of the array\n", - " \"\"\"\n", - " sum_elem = 0\n", - " for element in array:\n", - " sum_elem += element # this is the same as sum_elem = sum_elem + element\n", - " \n", - " mean = sum_elem / len(array)\n", - " \n", - " return mean\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you execute the cell above, the function`mean_value()` becomes available to use on any argument of the correct type. This function works on arrays of any length. We can try it now with our data." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1948.0\n" - ] - } - ], - "source": [ - "year_mean = mean_value(year)\n", - "print(year_mean)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0526277372263\n" - ] - } - ], - "source": [ - "temp_anomaly_mean = mean_value(temp_anomaly)\n", - "print(temp_anomaly_mean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Neat! You learned how to write a Python function, and we wrote one for computing the mean value of an array of numbers. We didn't have to, though, because NumPy has a built-in function to do just what we needed: [`numpy.mean()`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.mean.html).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Exercise \n", - "\n", - "Calculate the mean of the `year` and `temp_anomaly` arrays using the NumPy built-in function, and compare the results with the ones obtained using our custom `mean_value` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have mean values, we can compute our coefficients by following equations (12). We first calculate $a_1$ and then use that value to calculate $a_0$.\n", - "\n", - "Our coefficients are:\n", - "\n", - "$$\n", - " a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})} \\quad , \\quad a_0 = \\bar{y} - a_1\\bar{x}\n", - "$$ \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We already calculated the mean values of the data arrays, but the formula requires two sums over new derived arrays. Guess what, NumPy has a built-in function for that: [`numpy.sum()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html). Study the code below." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "a_1 = numpy.sum(temp_anomaly*(year - year_mean)) / numpy.sum(year*(year - year_mean)) " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0103702839435\n" - ] - } - ], - "source": [ - "print(a_1)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "a_0 = temp_anomaly_mean - a_1*year_mean" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-20.1486853847\n" - ] - } - ], - "source": [ - "print(a_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Exercise\n", - "\n", - "Write a function that computes the coefficients, call the function to compute them and compare the result with the values we obtained before. As a hint, we give you the structure that you should follow:\n", - "\n", - "```python\n", - "def coefficients(x, y, x_mean, y_mean):\n", - " \"\"\"\n", - " Write docstrings here\n", - " \"\"\"\n", - "\n", - " a_1 = \n", - " a_0 = \n", - " \n", - " return a_1, a_0\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now have the coefficients of a linear function that best fits our data. With them, we can compute the predicted values of temperature anomaly, according to our fit. Check again the equations above: the values we are going to compute are $f(x_i)$. \n", - "\n", - "Let's call `reg` the array obtined from evaluating $f(x_i)$ for all years." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "reg = a_0 + a_1 * year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the values of our linear regression, we can plot it on top of the original data to see how they look together. Study the code below. " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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FzMzOq9qmTMmhudnXqXF1U1P3CubepKbaSE21sWtXEy+9dJjf/GYHWVnd3+9E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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 5))\n", - "\n", - "plt.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1, alpha=0.5) \n", - "plt.plot(year, reg, 'k--', linewidth=2, label='Linear regression')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Land temperature anomaly [°C]')\n", - "plt.legend(loc='best', fontsize=15)\n", - "plt.grid();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Apply regression using NumPy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Above, we coded linear regression from scratch. But, guess what: we didn't have to because NumPy has built-in functions that do what we need!\n", - "\n", - "Yes! Python and NumPy are here to help! With [`polyfit()`](https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.polyfit.html), we get the slope and $y$-intercept of the line that best fits the data. With [`poly1d()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.poly1d.html), we can build the linear function from its slope and $y$-intercept.\n", - "\n", - "Check it out:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# First fit with NumPy, then name the coefficients obtained a_1n, a_0n:\n", - "a_1n, a_0n = numpy.polyfit(year, temp_anomaly, 1)\n", - "\n", - "f_linear = numpy.poly1d((a_1n, a_0n)) " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0103702839435\n" - ] - } - ], - "source": [ - "print(a_1n)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-20.1486853847\n" - ] - } - ], - "source": [ - "print(a_0n)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "0.01037 x - 20.15\n" - ] - } - ], - "source": [ - "print(f_linear)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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FzMzOq9qmTMmhudnXqXF1U1P3CubepKbaSE21sWtXEy+9dJjf/GYHWVnd3+9E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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 5))\n", - "\n", - "plt.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1, alpha=0.5) \n", - "plt.plot(year, f_linear(year), 'k--', linewidth=2, label='Linear regression')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Land temperature anomaly [°C]')\n", - "plt.legend(loc='best', fontsize=15)\n", - "plt.grid();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## \"Split regression\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you look at the plot above, you might notice that around 1970 the temperature starts increasing faster that the previous trend. So maybe one single straight line does not give us a good-enough fit.\n", - "\n", - "What if we break the data in two (before and after 1970) and do a linear regression in each segment? \n", - "\n", - "To do that, we first need to find the position in our `year` array where the year 1970 is located. Thankfully, NumPy has a function called [`numpy.where()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) that can help us. We pass a condition and `numpy.where()` tells us where in the array the condition is `True`. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([90]),)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numpy.where(year==1970)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To split the data, we use the powerful instrument of _slicing_ with the colon notation. Remember that a colon between two indices indicates a range of values from a `start` to an `end`. The rule is that `[start:end]` includes the element at index `start` but excludes the one at index `end`. For example, to grab the first 3 years in our `year` array, we do:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1880., 1881., 1882.])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "year[0:3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we know how to split our data in two sets, to get two regression lines. We need two slices of the arrays `year` and `temp_anomaly`, which we'll save in new variable names below. After that, we complete two linear fits using the helpful NumPy functions we learned above." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "year_1 , temp_anomaly_1 = year[0:90], temp_anomaly[0:90]\n", - "year_2 , temp_anomaly_2 = year[90:], temp_anomaly[90:]\n", - "\n", - "m1, b1 = numpy.polyfit(year_1, temp_anomaly_1, 1)\n", - "m2, b2 = numpy.polyfit(year_2, temp_anomaly_2, 1)\n", - "\n", - "f_linear_1 = numpy.poly1d((m1, b1))\n", - "f_linear_2 = numpy.poly1d((m2, b2))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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7HGMLmq7qS+B7wHHArkbXBwN+YGMnxCSEEEL0eMGgxmRqvySqpsYbnjcIUFSU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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 5))\n", - "\n", - "plt.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1, alpha=0.5) \n", - "plt.plot(year_1, f_linear_1(year_1), 'g--', linewidth=2, label='1880-1969')\n", - "plt.plot(year_2, f_linear_2(year_2), 'r--', linewidth=2, label='1970-2016')\n", - "\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Land temperature anomaly [°C]')\n", - "plt.legend(loc='best', fontsize=15)\n", - "plt.grid();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have two different curves for two different parts of our data set. A little problem with this and is that the end point of our first regression doesn't match the starting point of the second regression. We did this for the purpose of learning, but it is not rigorously correct. We'll fix in in the next course module when we learn more about different types of regression. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## We learned:\n", - "\n", - "* Making our plots more beautiful\n", - "* Defining and calling custom Python functions\n", - "* Applying linear regression to data\n", - "* NumPy built-ins for linear regression\n", - "* The Earth is warming up!!!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "1. [_Essential skills for reproducible research computing_](https://barbagroup.github.io/essential_skills_RRC/) (2017). Lorena A. Barba, Natalia C. Clementi, Gilbert Forsyth. \n", - "2. _Numerical Methods in Engineering with Python 3_ (2013). Jaan Kiusalaas. Cambridge University Press.\n", - "3. _Effective Computation in Physics: Field Guide to Research with Python_ (2015). Anthony Scopatz & Kathryn D. Huff. O'Reilly Media, Inc.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Execute this cell to load the notebook's style sheet, then ignore it\n", - "from IPython.core.display import HTML\n", - "css_file = '../style/custom.css'\n", - "HTML(open(css_file, \"r\").read())" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - }, - "widgets": { - "state": {}, - "version": "1.1.2" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/notebooks/.ipynb_checkpoints/04_Stats_and_Montecarlo-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/04_Stats_and_Montecarlo-checkpoint.ipynb new file mode 100644 index 0000000..7e22206 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/04_Stats_and_Montecarlo-checkpoint.ipynb @@ -0,0 +1,958 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Created under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2020 R.C. Cooper" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Statistics and Monte-Carlo Models\n", + "\n", + "Monte Carlo models use random numbers to either understand statistics or generate a solution [1]. \n", + "The main element in a Monte Carlo model is the use of random numbers. Monte Carlo methods are very useful if you can easily execute a function lots of time or even in parallel. \n", + "\n", + "We can generate random numbers in many ways, but most programming languages have 'pseudo'-random number generators. \n", + "\n", + "In Python, we use the numpy library as such\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "x=np.random.rand(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "#Import rcParams to set font styles\n", + "from matplotlib import rcParams\n", + "\n", + "#Set font style and size \n", + "rcParams['font.family'] = 'sans'\n", + "rcParams['font.size'] = 16\n", + "rcParams['lines.linewidth'] = 3" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([3., 1., 0., 0., 1., 0., 0., 2., 1., 2.]),\n", + " array([0.34837708, 0.41148652, 0.47459596, 0.53770539, 0.60081483,\n", + " 0.66392427, 0.72703371, 0.79014314, 0.85325258, 0.91636202,\n", + " 0.97947146]),\n", + " )" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(x,bins=10, color='b', histtype='bar', edgecolor='w')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `numpy.random.rand(10)` function generates 10 random numbers between 0 and 1. The pyplot function `hist` then displays a histogram of these randomly generated numbers. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "\n", + "Try generating more random numbers and plotting histograms of the results i.e. increase `10` to larger values. \n", + "\n", + "What should the histogram of `x` look like if Python is generating truly random numbers?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Examples of Monte Carlo models:\n", + "\n", + "Monte Carlo models have a wide array of applications. We are going to use Monte Carlo models in later modules to explore how uncertainty in measurements can be incorporated into computational models. The three main applications for Monte Carlo models are used in three main classes: optimization, numerical integration, and generating population distributions [1]. \n", + "\n", + "Here is a brief list of Monte Carlo model use cases in real-world applications:\n", + "\n", + "- [Eigenvlaues in supercritical systems](https://link.springer.com/chapter/10.1007%2FBFb0049064)\n", + "- [average time between failures for reliability](http://www.egr.msu.edu/~mitraj/research/pubs/proc/singh-mitra_em_stdby_ias95.pdf)\n", + "- disordered materials (physics)\n", + "- [Calculation of the energy output of a wind farm](http://www.mdpi.com/1996-1073/9/4/286/pdf)\n", + "- [US Coast Guard rescue missions](https://en.wikipedia.org/wiki/Search_and_Rescue_Optimal_Planning_System)\n", + "- [Radiation shielding](http://www.sciencedirect.com/science/article/pii/S0920379612000580)\n", + "- [Predict number of asteroids that hit body of water](https://cneos.jpl.nasa.gov/sentry/intro.html)\n", + "- [Financial modeling](https://en.wikipedia.org/wiki/Monte_Carlo_methods_in_finance)\n", + "\n", + "We will explore Monte Carlo modeling through the use of three examples:\n", + "\n", + "1. Calculate the value of $\\pi$\n", + "\n", + "2. Calculate the integral of a function\n", + "\n", + "3. Propagate uncertainty in manufacturing into uncertainty in failure load" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "\n", + "# Example 1: Calculate $\\pi$ with random numbers. \n", + "\n", + "Assuming we can actually generate random numbers (a topic of philosophical and heated debates) we can populate a unit square with random points and determine the ratio of points inside and outside of a circle.\n", + "\n", + "![Unit circle and unit square](../images/MonteCarloPi.gif)\n", + "\n", + "![1/4 Unit circle and 1/4 unit square](../images/MonteCarloPi_rand.gif)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "The ratio of the area of the circle to the square is:\n", + "\n", + "$\\frac{\\pi r^{2}}{4r^{2}}=\\frac{\\pi}{4}$\n", + "\n", + "So if we know the fraction of random points that are within the unit circle, then we can calculate $\\pi$\n", + "\n", + "(number of points in circle)/(total number of points)=$\\pi/4$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.61262684, 0.8262925 , 0.59955407, 0.43229944, 0.66224665,\n", + " 0.61154899, 0.34189469, 0.94126304, 0.41459117, 0.6400169 ])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.rand(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "def montecarlopi(N):\n", + " '''Create random x-y-coordinates to and use ratio of circle-to-square to \n", + " calculate the value of pi\n", + " i.e. Acircle/Asquare = pi/4\n", + " Arguments\n", + " ---------\n", + " N: number of random points to produce between x=0-1 and y=0-1\n", + " \n", + " Returns\n", + " -------\n", + " our_pi: the best prediction of pi using N points\n", + " '''\n", + " \n", + "\n", + " x=np.random.rand(N,1);\n", + " y=np.random.rand(N,1);\n", + " R=np.sqrt(x**2+y**2); # compute radius\n", + " num_in_circle=sum(R<1);\n", + " total_num_pts =len(R);\n", + " our_pi = 4*num_in_circle/total_num_pts;\n", + " return our_pi" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean value for pi = 3.188800\n", + "standard deviation is 0.041097\n", + "actual pi is 3.141593\n" + ] + } + ], + "source": [ + "test_pi=np.zeros(10)\n", + "for i in range(0,10):\n", + " test_pi[i]=montecarlopi(1000);\n", + "\n", + "print('mean value for pi = %f'%np.mean(test_pi))\n", + "print('standard deviation is %f'%np.std(test_pi))\n", + "print('actual pi is %f'%np.pi)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercises\n", + "\n", + "1. Why is there a standard deviation for the value of $\\pi$ calculated with a Monte Carlo method? Does it depend upon how many times you run the function i.e. the size of `test_pi`? or the number of random points `N`? Alter the script above to discover correlations\n", + "\n", + "2. How well does our function `montecarlopi` converge to the true value of $\\pi$ (you can use `np.pi` as a true value)? Plot the convergence as we did in [03-Numerical_error](https://github.uconn.edu/rcc02007/CompMech01-Getting-started/blob/master/notebooks/03-Numerical_error.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example 2: Calculate the integral of a function\n", + "\n", + "One way to calculate the integral of a function, $f(x)$, is to approximate it as a Riemann sum as such\n", + "\n", + "$I=\\int_{a}^{b}f(x) dx \\approx \\Delta x \\left(f(a)+f(a+\\Delta x)+f(a+2\\Delta x)+...+f(b)\\right))$.\n", + "\n", + "Another way to integrate this function is with a [Monte Carlo approach](https://en.wikipedia.org/wiki/Monte_Carlo_integration) [2, 3]. We can approximate the integral as such\n", + "\n", + "$I=\\int_{a}^{b}f(x) dx \\approx \\frac{1}{n}\\sum_{i=1}^n f(x_i)$. \n", + "\n", + "where $x_i$ are uniformly random values of $x$ over the interval $[a,b]$. We were actually doing this in two dimensions in Example #1 when we compared the area of the circle to the area of a square. \n", + "\n", + "Visually, this approximation can be represented by the following figure.\n", + "\n", + "![Integration approximation](../images/integrals.png)\n", + "\n", + "The figure above shows the exact integral on the left as compared to two approximations, the Riemann sum (top-right) and Monte Carlo method (bottom-right). \n", + "\n", + "The main benefit in using a Monte Carlo integration over a Riemann sum (or another non-random method e.g. trapezoidal) is that the integration converges as \n", + "\n", + "$error \\approx \\frac{1}{\\sqrt{n}}$\n", + "\n", + "independent of the number of dimensions in the formula. Non-random methods have convergence rates that depend upon the step size along each axis, so with two dimensions reducing the step size to 1/3 increases n by 4, in three dimensions its 8, in 4D its 16, and so on. We'll do an example with a 1D function to introduce the concept. \n", + "\n", + "You can even integrate functions that are not easy to evaluate, such as the [area of light shown by the bat signal](http://leios.github.io/Batman_Montecarlo)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise (Discussion)\n", + "\n", + "Why does the Monte Carlo method work? Is there a benefit to random numbers as opposed to using an ordered set divided into equally-spaced intervals?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we will calculate the integral $I=\\int_{1}^{3}f(x)dx$ where\n", + "\n", + "$I=\\int_{1}^{3}\\frac{x}{1+x^2}dx \\approx \\frac{1}{n}\\sum_{i=1}^n \\frac{x_i}{1+x_i^2}$\n", + "\n", + "Our first objective is to take random numbers between 0 and 1 and turn them into random number between 1 and 3. This process is called _scaling_ or _transforming_ our variable $x_{i}$" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ri=np.random.rand(100)\n", + "\n", + "xi=1+(3-1)*ri # scaling equation for our uniform distribution 0..1 to 1..3\n", + "\n", + "plt.hist(xi,label='x_i: our scaled var')\n", + "plt.hist(ri,label='r_i: our original var')\n", + "plt.legend();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "\n", + "Compare the scaled histogram to the original histogram. What is similar? What is different?\n", + "\n", + "Make a scaling equation to get uniformly random numbers between 10 and 20. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performing the integration\n", + "\n", + "If you have been doing a lot of calculus lately, you may know that \n", + "\n", + "$\\int\\frac{x}{1+x^2}dx = \\frac{\\log({x^2+1})}{2}$. \n", + "\n", + "If not, Python's [Sympy: Symbolic algebra](https://docs.sympy.org/1.5.1/modules/integrals/integrals.html) can help you jog your memory [4]. It will also give us an exact solution to judge our convergence. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\log{\\left(x^{2} + 1 \\right)}}{2}$" + ], + "text/plain": [ + "log(x**2 + 1)/2" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sympy\n", + "\n", + "x=sympy.Symbol('x')\n", + "\n", + "sympy.integrate(x/(1+x**2),x)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle 0.80471895621705$" + ], + "text/plain": [ + "0.804718956217050" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sympy.integrate(x/(1+x**2),(x,1,3)).evalf()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So the exact integral that we are approximating is\n", + "\n", + "$I=\\frac{\\log{10}}{2}-\\frac{\\log{2}}{2}=0.80471895621705$\n", + "\n", + "_Note: We won't be using sympy for general numerical methods. It is helpful to do some algebra for us, but in general we want to use arrays so that our solutions can be scaled up and solved quicker by the computer._" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7824846191561594\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'f(x)')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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3Qo/izu+9EeVlprkCd4vBXZmN5J1nfRe3DtLd8iIiV+EOblK8dXiyMvu+XvBex3q1h3S8bbB6VgtQ1a9w55sbAO+FqwCISGUR+S/HE8IJ253ndnLeWpLmbdz+6G7vlqe0WEpw/BaSWG0D4ewAymdwAPICbv/9soiEnntGRBqm7ee88+1zcfu5biGjjiD71/FswWu99A6q05ZZGbcfCCe7+xxfvotIV8QwXNV5KLBSXKfwS3BHvhWB1riN+w8gO73kvIhLpAtE5ANvGRfhmghOuDo5yAJgkXeOKe0+1yrAw6r6SzaWnyVVXSgiw3BJaIWITMR9ztNwV7E9DTynqr+LyIe4Hdt3IjLdG6cH7n7H3tGM6yS8hLvY5TUR6YqryXbBNfOEXjDUBPjQu+BnKe4HUAN3HnQ/8ErQuNNxO/JXROR9b/hPYa4kDOcV3IU3E0XkXdw5jvbeslJwpyKCzcVtc7d5B2HbgN2qOoqM3YhLdm97F02sxCWOC3C3cdwTQZzZoqrHRGQQ7vzRNG873Yg7Sm6DO9AZmckscrLshUR+FD7Mi+kJETkX97uqi9uGt+PWXU5Mx3XL+K6IfIa7Snqmqs5U1U9F5HHcgecqEfkcdxtMEu5WrNbAlZx4UVE4A3D7sl7AWm9ev3plDXFNxgm4e9XBdZV4D/Ci97k34K6ubY27eC80aWSbqq4SkfuBJ4GfROR/HL+3sgbwcpgryWNpOu4zThKRWbjTBp+o6k+4uxnOAa4GOonINI7f+3sW7mKfNhw/Z3oz7j7XD71tO+0+1+q4DiIaRxqU91t5BXeA96OIfII7H3+Jt4xwd3NkuF1lsAx/vous7tXR9PcQtQJewzU97fM+1EbcBnkDXs89euL9QgMymJ/gEvYy3NHLelyyLUn4exbHefOrBTyI2zkewjVr3BRm/iMIuTc0s2GZxYtrFv6M4z0vrcX9SBsGjVMSV8v/jeO9M92K+wIVr0eSoPFP+IyZrPtw8XYiwvtcgz7D994624w7uDlhXeMu9HgCd4/bFu+zrMadIjgzzHzvw+3MjgR/ToJ6aMrkc3XBnTfbg9uhfwDUIYP7Z3EHKz9424uGxB12feIusHiD9L2AvQhUCDNu2HWX1bAMxm+Ku7Btu7fcVbij8VPDjJtCDu5zPdlxcS0EL3rr5LC3jt4g/H2gGW6v4YbhWkqewf2uUzPYVi/C7T+2ecvfiGttuJMwPWhl8RkvxtVi13vb+D7czv5Zgu5z98Zthus4YyfuoO5z3J0BI4jwdxbJcNwFgTNxF+rt97bd67PxmcLOO7vbKa7pdaz3/R4lzH4Od8vKdG+dHPK2ic9xB1klQsZNxl2EuM8bfyLu9/4T7oA3eNwT1mnI8MK4+85X4fY1v3rTFM7gs2S4XUXjuyDzPJDhsNC/tBvO8wQRGYe7H6mGhunf2BhjjD+8c7ZbcLfstfQ7Hr/l6+e5GmOMiS4ROUVE/hJSloBrdi2G6y6wwMu3j6EyxhgTE6WBDSLyBe4ahhK4uzUa4U7RZXVBaoFgydUYY0x27MXdAtUFd7FYEdz5z+eAR9R1K1jg5alzrsYYY0xeYOdcjTHGmCiz5GqMMcZEmSVXY4wxJsosuRoTRERURGLS56uI9BeRH0Rkv4hsF5EPMuhmMqPpB3jxZfY3Nsx054nIZyKywVv2LyLyqrjH/QWPd4qI9BaRN0RkhTfubhGZKSLXZBDTiExisQtbTIFlVwsbkwvk+CMZ1+AeAl0W97DyriLSVl03dFlZyPG+UEMNwD02LN1zh0XkNtxVnNtxj/TbgbtlYhDQx1v2Ym/0Wrgejv7EPRP2I9zzdC8Dxnvjhj6/Oc14TuyqMNp9NhuTZ9jVwsYEERHFPdw9pw+yDp5nfVxf3CuBVuo9Dcfr1P9b3BNB2udg/mVx/UPvA05T7+Hn3oMZtuG6iGukqn8ETTME16fsOFUd6JVVAboD4zX9IxOTcE+xqe7FPz9o2AjcQcO5qppysp/BmPzGmoWNib0BuMd9/UuDHjOnqrNxjz1rl53m4TCuwt1r+HZaYvX8Bden7I/BidWT9mCFwFNCVHWjqr6iIY9MVPds3zHe23CP7TLGhLBmYWNiL+2ZkV+GGfY57mkuHXC925yMQd7rayHlm3HNwc1EpKKqBj8o+mLvdVqEyzjivaZmMLy9iLTEdQq/HPg6JNEbU6BYcjUmC95zRQdkY5K1qjou6H0dYI+qbgkzbtojEmuHGRZJbE1wT3j5UVV/DB6mquqdcx2He9TWh7gnmDTEPXLvVY4/VzWzZSTiasfgnoQSzsMh738XkatVNdLkbUy+YsnVmKxVx51XjNQ3uISWphTHn4UZ6k/v9YQHhkfoWu81tNYKgKq+JSLbgLdwj4VMMw94Q1WPhJsuxEO4x+eNV9UlIcMW4p5U9Q2upnw67kKt+4EpItJCVZdG+mGMyS/snKsxWVDVFFWVbPx1CjebaMclIoVxDxQ/hEue4ca5Dnde93XcQcKpuAdjC+5B7pk+GFxErsY9O3kxcEvocFX9SFXfUNXfVPWgqq5S1UeB23BPSLn3JD+eMXmaJVdjYu9PMq6ZlvJed5/EfHvhLlr6SFV3hg70LpIaBUxW1bu9BLhPVefgHjp/BHgio5mLyF9xSXkF0DX4YqwIjMedn22bjWmMyTesWdiYLEThnOsvQGsRqRDmvGsd73XVSYSWdiHTCR1HeLrifuMpoQNUdbOILAeaikhhVU13T6qIXAq8A6wGumRwvjhDqnpYRPYAxbMznTH5hSVXY7JWnZydc/0GaI1LdqHNtxd4r99mJyAROd2b3zpchw/hFPFey2cwPAk4RsgVwCLSA3jXm/e5YW7jiSS+2riOMuZld1pj8gNrFjYmC1E45zoOd4vKcBEpmVbodSLRHZilqiuCyk8RkfoiUiuTsAbgfr/jVPVYBuPM9l5vEJHKwQO8c7FVgRnB03vnYP8H/I5LrBszCkBECotIcpjyMsB/vLcTMvkMxuRb1kOTMUFi0UOTN98RHO/+cCJQBuiHS7rpuj/0mqHXAL+pavUw8xJcU3NNoKaqrs1kuROAPrhzuh8CW3FX/nYF9gMdVfV7b9z6uKt/iwBveDGEWqiqH3njl8Hd2rPQ+9sMnAZchKstfwb0iPCKZGPyFWsWNiYXqOoIEVkL3AoMBQ7gOpAYrqrLszm7Drh+gKdlllg9VwEzgGuAvwJFcbcFvQU8pqrLgsatxPGm5LAd9eMuVPrI+/8A8Dyuybsbrhl4H/AT8F/gP5nUqo3J16zmaowxxkSZnXM1xhhjosySqzHGGBNlllyNMcaYKLPkaowxxkSZXS0conz58lq9enW/wzDGmDzlhx9+2KaqSVmPWTBYcg1RvXp1vv/+e7/DMMaYPEVEfvM7hnhizcLGGGNMlFlyNcYYY6LMkqsxxhgTZZZcjTHGmCiL2+QqIlVF5H0R2S0if4rIRBE5I8JpNYO/qHbGbowxxoQTl1cLi0hxYBpwCOgPKPAoMF1EzlLVfRHMZhwwOqRsZTTjNMYYY8KJy+QKXI97nFY9VV0FICKLcY/ZGgw8G8E8Nqrq3NiFaIwxxoQXr8m1BzA3LbECqOoaEZkF9CSy5GrixNGjR/nll19YuHAhixYtYvHixezatYuSJUtSqlSpdK/ly5enZcuWNG/enMKFC/sdujHGnJR4Ta4NgUlhypcCl0c4jxtF5G7cw6jnAv9Q1RlRis9kYdmyZYwaNYr58+fz008/ceDAgWxNX6xYMVq1akX79u1p164dbdq0oWTJkjGK1hhjoitek2s5YGeY8h24BzJn5b/AFOB3oBpwNzBNRLqqakroyCJyA3ADwBlnRHTNlMnAggUL+Ne//sXEiRNzNJ8DBw6QkpJCSkoKAImJiXTt2pUBAwbQs2dPihYtGoVojTEmNuLyYekichh4RlXvCyn/F3CPqmbroEBESgJLgPWq2i6zcZOTk9W6P8y+2bNn869//YtPP/007PDKlSvTtGlTmjRpQpMmTahcuTJ79+5lz549gb8///yT1atXM3PmTNasWZPhssqUKUO/fv0YOHAgycnJiEisPpYxJkIi8oOqJvsdR7yI15rrTlztNVRZwtdoM6Wqe0TkE+DanAZm0lu8eDG3334706dPP2FY9+7duemmmzj77LNJSspef94bN25k5syZzJgxg5kzZ7Jo0aLAsF27djFq1ChGjRpFgwYNGDJkCNdeey3FixfP8ecxxphoiNea6zSgcGgtU0RScDF3PIl5jgIGqmqm7YlWc43cxx9/TL9+/di/f3+gTES4/PLLuf/++2nSpEnUlrV27VreeOMNxo0bF7ZWm5SUxJ133smNN95IqVKlorZcY0xkrOaaXrx2IvEx0FpEaqYViEh1oK03LFtEpBTQDZgXpfgKNFXlmWeeoVevXoHEmpiYyDXXXMOyZct49913o5pYwT2t6KGHHmLVqlWkpKTQv3//dDXVrVu3cu+991K9enVGjBjBjh07orp8Y4zJjnituZYAFgEHgAdwnUg8ApQEzlLVvd541YBfgYdV9WGv7C6gHjCd4xc0pZV1yeqKYau5Zu7IkSPcfPPNjBkzJlBWo0YNJk+eTMOGDXM1lj179jB+/HhGjhzJ+vXr0w079dRTGTp0KPfeey9lypTJ1biMKYis5ppeXNZcvR6YOuN6VHoTeAtYA3ROS6weARJJ/zl+BhoAzwNf4u6JXQO0s1txcmbXrl1cdNFF6RJr27ZtmTdvXq4nVoCSJUty8803s2rVKl599VVq164dGLZ3716efPJJateuzQsvvMCRI0dyPT5jTMEVlzVXP1nNNbzVq1fTrVs3VqxYESi76qqrGDt2LEWKFPExsuNSU1N57733eOyxx1i6dGm6YXXr1mXkyJH06NHDri42Jgas5ppeXNZcTXzZvHkzHTp0SJdYH374Yd588824SawAhQoV4sorr2Tx4sVMmDCB6tWrB4atXLmSXr160blzZ3744Qf/gjTGFAiWXE2mUlNT6du3Lxs3bgSgSJEivPPOOzz44INxWwNMSEigT58+LF++nJEjR1K6dOnAsJSUFFq0aMHgwYPtoidjTMxYcjWZGj58eKCXJBHho48+om/fvv4GFaGiRYty9913s2rVKm6++WYSExMBd7XzmDFjqFevHuPGjcNOjRhjos2Sq8nQhx9+yMiRIwPv//nPf3LhhRf6GNHJKV++PC+88AJLly6lW7dugfJt27YxcOBAOnbseMI5WmOMyQlLriaslStX0r9//8D7iy++mOHDh/sYUc7Vq1ePyZMn8+GHH1K1atVA+YwZM2jatCnDhg1j375IHhVsjDGZs+RqTrBv3z569+7Nnj17ANeBw5tvvklCQt7fXESEXr16sXz5coYNG0ahQq4H0NTUVJ566ikaNWrEV1995XOUxpi8Lu/vLU1UqSqDBw9myZIlgLuA6YMPPqBcuXBdPeddJUqU4Mknn+THH3+kffv2gfK1a9fStWtXBg0axM6d2e7G2hhjALvP9QQF/T7Xl19+maFDhwbejx07lkGDBvkYUXQ9M+LEMlXl+0Xj+fjzv3Pg4PGEWvLUSlx28Us0PvOyQNmdYaY3xth9rqGs5moCli1bxu233x54f+211+arxJoREaFF0wEMG7qcsxpcHijfs3cT49/rzfj3evPnnj98jNAYk9dYcjUBw4YNC3QT2KxZM1588UWfI8pdJU+tyDWXv8eAPh9S6tTKgfKflk/kqZcbsmDxW3bbjjEmIvH6PFeTy77++ms++eQTwNXkXn/9dYoWzfTpfPnG7JTQkl50aNKJJb/ezdpNrwJw4OBO3v7wbyw68z16XzKaUqdWAqyZ2BgTntVcDceOHeOuu+4KvB8wYEDUHxmX1xQuVIbm9f5Du7O+onjR6oHypT9/zFMvNbBarDEmU5ZcDf/9739ZuHAhAMWKFeORRx7xOaL4UaFsF7qcvZgap90YKEurxY5/7zI2bdrkY3TGmHhlybWAO3DgQLrOIe666y6qVKniY0Tx55RCJWlW52XanfUVZUtXC5QvWfERDRs25N133/UxOmNMPLLkWsA999xzbNiwAYAKFSpw9913+xxR/KpQtgt33vgTbc4eEijbsWMHffv2pU+fPmzbtl3DjAcAACAASURBVM3H6Iwx8cSSawG2ZcsWHn/88cD7hx9+mJIlS/oYUfwrWqQkvS8ZxQ1Xf0mZ0mcEyt977z0aNWrExx9/7GN0xph4Ycm1APvnP/8Z6OLwzDPP5Nprr/U5oryjbs3zuOvGn9Kts82bN9OzZ08GDBjArl27fIzOGOM3S64F1IoVKxg9enTg/VNPPRXoZ9dEpmiRUrz66qtMmTKFypWP3xc7fvx4GjduzBdffOFjdMYYP1lyLaDuuecejh49CkDnzp25+OKLfY4o7+rWrRtLlizhyiuvDJRt2LCBCy64gJtuuom9e/f6GJ0xxg+WXAugb7/9NnBuUER4+umnERGfo8rbypUrx1tvvcX7779P+fLlA+WjRo2iSZMmzJgxw8fojDG5zdoBC6Bnnnkm8P/VV19Ns2bNfIwm70v/MIDeDB3Qng+mDGbJio8AWL16NR07dKRDm79zYedHOaXQ8Z6vrIcnY/Inq7kWMGvXrmXy5MmB93n9AejxqGSJCvS/YiL9er1B0SKlAVCUb+Y8w/+Nbs763wvuU5eMKSgsuRYwr7zySqDbvvPPP5+6dev6HFH+JCKc3eRq7rppCXVrnR8o37JtOS+82prPpj1I6tHDPkZojIklS64FyMGDB3n11VcD74Of22pio0yp07n+qs/o3e0VCp9SAoBjepSvZjzKv//TItDtpDEmf7HkWoC8++67bN++HYBq1arRrVs3nyMqGESENsmDufPGxdSs1jFQ/sfmxbRo0YJHHnkk8Kg/Y0z+YMm1AHnppZcC/994440kJib6GE3B85eyNRnSfxo9L3iOUwoVAyA1NZWHHnqINm3asGTJEp8jNMZEiyXXAmL+/Pl89913ABQpUsR6Y/JJgiTQvvVt/H3IQqqd3iZQ/sMPP3D22Wfz2GOPkZqa6mOExphosORaQATXWvv27ZvuXkyT+5L+UpehA2cwcuRIChcuDMDhw4cZPnw4rVu3tlqsMXlc3CZXEakqIu+LyG4R+VNEJorIGVlPecJ87hMRFZGZsYgzL9i6dSsTJkwIvLcLmeJDQkIid999Nz/++CMtWrQIlFst1pi8Ly6Tq4gUB6YB9YH+wNVAHWC6iJTIxnxqAsOBLbGIM68YO3Yshw+72z5atmyZbkdu/NegQQNmz57NE088YbVYY/KJeO2h6XqgJlBPVVcBiMhi4BdgMPBshPMZBbwF1CN+P2tMHT16lFGjRgXeW601fsxOAUakvStEIe7h1uu68+6kgazfOB9wtdimTZrTpcMDdG53L4USXfK1np2MiW9xWXMFegBz0xIrgKquAWYBPSOZgYhcCTQH7otJhHnElClTWLduHQDly5fniiuu8Dkik5lKSQ24edAsLu7yBIleIj167AhfpPyDf49Jtt6djMkj4rU21xCYFKZ8KXB5VhOLSFng/4BhqrqjIHdKH3wh03XXXUfRokUzGdv4ZXZK8LtCFOUezm3WgwU/D2LHnrkA/LHlJ/79n1bUrXo3R478g1NOKRaYwmqyxsSXeK25lgN2hinfAZSNYPqngJXAuEgWJiI3iMj3IvL91q1bIw4y3v388898+eWXACQkJDBkyBCfIzLZUarEmXRsNpPGtZ4lMSEtkR5j5foneXZ0U9asK7DX6BkT9+I1uQJomLIsq6Ai0h64BrhR0zrRzWpBqmNUNVlVk5OSkrIZZvx65ZVXAv93796datWq+RiNORkiidQ5/Q66JP9EUplzA+Vbt6/kpdfb88EnN3Hg4G4fIzTGhBOvyXUnrvYaqizha7TBRgNjgQ0iUkZEyuCavxO990WiG2p8Onr0aLrbb2666SYfozE5dWqxWrQ76yua1RlNocSSgfI534/iqZcbMGlSuLMoxhi/xGtyXYo77xqqAbAsi2nPBIbgknDaX1ugtff/jdELM37NmjWLTZs2AVChQgW6dOnic0Qmp0QSqHHaDZyXvJQz614SKP9zz+/06tWLv/71r/zxxx8+RmiMSROvyfVjoLV3nyoAIlIdlyQ/zmLac8P8LQKWeP+/H/1w48///ve/wP+XXXaZ9SOcjxQvWpVBfT/mb70ncGqJCoHyDz74gDPPPJMxY8Zw7NgxHyM0xsRrcv0PsBaYJCI9RaQH7urh9bhmXwBEpJqIpIrIQ2llqpoS+gfsAnZ77zfk6ifxwbFjx/jggw8C7y+/PMsLrE0eIyI0bdSHYUOX07LpoED57t27GTx4MB07dmTp0qU+RmhMwRaXyVVV9wGdcVf8vonrCGIN0FlV9waNKkAicfo5/DJr1qxA82BSUhIdOnTwOSITK8WLleOKnmP5+uuvqVWrVqB85syZNG3alPvuu4/9+/f7GKExBVPcJiVVXaeqvVW1lKqWVNVeqro2ZJy1qiqqOiKLeXVS1XaxjDeehDYJFyoUr7czm2jp3LkzP/30E/fdd1/g+05NTeWJJ56gYcOGfPrppz5HaEzBYnvdfMaahAuW2SnePyMAivGXwo9x+/VX8cEnQwL3wa5du5Zu3bpxVoO/0vOC5yhdqgpgHU8YE0txW3M1J2f27Nn8/vvvgOvusGPHjj5HZHJbpQoNuXHAN1ze/VWKFzt+R9viZe8z8qX6pMx+mqNHj/gYoTH5nyXXfMaahA24h7K3an4tw4au4Owm1wTKDx3ey5Qv7+bZ0U1JSUnxL0Bj8jnb8+Yjx44d4/33j99pZE3CBdPslOB3SVQrM57iTQay8Jeh7NnvbhPfvHUZ5557Ls0aX0n3rk9TqmRlwJqKjYkWq7nmI3PmzEnXJNypUyd/AzJxI6lMJ7qcvZBGNZ8iMeH4I5F//OltnnyxHt/Medaaio2JIkuu+Uhwk/Cll15qTcImnYSEU6hb9S66tlzB6Ul9AuWHDu9h8hd38swrZ/HFF1/4GKEx+Ycl13zCmoRNpIoXOZ2WDSYw+OqvqFC+fqB8y7YVXHDBBfTq1YvVq1f7GKExeZ8l13xi7ty5bNy4EYC//OUvnHvuuVlMYQq6OjW78Pchi7ik69MUKXz8YQCTJk2iQYMGDB8+nL1792YyB2NMRiy55hPWJGxORqHEwnQ6507uuWUlyU0HBMoPHTrEY489Rr169Rg/frz1VWxMNllyzQesSdjkVKlTK9G35+vMnTuXli1bBsp///13BgwYQIsWLfj22299jNCYvMWSaz4wb948NmxwzyMoV66cNQmbk9aqVSvmzJnDuHHjqFSpUqB8wYIFdOzYkd69e/Prr7/6GKExeYO1HeYDoU3Cp5xyio/RmLxodor3zwhwx9z9uWVgb6bPfJKUOU+TmnoQgIkTJzLpo8m0bXkL53UYHugByu6PNSY9q7nmcarKxIkTA++tSdhES5HCp3Jh50e45+afad74qkD50WNH+Hbuszz+fC1SZj/NES/xGmOOs+Sax/3888/89ttvAJQqVYrOnTv7HJHJb8qWPoMrL/svt1w7l+pVzwmUHzi4iylf3s2TL9bjzTfftIuejAliyTWPC77pv0uXLtYkbGKm2umtGDpwJlf/9T3Kl6sdKN+1ex3XXHMNZ599tnVCYYzHkmseF7wzO//8832MxBQEIkKThpdz101L6XXRC5QonhQYtnDhQi644AK6dOnCvHnzfIzSGP9Zcs3DDh06xPTp0wPvLbma3FIosTDtWt7Mfbeu4rz2D1CsWLHAsGnTptG6dWsuvfRSli5d6mOUxvjHkmseNmfOHPbv3w9ArVq1qFmzps8RmYKmaJFSXNj5EVatWsX1119PYmJiYNhHH31E48aN6d+/P2vXrvUvSGN8YMk1D7MmYRMvTjvtNMaMGcOyZcvo0+f4QwFUlTfeeIO6desydOjQQBedxuR3llzzMEuuJt7UrVuXCRMmsGDBAi666KJA+ZEjR3j55ZepVasWt912G5s2bfIxSmNiz5JrHrV161YWLFgAQGJiovXKZOJKs2bN+PTTT/n2229p27ZtoPzQoUM8//zz1KxZk7vuuoutW7f6GKUxsWPJNY/6+uuvUVUAWrduTenSpX2OyJgTtW/fnhkzZvD555+n67P4wIEDPPPMM9SoUYN77rmHLVu2+BilMdFn3R/mUdYkbPKCZ0YACHA+l1/YlUa1PuXzlIfY+Idrddm3bx8jR47k/559gTbJN9LpnLsoVbIyYF0qmrzNaq55kKpacjV5jojQoG43br/+ewb0+ZDKFc8KDDuSeoBv5z7LY/+uwUdTb2XXnxt8jNSYnLPkmgctX748cNVlmTJlSE5O9jkiYyInIjSq34s7Bv9I/ysmclqlpoFhqUcPMXP+Czz+fC2GDBnC6tWrfYzUmJNnyTUPCu3y0B6MbvKiBEmg8ZmXcscNCxjY92OqntYiMOzo0cOMHj2aOnXqcOWVV7J48WIfIzUm+yy55kHByfWCCy7wMRJjck5EaFivO7deN4/rrppKtdPbBIYdO3aMd955hyZNmtC9e3dmz57tY6TGRM6Sax5z6NAhUlJSAu+7du3qXzDGRJGIUL/2hdw8aBZDrpl2wrY9ZcoU2rZtS4cOHZgyZYo9hcfEtbhNriJSVUTeF5HdIvKniEwUkTMimK6aiEwSkd9E5ICIbBORFBG5KKtp84KZM2dy4MABwN2wX716dX8DMibKRITaNc7liy++4LvvvuOyyy5DRALDZ8yYQffu3WnUqBFjx47l0KFDPkZrTHhxebJORIoD04BDQH9AgUeB6SJylqruy2TyU4FtwAPABqAUcD3wqYj0VtWJmUwb9+wqYVMQzE4BRgAkc07jD6hdeQXTZj3JgsX/5dixVMBd2Hfddddxx+0P0K7lrbRJHkLxYmUBu43H+C9ea67XAzWBXqr6kapOAnoA1YDBmU2oqktV9VpVfVNVp3vT9sIl2oGxDjzWLLmagqhC+fr07fk699+6mo5t7qJI4ZKBYXv2bmLqtPt59P+q8uGnt7B1+y8+RmqME6/JtQcwV1VXpRWo6hpgFtAzuzNT1VRgN3AkahH6YPPmzSxcuBCAQoUK0alTJ38DMiaXlSldle7nP8UDd6yn23kjKV2ySmDY4SP7mPXdi4x8sR49evRg2rRpgV7MjMlt8ZpcGwJLwpQvBRpEMgMRSRCRQiJSSUQeBOoCL0Uxxlz31VdfBf4/55xzKFmyZCZjG5N/FStamnPb3s19t62mb6/xVK7QODBMUSZPnkyXLl1o2rQpr7/+OgcPHvQxWlMQxWtyLQfsDFO+Aygb4TxG4mqqfwDDgL6q+nV0wvOHNQkbk16hxMIkN7mGvw9ZxA1Xf8mZdbqlG7548WIGDRpE1apVuf/++1m3bp1PkZqCJl6TK7iLmEJJmLKMPAe0ALoDU4G3ReSScCOKyA0i8r2IfB+vT+mwLg+NyZiIULfmeVx75RSGDV3BTTfdRPHixQPDt23bxuOPP06NGjXo3bs306dPtyZjE1Pxmlx34mqvocoSvkZ7AlXdoKrfq+oUVb0CmAs8ncG4Y1Q1WVWTk5KSTjroWFq+fHngGZjlypWjefPmPkdkTHyqUL4eL730EuvXr+eJJ56gatWqgWHHjh1j4sSJdO7cmcaNGzNq1Cj+/PNPH6M1+VW8JteluPOuoRoAy05ynt8DtU86Ip/NnDkz8H+HDh1ITEz0MRpj4l+5cuW45557WL16NR9++CGdO3dON3zp0qXcdNNNVKlShSFDhrBo0SKfIjX5Ubwm14+B1iJSM61ARKoDbb1h2SIiCUA74NcoxZfrgpNru3btfIzEmLylUKFC9OrVi6+//jqQUEuUKBEYvnfvXkaPHk3Tpk0555xzeOONNwIdtRhzsuI1uf4HWAtMEpGeItIDmASsB0anjeT1xpQqIg8FlY0QkedFpI+IdBSRPsBnQEvgH7n6KaLIkqsxOdegQQNeeuklNm7cyAsvvECDBulvPpgzZw79+/enSpUq3HbbbSxZEu6mBWOyFrPk6t0C01xE2opIPREpHOm0Xg9MnYGVwJvAW8AaoLOq7g1eDJBI+s+xAGgEvAB8gbtq+CDQXlUn5OQz+WXjxo2sWbMGgGLFitGsWTOfIzImbytdujQ333wzS5Ys4ZtvvqFv376ccsopgeE7d+7k+eefp3HjxrRp04axY8eyd+/eTOZoTHpR7f5QRJKB64ALgaohgw+LyHfAO8BbqprpVQSqug7oncU4awm5glhVP+Ykmo7j2axZswL/t2rVisKFIz5OMcZkQkTo0KEDHTp0YMuWLbz22muMGTMmcDALMHfuXObOncsdd9xBv379GDhwIK1atUrX37ExoaJScxWRZBFJAeYDbYDJuC4MewEXAP1wPYVuA54A1ovIcBEpGo3l53fWJGxM7FWoUIF7772XVatW8eWXX3LFFVekq83u2bOHMWPG0KZNGxo2bMhTTz0VuILfmFDRahb+BlgINFTVJqp6i6q+pqqTVfUrVX1PVZ9U1cuAisANwGXA3VFafr4WXHO15GpMbCUkJHDeeefx7rvvsnHjRp5++mnq1auXbpzly5czbNgwTj/9dLp3787EiRM5fPiwTxGbeBSt5FpLVW9X1eVZjaiqB1X1XVU9GxgTpeXnW3v27An0J5yQkECbNm2ymMIYEy1JSUnceeedLF++nBkzZjBo0KB0VxofPXqUKVOm0Lt3bypXrszQoUOZN2+edVBhopNcVfWk2kZUdXM0lp+fzZ07N/BQ6LPOOotSpUr5HJExBY+I0K5dO8aOHcumTZt4/fXX6dChQ7pxduzYwcsvv0zr1q2pX78+jz76KGvXrvUnYOO7qF8tnNVDyUXEmoKzwc63GhNfTj31VAYMGMA333zDL7/8wvDhwznjjDPSjbNy5UoefPBBatSoQYcOHXjllVfYvn27TxEbP8TiVpxPROT/Qm+98W7N+Qp4LAbLzLcsuRoTv2rXrs2jjz7KmjVrmD59OgMHDjzhaVUzZszgxhtvpFKlSnTv3p0JEyawf/9+nyI2uSUWyXUI7krhuSJSD0BEugOLcY996xKDZeZLR44cYe7cuYH3bdu29TEaY0xGEhIS6NSpE6+99hqbNm3i7bff5sILLyQh4fguNjU1lSlTptCvXz8qVKjA3/72NyZPnsyhQ4d8jNzEStSTq6qOwT2NJhH4QUQmAh8B3wJNVPXbaC8zv1q4cGHgCLd69eqcfvrpPkdkjMlK8eLF6devH1OnTuX333/n+eefp1WrVunG2bdvH2+99RY9evSgYsWKDBo0iM8//5wjR474FLWJtpj00ORdNTwQl2B74TrN76uqET3RxjjWJGxM3laxYkVuueUW5s6dy6pVq3jkkUeoX79+unF2797N66+/zoUXXkjlypUZPHgwX331FampqT5FbaIhJslVRG7A1VRXAPfjuiOcIyJ59qk0fghOrtYkbEzeVqtWLR544AGWLVvGjz/+yL333kv16tXTjbN9+3bGjBlD165dqVSpEtdff73VaPOoWFwt/AHwCvAq0EpVn8B1ml8E+FFEBkZ7mfmRqlrN1Zh8SERo2rQpjz/+OKtXr2bevHn8/e9/p0qVKunG2759O6+++ioXXnghFStWZODAgUyePJmDBw/6FLnJjljUXNsBl3idShwGUNWluPOw43BJ12Rh1apVbNmyBYAyZcqc8PQOY0zeJyK0bNmSZ555hnXr1jFjxgxuu+22ExLtzp07GTduHD169CApKYkrrriCd955xx70HsdikVybqOqnoYWqekhVbwF6xGCZ+U5ok3DwVYfGmPwnISGBdu3a8dxzz7Fu3Tpmz57NHXfcQdWq6Z+BsnfvXv73v/9x5ZVXkpSUxMUXX8yYMWP4448/fIrchBOLq4Uz7a1JVT+J9jLzI2sSNqbgSuvq9Nlnn+W3335j7ty53HPPPdSpUyfdeIcPH2bq1KkMHjyY0047jVatWvHYY4+xZMkS64LRZ9F6Ks6lJzFNZRFpHY3l50eWXI0x4JqOW7VqxRNPPMHPP//MkiVLePjhh8M+13n+/PkMHz6cxo0bU6dOHe644w6+/vpre6iAD6JVc31JRBaJyBARKZfZiCLSXkTGAKuAs6K0/Hxly5YtrFy5EoDChQuTnJzsc0TGmHggIjRs2JAHH3yQBQsWsHr1ap577jnOPfdcEhMT043766+/8txzz3HeeedRvnx5Lr/8csaPHx+4lsPEVrQell4HuBN4GHhBRJYDi4CtwCGgLFATSAZK427T6aqqs6O0/Hwl+BFzLVq0oGhRe+ytMeZENWrU4LbbbuO2225jx44dTJ06lUmTJjF16lT27t0bGG/Pnj28//77vP/++4GLqC666CIuvvhizj77bLumIwai9VScfar6MFAFuArXacTZwCDgDqA7rkOJf+Oe+XquJdaMWZOwMSa7ypUrx1VXXcV7773Htm3b+Oyzz7jllluoUaNGuvFUlXnz5jFixAhatmxJpUqVuOaaa5gwYQI7duzwKfr8J1rnXHeISHNVPQJcCDyiqg1UtYyqFlXVKqraRVX/qaororHM/MySqzEmJ4oUKcIFF1zA888/z6+//srSpUt58sknad++/Qm11K1bt/Lmm2/Sr18/kpKSuO+++3yKOn+JVltACVwnEQADgKQozbfA2bdvHwsWLAi8P+ecc3yMxhiT14kIDRo0YNiwYXz77bds3bqVCRMm0L9/fypUqJBu3GPHjlGzZk2fIs1fonXO9TfgehFJS7DNRCTDE4XWeX/G5s+fH+hTtGHDhpQrl+n1YcYYky3lypWjT58+9OnTh2PHjrFgwQKmTp3Kp59+yvz587nookwfyW0iFK3k+gQwGugPKPByBuOJNzwxg+EF3rx58wL/W63VGBNLCQkJJCcnk5yczIMPPsjOnTspW7as32HlC1FJrqr6mohMxT2vdTpwK7A8GvMuaL777rvA/y1btvQxEmNMQWOJNXqiVXNFVf8A/hCR8cAnqromWvMuSObPnx/4v0WLFj5GYowx5mRFLbmmUVV76s1J2rRpExs2bACgWLFiNGzY0OeIjDHGnAy7cziOBDcJN2/enEKFon7sY4wxJhdYco0j1iRsjDH5gyXXOGIXMxljTP5gyTVOqGq65Go1V2OMybviNrmKSFUReV9EdovInyIyUUTOiGC6ZBEZIyIrRGS/iKwTkbdEpEZW0/ppzZo1gX49y5YtS61atXyOyBhjzMmKy+QqIsWBaUB9XMcUV+OevDNdREpkMXlfoCHwPHARcC/QHPheRKrGLOgcCj3fKiI+RmOMMSYn4vVy1Otxj6irp6qrAERkMfALMBh4NpNpn1TVrcEFIjILWOPN96GYRJxD1iRsjDH5R1zWXIEewNy0xArgdUoxC+iZ2YShidUr+w33bNkqUY4zaiy5GmNM/hGvybUhsCRM+VKgQXZnJiJnAhWI0y4ZU1NT+eGHHwLv7UphY4zJ2+I1uZYDdoYp3wFkq/NLESkEvIKruY7NYJwbROR7Efl+69YTKr4xt3z5cvbv3w9AlSpVqFy5cq7HYIwxJnriNbmCe3pOqJO5yudF4Bzgb6oaLmGjqmNUNVlVk5OScv9RtNYkbIwx+Uu8XtC0E1d7DVWW8DXasETkceAGoL+qfhGl2KIu+EphaxI2xpi8L16T61LceddQDYBlkcxARIbjbsO5VVXfjGJsUWc1V2OMyV/itVn4Y6C1iNRMKxCR6kBbb1imRORW4FFguKq+EKMYo+LgwYMsXrw48D45OdnHaIwxxkRDvCbX/wBrgUki0lNEegCTgPXA6LSRRKSaiKSKyENBZX2B54DPgGki0jroL9tXGsfawoULSU1NBaBu3bqUKVPG54iMMcbkVFw2C6vqPhHpDPwf8CbuQqavgdtVdW/QqAIkkv4g4UKv/ELvL9g3QKcYhX1SrEnYGGPyn7hMrgCqug7oncU4awm5glhVBwADYhVXtFlyNcaY/Cdem4ULDEuuxhiT/1hy9dHu3btZsWIFAImJiTRr1szniIwxxkSDJVcfBXd52LhxY4oVK+ZjNMYYY6LFkquPrEnYGGPyJ0uuPrKemYwxJn+y5Oojq7kaY0z+ZMnVJ5s2bWL9+vUAFCtWjIYNw/X2aIwxJi+y5OqT4Fpr8+bNKVQobm85NsYYk02WXH1iTcLGGJN/WXL1yYIFCwL/W2f9xhiTv1hy9cmPP/4Y+N86jzDGmPzFkqsPNm/ezO+//w64i5nq1avnc0TGGGOiyZKrD4JrrU2aNCExMdHHaIwxxkSbJVcfWJOwMcbkb5ZcfWDJ1Rhj8jdLrj4ITq7Nmzf3MRJjjDGxYMk1l+3evZtVq1YBUKhQIRo1auRzRMYYY6LNkmsuW7RoUeD/Bg0aUKRIER+jMcYYEwuWXHOZnW81xpj8z5JrLrPzrcYYk/9Zcs1lwd0eWs3VGGPyJ0uuuejgwYMsW7Ys8L5JkyY+RmOMMSZWLLnmoiVLlnD06FEAateuTalSpXyOyBhjTCxYcs1Fdr7VGGMKBkuuucjOtxpjTMFgyTUX2W04xhhTMFhyzSVHjx5l8eLFgfeWXI0xJv+y5JpLfv75Zw4cOABAlSpVqFChgs8RGWOMiZW4Ta4iUlVE3heR3SLyp4hMFJEzIpz2MRH5QkS2i4iKyIAYh5slO99qjDEFR1wmVxEpDkwD6gP9gauBOsB0ESkRwSxuAYoBU2IWZDbZ+VZjjCk4CvkdQAauB2oC9VR1FYCILAZ+AQYDz2YxfWlVPSYitYFrYhpphCy5GmNMwRGXNVegBzA3LbECqOoaYBbQM6uJVfVYDGPLNlW15GqMMQVIvCbXhsCSMOVLgQa5HEuOrV27ll27dgFQtmxZqlWr5nNExhhjYilek2s5YGeY8h1A2WgvTERuEJHvReT7rVu3Rnv2J9RaRSTqyzDGGBM/4jW5AmiYsphkJVUdo6rJqpqclJQU9flbk7AxxhQs8Zpcd+Jqr6HKEr5GG9csuRpjTMESr8l1Ke68a6gGwLIw5XEt+B5X67DfGGPyv3hNrh8DrUWkZlqBiFQH2nrD+q4ArwAACUdJREFU8ozNmzfzxx9/AFC8eHHq1q3rc0TGGGNiLV6T63+AtcAkEekpIj2AScB6YHTaSCJSTURSReSh4IlFpKOI/BW40CtKFpG/emW5KrhJ+KyzziIxMTG3QzDGGJPL4rITCVXdJyKdgf8D3sRdyPQ1cLuq7g0aVYBETjxI+CfQMej9UO8vbZpcY+dbjTGm4InL5AqgquuA3lmMs5YwyVJVO8Umquyz863GGFPwxGuzcL6xcOHCwP9Nmzb1MRJjjDG5xZJrDO3du5dff/0VgMTERBo1auRzRMYYY3KDJdcY+umnn1B1fWHUr1+fokWL+hyRMcaY3GDJNYaCm4SbNGniYyTGGGNykyXXGFq0aFHgf0uuxhhTcFhyjaHg5GoXMxljTMFhyTVGjh49yuLFiwPvreZqjDEFhyXXGPn111/Zv38/ABUrVqRixYo+R2SMMSa3WHKNETvfaowxBZcl1xixziOMMabgsuQaI1ZzNcaYgsuSa4xYcjXGmILLkmsMbN++nQ0bNgBQpEgR6tWr53NExhhjcpMl1xgIrrU2atSIQoXi9uFDxhhjYsCSawxYk7AxxhRsllxjwK4UNsaYgs2SawxYzdUYYwo2S65RdvjwYZYtWxZ4f9ZZZ/kYjTHGGD9Yco2yFStWcOTIEQCqV69OmTJlfI7IGGNMbrPkGmX2DFdjjDGWXKPMzrcaY4yx5Bpl9gxXY4wxllyjSFWtWdgYY4wl12j6/fff2b59OwAlS5akevXq/gZkjDHGF5Zcoyj0fGtCgq1eY4wpiGzvH0XWJGyMMQYsuUaVXSlsjDEGLLlGlV0pbIwxBiy5Rs2+fftYuXIlAAkJCTRq1MjniIwxxvglbpOriFQVkfdFZLeI/CkiE0XkjAinLSoiT4nIHyJyQETmiEiHWMa7ZMkSVBWAunXrUqxYsVguzhhjTByLy+QqIsWBaUB9oD9wNVAHmC4iJSKYxVjgeuAh4BLgD+BzEYlZW601CRtjjElTyO8AMnA9UBOop6qrAERkMfALMBh4NqMJRaQJcCUwSFVf98q+AZYCDwM9YhGwXSlsjDEmTVzWXHEJcG5aYgVQ1TXALKBnBNMeAd4NmjYVmABcICJFoh+uXSlsjDHmuHhNrg2BJWHKlwINIph2jaruDzNtYaB2zsNL79ixYyxevDjw3pKrMcYUbJJ2EU48EZHDwLOqem9I+aPAvaqaYXO2iHwBlFLV1iHl5wFfAh1UdUbIsBuAG7y39YCfcxB+eWBbDqYvaGx9ZY+tr+yx9ZU9OVlf1VQ1KZrB5GXxes4VIFzWlwimk+xOq6pjgDERxpX5wkW+V9XkaMyrILD1lT22vrLH1lf22PqKnnhtFt4JlAtTXtYblpkdmUybNtwYY4yJmXhNrktx505DNQCWRTBtDe92ntBpDwOrTpzEGGOMiZ54Ta4fA61FpGZagYhUB9p6w7Ka9hTg8qBpCwF9gC9U9VC0gw0RleblAsTWV/bY+soeW1/ZY+srSuL1gqYSwCLgAPAA7hzqI0BJ4CxV3euNVw34FXhYVR/+//buLVSzMY7j+PeXzCaUXKBQGoc7JJKkmNEMKdxMiqKUC6dCyPGCMibCKBdyJTmmJEWUQzY3YxxySJjIyFzIOEXIMP4u1trZ2HtmzFrttV77+6m3993v3rt+7661/+t51nqe/6zffxw4FbgG+Ay4mGYziROq6u0F/CiSpEVolCPXqvoJWA5sAB4CHqEpkstnCmsrwC78+3NcADwA3Ao8CxwEnGZhlSQthFGOXCVJmmSjHLlOki4NBhajJAcmubdtpvBzkmqvp2sOSVYleTLJ520Tio+TrEmy19DZxijJqUleTvJlkl+TbEryRJLtbT6jVpLn2+Py1qGzTDKLawc9NBhYjA4FzqZZUvXadn5WcDWwFbgBOA24j+YegheSePz+2z7AW8BlwErgepqVB+vaezS0DUnOAdxirgdj3kRiEux0g4FF7NWq2g8gyYU0/wA1vzOqavOsr6eTfAs8CJxMc3KnVlU9Bjw2+70k64GPgFXAXUPkmgRJ9gbWAlcCjw4cZ+J55ttNlwYDi1JV/TF0hknyj8I64432+YCFzDLBvmmffxs0xfjdAXzQnqCoI4trN10aDEg766T2+cNBU4xYkl2SLElyGHA/8CVNZyzNIcmJwPnAJUNn+b9wWribfZh7O8Zv+Wu7Rak3SQ6g6Uv8YlW9OXSeEXsdOKZ9/QnNMr6vBswzWkl2pTkBubOqujQt0SyOXLvb2QYD0n+SZE/gaeB3mrXcmt95wPHAucAPNDeAHTxkoBG7FtgdWD10kP8TR67ddGkwIO2wJLvRbO25FDipqjYNHGnUqmpmyvz1JM8BG4HrgIsGCzVC7bLBG4ELgakkU7O+PdXe5PRjVW0dJOAEc+TaTZcGA9IOaaftngSOA06vqvcHjjRRqup7mqnhQ4fOMkJLgd2Ah2kGBDMPaJaBfQccMUy0yWZx7aZLgwFpu9q1rI8ApwBnVdW6gSNNnCT70axF/3ToLCP0DrBsjgc0BXcZdhLbKW5/2MGONhjQ3yVZ1b48hWaa7hJgM7C5qqYHCzZCSe6j+RutBp75x7c3OT38d0meAt4G3qO51no4zbrN/YHjqmrDgPEmRpICVlfVTUNnmVQW147aaxZrgRU0NzK9BFxRVRuHzDVm7YE7l+mqOnkhs4xdko3AfDsL3VJVNy9cmvFLci3NDmCHAEuAL4BXgDUekzvO4tqdxVWSpJ55zVWSpJ5ZXCVJ6pnFVZKknllcJUnqmcVVkqSeWVwlSeqZxVWSpJ5ZXCVJ6pnFVZKknllcpZFKskeSj5KsbzvjzLy/MskfSS4dMp+k+bn9oTRiSY4G1gFrq+q6JPvSbEq/vqrOHDadpPlYXKWRS3IlcBewkqbH5hHAUVX19aDBJM3L4iqNXJIAzwLLaTq9rKi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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f= lambda x:x/(1+x**2)\n", + "\n", + "n=10\n", + "ri=np.random.rand(n)\n", + "\n", + "xi=1+(3-1)*ri # scaling equation for our uniform distribution 0..1 to 1..3\n", + "# we scaled our variable by a factor of 2 so we scale the bins accorcdingly\n", + "bin_scale = 3-1 \n", + "\n", + "my_I=sum(f(xi))/n*bin_scale\n", + "print(my_I)\n", + "\n", + "plt.bar(xi,f(xi),width=bin_scale/n,color=[0.1,0,1,0.5])\n", + "plt.plot(np.linspace(0,4),f(np.linspace(0,4)),color='k')\n", + "plt.title('Graphical illustration of Monte Carlo'+\\\n", + " ' integration\\n I={:.4f}'.format(my_I))\n", + "plt.xlabel('x')\n", + "plt.ylabel('f(x)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def montecarlo_integrate(f,a,b,N):\n", + " '''Estimate the integral of a function f(x) from a->b with N \n", + " randomly chosen points\n", + " Arguments\n", + " ---------\n", + " f: a function of the form f=lambda x: ...\n", + " a: the lower bound of the integration\n", + " b: the upper bound of the integration\n", + " N: number of random points to produce between xi=a-b\n", + " \n", + " Returns\n", + " -------\n", + " our_I: the best prediction of the integral using N points \n", + " '''\n", + " ri=np.random.rand(n)\n", + "\n", + " xi=a+(b-a)*ri # scaling equation for our uniform distribution 0..1 to 1..3\n", + " # we scaled our variable by a factor of 2 so we scale the bins accorcdingly\n", + " bin_scale = b-a\n", + "\n", + " our_I=sum(f(xi))/n*bin_scale\n", + " \n", + " return our_I\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above function, we have defined a general Monte Carlo integration function. Now, we can create a for loop to try the function multiple times and get an average and standard deviation. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean value is I = 0.81410075695566\n", + "standard deviation is 0.028138512733038436\n", + "actual I = 0.80471895621705\n", + "error in mean = -0.009381800738609947\n" + ] + } + ], + "source": [ + "test_I=np.zeros(10)\n", + "for i in range(0,10):\n", + " test_I[i]=montecarlo_integrate(f,1,3,500);\n", + "\n", + "print('mean value is I = {}'.format(np.mean(test_I)))\n", + "print('standard deviation is {}'.format(np.std(test_I)))\n", + "print('actual I = 0.80471895621705')\n", + "print('error in mean = {}'.format(0.80471895621705-np.mean(test_I)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Example 3: Determine uncertainty in failure load based on geometry uncertainty\n", + "\n", + "In this example, we know that a steel bar will break under 940 MPa tensile stress. The bar is 1 mm by 2 mm with a tolerance of 10 %. What is the range of tensile loads that can be safely applied to the beam?\n", + "\n", + "$\\sigma_{UTS}=\\frac{F_{fail}}{wh}$\n", + "\n", + "$F_{fail}=\\sigma_{UTS}wh$" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "N=10000;\n", + "r=np.random.rand(N,1);\n", + "wmean=1; # in mm\n", + "wmin=wmean-wmean*0.1;\n", + "wmax=wmean+wmean*0.1;\n", + "hmean=2; # in mm\n", + "hmin=hmean-hmean*0.1;\n", + "hmax=hmean+hmean*0.1;\n", + "\n", + "wrand=wmin+(wmax-wmin)*r;\n", + "hrand=hmin+(hmax-hmin)*r;\n", + "\n", + "uts=940; # in N/mm^2=MPa\n", + "\n", + "Ffail=uts*wrand*hrand*1e-3; # force in kN\n", + "plt.hist(Ffail,bins=20,)\n", + "plt.xlabel('failure load (kN)')\n", + "plt.ylabel('relative counts')\n", + "plt.title('Failure load is {:.2f}+/- {:.2f} kN'.format(np.mean(Ffail),np.std(Ffail)));" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Normally, the tolerance is not a maximum/minimum specification, but instead a normal distribution that describes the standard deviation, or the 68% confidence interval.\n", + "\n", + "So instead, we should generate normally distributed dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "N=10000;\n", + "wmean=1; # in mm\n", + "wstd=wmean*0.1; # standard deviation in mm\n", + "hmean=2; # in mm\n", + "hstd=hmean*0.1; # standard deviation in mm\n", + "\n", + "\n", + "wrand=np.random.normal(wmean,wstd,size=N);\n", + "hrand=np.random.normal(hmean,hstd,size=N);\n", + "uts=940; # in N/mm^2=MPa\n", + "\n", + "Ffail=uts*wrand*hrand*1e-3; # force in kN\n", + "plt.hist(Ffail,bins=20)\n", + "#plt.xlabel('failure load (kN)')\n", + "#plt.ylabel('relative counts')\n", + "plt.title('Failure load is {:.2f}+/- {:.2f} kN'.format(np.mean(Ffail),np.std(Ffail)));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this propagation of uncertainty, the final value of failure load seems to be independent of wheher the distribution is uniformly random or normally distributed. In both cases, the failure load is $\\approx 1.9 \\pm 0.25$ kN.\n", + "\n", + "The difference is much more apparent if you look at the number of occurrences that failure will occur whether the dimensions are uniformly random or normally distributed. \n", + "\n", + "For the uniformly random case, there are approximately 500 parts out of 10,000 that will fail at 1.9 kN. \n", + "\n", + "For the normally distributed case, there are approximately 1500 parts out of 10,000 that will fail at 1.9 kN. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Where does a normal distribution come from?\n", + "\n", + "\"Everybody believes in the exponential law of errors: the experimenters, because they think it can be proved by mathematics; and the mathematicians, because they believe it has been established by observation\" [5].\n", + "\n", + "In the previous example, we drew dimensions from uniformly random distributions and normally distributed random distributions. Why do we use the \"normal\" distribution to describe data with a mean and standard deviation? There are exact statistical methods to derive the normal distribution, but let's take a look at a Monte Carlo approach. \n", + "\n", + "Let's say there are 10 different independent factors that could change the dimensions of the steel bar in question e.g. which tool was used, how old the blade is, the humidity, the temperature, and the list goes on. \n", + "\n", + "Let's consider one dimension. \n", + "Each of these factors could change the dimensions of the part, let's use a uniform scale of -1/2-1/2.\n", + "If the effect is 0, the dimension is exactly as specified. If the effect is -1/2, the dimension is much smaller. Conversely, if the effect is 1/2 the dimension is much larger. Now, we use a Monte Carlo model to generate 10 effects on 10,000 parts as shown in the next block." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "factors = np.random.rand(10000,10)-1/2 # each row represents a part and each column is an effect (-1/2-1/2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we have created 10,000 parts with 10 uniformly random effects between -1/2-1/2. \n", + "\n", + "We sum the effects and look at the final part distribution. The x-axis is labeled \"A.U.\" for arbitrary units, we are just assuming an effect of -1/2-1/2 for each of the 10 factors. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'number of parts')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WxmnA2sDYbupU1hOrd7439ZYtlI4FjgUYPHhwd1XNzKwHejqjfktge9JosHnAAxHxVA/eP5g0UfIrQL+aPo9+ufP+TVJLo14rY0A+VlonC4B6WWFAVflyIuJq4GqA4cOH10tKZmbWC4WSSl6K5SrgH1l2NeL3JF0PHB8RiwtcagugP3BjnbKT8+MTpD6RPevU2Qp4NiIW5tezgAMkrVPTr7IVaahz4YRnZmYrr2hH/SXAYcA5pM7v9fNxNCnRjCt4nZmkFY9rH5ASzW6kRDARGCRpl8obJW0A7JfLKiaS5q8cVFVvDeBgYHLBRGdmZiUpevvry8D38oz6imeAsWmeIt8izx3pTkT8Cbi39ny+xtyIuDe/nghMB26UdArpdtcZpL6Si6uuN1PSLcBleQDAbOA4YCgpCZqZWRMVban0A37bRdlvSMN3S5NXPN4XuJt02+124D1gt4h4rqb60cB4YAxwJ7A5sHdE1C4nY2ZmDVa0pTKF1McxpU7ZnsBKbSUcEcuN1oqIBcCo/OjuvYuAk/LDzMxaqMukImmLqpc/AG6QtC5p0uOLwF8DXwL2AQ5vZJBmZtYeumupPMWyc0BE6q/4as05gGl4j3ozs47XXVI5umlRmJlZn9BlUomI65sZiJmZtb+io7/MzMxWqPAyLZL2Jk0y3Jw0K75aRMQuy7/LzMw6SaGWiqRTgbtIc0fWJc0ZqX4sbVSAZmbWPoq2VE4gLVd/Ql6t2Kxb3tHRrDMV7VPZALjVCcXMzLpTNKlMAnZoZCBmZtb+enL763ZJAUymzm6LEfFMmYGZmVn7KZpUgrR51ljSwo31eEa9mVmHK5pUJgCfBi4FniBtgGVmZraMokllV9LIrwmNC8XMzNpd0Y76V0grE5uZmXWpaFK5AviaJC/rYmZmXSp6+2sAsA3wuKS7WX70V0TEOaVGZmZmbadoUjmr6vlH65QH4KRiZtbhCiWViPBtLzMzWyEnCzMzK03hpe/NzHqr6AKjcy4c0eBIrNEKJRVJS1l2v/rlRIRn1JuZdbiiLZVzWT6pbATsCfQjzbg3M7MOV7SjfnS985JWB+4AXi8xJjMza1Mr1VGf91e5CjixnHDMzKydlTH6qx8wsITrmJlZmyvaUT+4zum1SLPsLwQeLDMoMzNrT0U76udQf/SXgKeB48sKyMzM2lfRpDKK5ZPKn4G5wAPeu97MzKD46K8JDY7DzMz6AC/TYmZmpSm8TIukI4FDgMFA/5riiIgPlxmYmZm1n0ItFUnfBcYDmwEzgWk1j/sKXueLkm6TNFfSIklPSrpA0vo19QZIukbSK5LekjRF0rZ1rtdf0jhJ8/P1pkvauUgsZmZWvqItlX8CLo+Ib63k550MPAucCcwDPgGMBnaT9OmIWCpJwERgKPB10oZgZwBTJW0XEfOqrnctMAI4BXiGNAptkqQdI2LmSsZqZmY9VDSpbERajmVl7RcRL1e9niZpAXA9sCtwDzAS2AnYPSKmAkiaDswGTgW+kc8NAw4FRkXE+HxuGjCLtFbZyBLiNTOzHijaUT8NGLayH1aTUCoeyMdB+TgSeL6SUPL7Xicltf2r3jcSeBe4pareEuBmYC9J/VY2XjMz65miLZUTgZ9JehW4C1hQWyEilvYyhl3y8ff5uDXwWJ16s4AjJK0XEQtzvdkR8XademsBW+bnVqKi+2KY9Yb3XWl/RZPKH/JxfBfl0YNr/YWkQaRbVVMiorLUy0DSDP5alUQ2AFiY673WTb0u1yOTdCxwLMDgwfVWoDEzs95Ymf1UVoqk9YCfA0uAo6uLuvgs1XldpN5yIuJq4GqA4cOHl/q9zMw62Urtp9JbkvqTRnhtAexSM6JrAfVbGQPy8bWqevWaGQOqys3MrImaPqNe0prAbcD2wD4R8WhNlVmk/pJaWwHP5v6USr2hktapU+8d4KnyojYzsyKamlQkrQbcBOwB7B8R99epNhEYJGmXqvdtAOyXy6rrrQkcVFVvDeBgYHJELC7/G5iZWXd63Lm+kv6VlATGAm9J2qGqbF6+DTYRmA7cKOkU3p/8KODiSuWImCnpFuCy3PqZDRxHmjR5WDO+jJmZLavZt7/+IR/PIiWO6sdX4C9Dk/cF7iZtVXw78B6wW0Q8V3O9o0kj0sYAdwKbA3tHxIzGfg0zM6unqS2ViBhSsN4C0h4uo1ZQbxFwUn6YmVmLeel7MzMrjZOKmZmVxknFzMxK46RiZmalcVIxM7PSOKmYmVlpnFTMzKw0zZ5Rb6so75NiZmVwS8XMzErjpGJmZqVxUjEzs9K4T8XM2o73sl91uaViZmalcVIxM7PSOKmYmVlpnFTMzKw0TipmZlYaJxUzMyuNk4qZmZXG81T6OK/pZZ3M81mazy0VMzMrjZOKmZmVxknFzMxK46RiZmalcVIxM7PSOKmYmVlpnFTMzKw0TipmZlYaT35sQ57QaGarKicVM+t4PfmPmmffd89JZRXiFoiZtTv3qZiZWWn6RFKRtLmkn0p6XdIbkn4maXCr4zIz6zRtf/tL0jrAPcBi4EgggDHAVEkfj4i3WhmfmfUtXvm4e22fVIBjgC2Av42IpwAk/Q74I/DPwA9aGJuZWUfpC0llJHB/JaEARMRsSb8G9mcVSCrugDfrPJ3aoukLSWVr4Od1zs8CDmrkBztZmNnK6mvJpy8klYHAa3XOLwAG1HuDpGOBY/PLhZKebFBsq7qNgVdaHUSH8c+8+frEz1wXtTqC5Xyo3sm+kFQgdc7XUpeVI64Grm5cOO1B0oMRMbzVcXQS/8ybzz/z5uoLQ4pfI7VWag2gfgvGzMwapC8klVmkfpVaWwGPNzkWM7OO1heSykRgB0lbVE5IGgJ8JpdZ1zr+FmAL+GfefP6ZN5Ei6nVHtA9J6wKPAIuA75D6V84D1gc+HhELWxiemVlHafuWSp4xvzvwB+AG4CZgNrC7E4qZWXO1fUvFzMxWHW3fUrGVJ+mjki6X9DtJCyXNlzRR0rBWx9YXeMHT5pL0RUm3SZoraZGkJyVdIGn9VsfWCdxSMSSdQJoMej0wA9gQOBX4BPCZiHioheG1tbzg6SOkBU8rfX5jgHVIfX5e8LRkku4HniWttDGP9Hs8GngC+HRELG1ddH2fk4ohaWPg1aj6ZZD0V8Ac4I6IOKJVsbU7Sd8krT9XveDpUNKCp6dGRMvXputrJG0SES/XnDuC9J+mPSLintZE1hl8+8uIiFei5n8XEfE6afDDoNZE1WfUXfAUqCx4aiWrTSjZA/no3+cGc1KxuiQNBLYBft/qWNrc1sBjdc7PIk3QtebYJR/9+9xgTirWlX8hrZ92WasDaXM9XvDUyiVpEHAuMCUiHmx1PH2dk0ofJOlzkqLA494u3n8GcChwQvVtG+u1Hi14auWRtB6pw34JcHSLw+kIfWWVYlvW/wAfK1Dv7doTkr4KnA98JyKuKzuwDuQFT1tEUn/SUk1bALtExLwWh9QRnFT6oIh4mzR8skck/SNwFfD9iBhbemCdyQuetoCkNYHbgO2Bz0XEoy0OqWP49pcBIOkAYDxwTUSc3Op4+hAveNpkklYjLde0B7B/RNzf4pA6iuepGJJ2BiaT/ud8AlA9OWxxRDzcksD6AC942nySfgh8FRgL/GdN8TzfBmssJxVD0mjgnC6K50bEkOZF0/fkJVkuBf4fqYP+v4ATI2JOK+PqqyTNoYutboHvRcTo5kXTeZxUzMysNO5TMTOz0jipmJlZaZxUzMysNE4qZmZWGicVMzMrjZOKmZmVxknFbAUknSnpWUlLJM3M5zbNWy4vyItznljyZ54o6Qu9eN93cjw/68F7jsrv2bKL8pA0pqexWGfy2l9m3ZC0PWlm9jjgP4A3c9HZpD06jgLmk3bJLNOJwK+Awskhq+zSOULSRhHxarlhmXXPLRWz7lVWe/5RRPxP1cKEHwMeiYjbI+L+iHihRfH9haRPAx8B7gLWAg5pbUTWiZxUrGNJGpZvYb0maZGkX0v6bFX5vcCE/PLpfBtogqQAdgU+W7U3zZD8nqGSbpL0sqTFkmbmxTrrffbtkl7Nn/1k3semepmRw6quP6H2GnUcCbwHHAM8x/utFrOmcVKxjiTpk6R9ZwaS/ggfCLwKTJH0d7na14AL8vMvADuS1kjbEfgd8HB+viMwX9LmwG+AYcC3SPvTzwBukzSy6rO3B6YDH871RgA/AD6YqxwAvABMqrr+eSv4Pv2BLwF3R8TzwI3ApyQV2VfHrDTuU7FONQ54Ftg9It4BkDSJtJ/8d4HPR8Tjkp7J9R+uWgByrqQ3gSXVy6rnhTlF2hCq0pcxKSebc3l/qftLSAlsh7z3DcA9letExMOSFgOv9GDZ9v2BDYH/n19fD5xBar2cXvAaZivNLRXrOJLWJnWy3woslbSGpDVICWEKsHMvL703qT/j9co183UnAcMkbSBpHdJeKjdVJZQyHAm8QRpMQEQ8SWo1HZ73FzFrCv+yWScaCKxOapG8W/M4ARjQyz/EHyD1Y9Rec1wu34i0jfBqQGl7ekjaFNgTuBPoJ2lDSRuSdj4cRNqsqjtL8nH1OtdevaaOWbd8+8s60Z9IG5H9K+/fLlpGRCytd34FXgX+G7ioi/LnSX+4l5L+2Jfl8HzdQ6g/4utI4O5u3v9SPm4GPFlTtlk+vrgyAVrncFKxjhMRb0n6b1KH+oxeJpB6fknqVJ8VEYu6qiTpV6TbUud2U28xsHbBzz0CmEuaM1PrNOAASetHxJt1yiHdJltEGqwwtabswHy8t2As1uGcVKxTnQTcR+pIv5Y0gXFj4JPA6hHRm87ts4HfAvdJupI0IXIAsA2wRUSMyvVOBqYB0yV9n3QrbAtgu4j4eq7zOGnI8r6kkWCv1NspMo9i2xYYHRH31invT+rr+SIwXtKHgKeBcyPiXICIeF3SWGCMpH7AHcA7pH6nbwMTIuL3Vdc8ArgO2CMipvXi52R9mPtUrCNFxAzgU6RbVlcAk4HLSX+g7+vlNZ8FhpP2pD+fdMvph6Q/ztWjux4gddY/B/wLqXP/FJbtZzmDdCvq34EHgNFdfOyRpH3vJ3RRPjl/zpH5tUi3ypb5tx8RY4Gjge2Am0kd/iOBs4Cv1FxztXwNdfGZ1sG8nbCZmZXGLRUzMyuNk4qZmZXGScXMzErjpGJmZqVxUjEzs9I4qZiZWWmcVMzMrDROKmZmVpr/A/wt4URURV3eAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dims = np.sum(factors,axis=1)\n", + "\n", + "plt.hist(dims,30)\n", + "plt.xlabel('effect A.U.')\n", + "plt.ylabel('number of parts')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, depending upon which random numbers were generated, you should see what looks like a normal distribution. \n", + "\n", + "Normal distributions come from the assumption that we have a large (or infinite) number of uncontrollable factors that can change our desired result. In our case, ideally each factor would have an effect of 0, because then it is exactly as specified, but the reality is that we can't control most factors. As engineers, we always have to consider the uncertainty in our models and measurements. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## We learned:\n", + "\n", + "* How to generate \"random\" numbers in Python$^+$\n", + "* The definition of a Monte Carlo model\n", + "* How to calculate $\\pi$ with Monte Carlo\n", + "* How to take the integral of a function with Monte Carlo\n", + "* How to propagate uncertainty in a model with Monte Carlo\n", + "* **Bonus**: use Sympy to do calculus and algebra for us! *no need for Wolfram, sorry Stephen*\n", + "* How to generate a normal distribution using uniformly random numbers\n", + "\n", + "$^+$ Remember, the computer only generates pseudo-random numbers. For further information **and** truly random numbers check [www.random.org](https://www.random.org/randomness/) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# References\n", + "\n", + "1. [Why the Monte Carlo method is so important today\n", + "Dirk P. Kroese, Tim Brereton *et al.* Wiley Interdisciplinary Reviews: Computational Statistics, 6, 6, 11 2014](https://onlinelibrary.wiley.com/doi/full/10.1002/wics.1314)\n", + "\n", + "2. [Wikipedia: Monte Carlo integration](https://en.wikipedia.org/wiki/Monte_Carlo_integration)\n", + "\n", + "3. [Weinzierl, S. (2000). \"Introduction to Monte Carlo methods\"](https://arxiv.org/abs/hep-ph/0006269)\n", + "\n", + "4. Meurer A, _et al._ (2017) SymPy: symbolic computing in Python. PeerJ Computer Science 3:e103 https://doi.org/10.7717/peerj-cs.103\n", + "\n", + "5. Whittaker, E. T. and Robinson, G. \"Normal Frequency Distribution.\" Ch. 8 in The Calculus of Observations: A Treatise on Numerical Mathematics, 4th ed. New York: Dover, p. 179, 1967." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Problems\n", + "\n", + "1. Calculate the area of a unit circle using the Monte Carlo integration method that we devloped in Example 2. Calculate the area of a quarter of the circle, then multiply the final result by 4. \n", + "\n", + " _Hint: You have to create a function that describes the perimeter of the circle._\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. 100 steel rods are going to be used to support a 1000 kg structure. The\n", + "rods will buckle when the load in any rod exceeds the [critical buckling\n", + "load](https://en.wikipedia.org/wiki/Euler%27s_critical_load)\n", + "\n", + " $P_{cr}=\\frac{\\pi^3 Er^4}{16L^2}$\n", + "\n", + " where E=200e9 Pa, r=0.01 m +/-0.001 m, and L is the \n", + " length of the rods supporting the structure. Create a Monte\n", + " Carlo model `montecarlo_buckle` that predicts \n", + " the mean and standard deviation of the buckling load for 100\n", + " samples with normally distributed dimensions r and L. \n", + "\n", + " ```python\n", + " mean_buckle_load,std_buckle_load=\\\n", + " montecarlo_buckle(E,r_mean,r_std,L,N=100)\n", + " ```\n", + "\n", + " a. What is the mean_buckle_load and std_buckle_load for L=5 m?\n", + "\n", + " b. What length, L, should the beams be so that only 2.5% will \n", + " reach the critical buckling load?" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def montecarlo_buckle(E,r_mean,r_std,L,N=100):\n", + " '''Generate N rods of length L with radii of r=r_mean+/-r_std\n", + " then calculate the mean and std of the buckling loads in for the\n", + " rod population holding a 1000-kg structure\n", + " Arguments\n", + " ---------\n", + " E: Young's modulus [note: keep units consistent]\n", + " r_mean: mean radius of the N rods holding the structure\n", + " r_std: standard deviation of the N rods holding the structure\n", + " L: length of the rods (or the height of the structure)\n", + " N: number of rods holding the structure, default is N=100 rods\n", + " Returns\n", + " -------\n", + " mean_buckle_load: mean buckling load of N rods under 1000*9.81/N-Newton load\n", + " std_buckle_load: std dev buckling load of N rods under 1000*9.81/N-Newton load\n", + " '''\n", + " \n", + " # your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Generate your own normal distribution using uniformly random numbers between -1/2 and 1/2. \n", + "\n", + " a. What is the effect of changing the number of factors?\n", + " \n", + " b. What is the effect of changing the number of samples?\n", + " \n", + " *Hint: for a-b try plotting histograms of the results.*\n", + " \n", + " c. How would you change the mean in your generated distribution?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/01_Cheers_Stats_Beers.ipynb b/notebooks/01_Cheers_Stats_Beers.ipynb index 0de1a15..fa8752b 100644 --- a/notebooks/01_Cheers_Stats_Beers.ipynb +++ b/notebooks/01_Cheers_Stats_Beers.ipynb @@ -14,7 +14,6 @@ "# Cheers! Stats with Beers\n", "\n", "\n", - "\n", "Welcome to the second module in _Computational Mechanics_, our series in computational thinking for undergraduate engineering students. This module explores practical data and statistical analysis with Python.\n", "\n", "This first lesson explores how we can answer questions using data combined with practical methods from statistics.\n", @@ -23,7 +22,7 @@ "\n", "The data source ([@craftcans](https://twitter.com/craftcans) on Twitter) doesn't say that the set includes *all* the canned beers brewed in the country. So we have to asume that the data is a sample and may contain biases.\n", "\n", - "We'll process the data using **numpy**—the array library for Python that we learned about in [Module 1, lesson 2: Getting Started](https://github.uconn.edu/rcc02007/CompMech01-Getting-started/blob/master/notebooks/02_Getting-started.ipynb). But we'll also learn about a new Python library for data analysis called **pandas**. \n", + "We'll process the data using **numpy**—the array library for Python that we learned about in [Module 1, lesson 2: Getting Started](../../CompMech01-Getting-started/notebooks/02-Getting-started.ipynb). But we'll also learn about a new Python library for data analysis called **pandas**. \n", "\n", "[`pandas`](http://pandas.pydata.org/) is an open-source library providing high-performance, easy-to-use data structures and data-analysis tools. Even though `pandas` is great for data analysis, we won't exploit all its power in this lesson. But we'll learn more about it later on!\n", "\n", @@ -34,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -90,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -99,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -108,7 +107,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 24, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -126,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -228,7 +227,7 @@ " ...\n", " \n", " \n", - " 2405\n", + " 2402\n", " 2405\n", " 0.067\n", " 45.0\n", @@ -239,7 +238,7 @@ " 12.0\n", " \n", " \n", - " 2406\n", + " 2403\n", " 2406\n", " 0.052\n", " NaN\n", @@ -250,7 +249,7 @@ " 12.0\n", " \n", " \n", - " 2407\n", + " 2404\n", " 2407\n", " 0.055\n", " NaN\n", @@ -261,7 +260,7 @@ " 12.0\n", " \n", " \n", - " 2408\n", + " 2405\n", " 2408\n", " 0.055\n", " 40.0\n", @@ -272,7 +271,7 @@ " 12.0\n", " \n", " \n", - " 2409\n", + " 2406\n", " 2409\n", " 0.052\n", " NaN\n", @@ -284,7 +283,7 @@ " \n", " \n", "\n", - "

2410 rows × 8 columns

\n", + "

2407 rows × 8 columns

\n", "" ], "text/plain": [ @@ -295,11 +294,11 @@ "3 3 0.090 NaN 2263 Sinister \n", "4 4 0.075 NaN 2262 Sex and Candy \n", "... ... ... ... ... ... \n", - "2405 2405 0.067 45.0 928 Belgorado \n", - "2406 2406 0.052 NaN 807 Rail Yard Ale \n", - "2407 2407 0.055 NaN 620 B3K Black Lager \n", - "2408 2408 0.055 40.0 145 Silverback Pale Ale \n", - "2409 2409 0.052 NaN 84 Rail Yard Ale (2009) \n", + "2402 2405 0.067 45.0 928 Belgorado \n", + "2403 2406 0.052 NaN 807 Rail Yard Ale \n", + "2404 2407 0.055 NaN 620 B3K Black Lager \n", + "2405 2408 0.055 40.0 145 Silverback Pale Ale \n", + "2406 2409 0.052 NaN 84 Rail Yard Ale (2009) \n", "\n", " style brewery_id ounces \n", "0 American Pale Lager 408 12.0 \n", @@ -308,16 +307,16 @@ "3 American Double / Imperial IPA 177 12.0 \n", "4 American IPA 177 12.0 \n", "... ... ... ... \n", - "2405 Belgian IPA 424 12.0 \n", + "2402 Belgian IPA 424 12.0 \n", + "2403 American Amber / Red Ale 424 12.0 \n", + "2404 Schwarzbier 424 12.0 \n", + "2405 American Pale Ale (APA) 424 12.0 \n", "2406 American Amber / Red Ale 424 12.0 \n", - "2407 Schwarzbier 424 12.0 \n", - "2408 American Pale Ale (APA) 424 12.0 \n", - "2409 American Amber / Red Ale 424 12.0 \n", "\n", - "[2410 rows x 8 columns]" + "[2407 rows x 8 columns]" ] }, - "execution_count": 25, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -362,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -371,7 +370,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 26, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -389,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -408,7 +407,7 @@ "Name: abv, dtype: float64" ] }, - "execution_count": 27, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -428,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -437,16 +436,16 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2410" + "2407" ] }, - "execution_count": 29, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -464,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -480,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -489,7 +488,7 @@ "2348" ] }, - "execution_count": 31, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -507,7 +506,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -516,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -533,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -542,7 +541,7 @@ "numpy.ndarray" ] }, - "execution_count": 34, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -560,16 +559,16 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2410" + "2407" ] }, - "execution_count": 35, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -582,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -591,7 +590,7 @@ "1405" ] }, - "execution_count": 36, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -662,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -672,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -698,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -708,7 +707,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -744,7 +743,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -765,7 +764,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -789,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -868,7 +867,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -927,7 +926,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -958,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -975,7 +974,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1014,7 +1013,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1022,22 +1021,83 @@ "%matplotlib inline\n", "\n", "#Import rcParams to set font styles\n", - "from matplotlib import rcParams\n", + "from matplotlib import rcParams" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 1., 0., 38., 699., 857., 486., 159., 104., 1., 3.]),\n", + " array([0.001 , 0.0137, 0.0264, 0.0391, 0.0518, 0.0645, 0.0772, 0.0899,\n", + " 0.1026, 0.1153, 0.128 ]),\n", + " )" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pyplot.hist(abv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have a line plot, but if you see this plot without any information you would not be able to figure out what kind of data it is! We need labels on the axes, a title and why not a better color, font and size of the ticks. \n", + "**Publication quality** plots should always be your standard for plotting. \n", + "How you present your data will allow others (and probably you in the future) to better understand your work. \n", "\n", + "We can customize the style of our plots using parameters for the lines, text, font and other plot options. We set some style options that apply for all the plots in the current session with [`plt.rc()`](https://matplotlib.org/api/_as_gen/matplotlib.plt.rc.html)\n", + "Here, we'll make the font of a specific type and size (sans-serif and 18 points). You can also customize other parameters like line width, color, and so on (check out the documentation)." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ "#Set font style and size \n", "rcParams['font.family'] = 'sans'\n", "rcParams['lines.linewidth'] = 3\n", - "rcParams['font.size'] = 16" + "rcParams['font.size'] = 18" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll redo the same plot, but now we'll add a few things to make it prettier and **publication quality**. We'll add a title, label the axes and, show a background grid. Study the commands below and look at the result!" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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yg22sAM4C/jfwROBw4EHAmUnuBZBkW+BU4AHAwcBzgfsCp7W3TKGtF2AV8CTgr4GnA1u19e4x5LFJkiR10rBz6O4B/NcUy24A7jDdBqrq08Cne8uSfAf4CfAM4F+BF9PcGuX+VXV+W+eHNKHxJcA721UPAPYAHltVp7X1VgPrgMOAlw9xbJIkSZ007AjdJTSjaYPsBlwwy3b8un2fvKjiAODMyTAHUFXrgG8BT+1Z7wDgkskw19a7mub0b289SZKkJWvYQHc88Pokj+gpqyT3AV4JHDfTDSXZMsnWSe4LfBC4DDi2Xfwg4JwBq60FeufabarejkmmHTGUJEnqumED3RHA+TSnXc9ty46lCVXraC6WmKlvAxuBnwJ/RHPa9FftshXAlQPWWQ8s7/m8qXr01ZUkSVqShgp0VfVbYE+ax36dDXwd+CHNBQ6Pq6qNQ2zuucAjgYOAa4CTk+zUu7sB62TA55nUu/XC5JAka5Ksufzyy2fcYEmSpMVo6BsLV9VNwMfa16xV1eQI37eTfIVm/t0/AC+lGXVbMWC15dx6RG79JurB4NE7quoo4CiAlStXDgqEkpaoDRtgYmLcrZCk+TWrJ0XMt6q6Ksn53HLbk7UMvvhiV+DHPZ/X0tz6ZFC9i6rq2nltqKTOm5iAbHIMf36U/1WUNELD3lj4Z0l+uonXebNpRJK70txz7r/bolXAI5Pcu6fOTjQ3M17Vs+oqYIcke/XUuxOwf189SZKkJWvYEbpvc9s5a3emmQt3DXDGdBtI8gWa+Xc/bNe5H83TJ26iuQcdwIdo5uV9qX2cWAFvAn5Bc0XspFXAauDoJK+iOcV6OM0curcNeWySJEmdNFSgq6rnDCpPsgL4KnDCDDZzJnAg8PfA1jQh7evAW6vqgnY/v03yWOBdwKdoAtopwN/2nkatqpuT7Ae8A3g/MEET8Pauql8Mc2ySJEldlZqniR5JngG8sap2nbbyIrJy5cpas2bNuJshdcJCzz2rWhr7GNV+nKcnbX6SnFVVK/vLh70P3aZcB+w4j9uTJEnSDMz5KtckW9BcVfp6brnZsCRJkkZkqECX5EZue1HEFjRz3K4F9p2ndkmSJGmGhh2h+xduG+g2ABcCJ1TVwBv5SpIkaeEMe5XraxeqIZIkSZqd+bwoQpIkSWMw7By6o4aoXlX1kiHbI0mSpCENO4fuycAdgTsBN9M8mWE5zUjfNcBveup6hyRJkqQRGPaU64E0we05wO2qanvgdsBz2/JnVtU925f3pJMkSRqBYUfo3gW8rar+fbKgqm4Ejmkf//Ue4BHz2D5JkiRNY9gRut2B86ZYdh6w29yaI0mSpGENG+j+B3jGFMueCfxqbs2RJEnSsIY95foe4F+T3A34LE3AuyvN3Lp9gb+f3+ZJkiRpOsPeWPhdSa4DXgfs37PoEuDQqhrmtiaSJEmaB8OO0FFVH0zyIeBewN2BS4ELq+rm+W6cJEmSpjd0oANow9u69iVJkqQxGvrRX0n+KMlnklyW5IYkf9yWH5nkifPfREmSJG3KUIEuyZ8A36a5fcnngS37tvXS+WuaJEmSZmLYEbp/AU4BHgi8HEjPsjXAQ+epXZIkSZqhYefQPRR4elXdnCR9y66guYWJJEmSRmjYEbqNNM9uHeRuwNVza44kSZKGNWyg+ybw8iS961X7/kLgtHlplSRJkmZs2FOur6cJdd+jeVJEAc9J8jbgkcDD57d5kiRJms5QI3RV9T3gMcBVwBE0F0X8LTAB7F1V585z+yRJkjSN2Twp4rvAXkm2Be4CXFlVv5n3lkmSJGlGZjxCl2TrJL9Ksj9AVV1XVRcZ5iRJksZrxoGuqm6gOcW6YeGaI0mSpGENe5XrKuDpC9EQSZIkzc6wc+hWAe9LcizwReBSbrltCQBVdcY8tU2SJEkzMGyg+0L7fmD76g1zaT9v2b+SJEmSFs6wge4JC9IKSZIkzdq0gS7JY4HvVNW1VXXKCNokSZKkIczkooiTgV0nPyTZIskZSe67cM2SJEnSTM0k0GXA5z2AOw67syTPSPK5JBcmuT7JeUnemuSOffWWJ/lwkiuS/DbJ15LsNmB7E0nenuTSdnurk+w5bLskSZK6bNjblszVK4HfAf8IPAn4AHAocHKSLQCShOZq2icBf01zm5StgNOS3KNvex8BXkzzjNn9aK66PTHJgxf+UCRJkhaHoR/9NUf7V9XlPZ9PT7Ie+ATNM2JPBQ6gGQF8bFWdBpBkNbAOOAx4eVu2O3AQ8MKq+lhbdjqwFnhjux1JkqQlb6aBbock927/vWVP2VX9Favq51NtpC/MTfru5Pba9wOASybDXLve1Um+DDyVNtC19W4Ejuupd1N7j7x/SLJNVW2c/tAkSZK6baaB7vgBZV+cou6w96Hbq30/t31/EHDOgHprgecluUNVXdvWW1dV1w2otzWwS/tvSZKkJW0mge4FC7XzJDvQnB79WlWtaYtXABcMqL6+fV8OXNvWu3IT9VbMX0ulxWvDBpiYGHcrJEnjNG2gq6pPLMSOk9wB+BJwE7cOjZNPnLjNKgM+z6TeoH0fAhwCsOOOO86kudKiNTEBmfa7fu5q0E+bJGlRGPVVrkBzuxGaK1nvDexTVRf3LF7P4NG15e37lTOst37AMgCq6qiqWllVK7fffvuh2i5JkrTYjDzQJdkK+BzwcOApVfWjvipraebH9dsVuKidPzdZb+ck2w6odwNw/vy1WpIkafEaaaBr7zV3DPA44KlVdeaAaqtorqDdq2e9OwH7t8t6620FPLOn3jLgWcBJXuEqSZI2F6O+D93/oQlgbwZ+m+SRPcsubk+9rgJWA0cneRXNKdbDaebGvW2yclV9P8lxwLvbUb91NDcp3hl49igORpIkaTEY9SnXJ7fvr6EJbb2vFwFU1c00T304GXg/8AWap0vsXVW/6NveC4CPAUcCJwD3BJ5UVWcv7GFIkiQtHqnN/NK1lStX1po1a6avKC1io7rKdaH3s1T2Mar9bOa/vqXNUpKzqmplf/lYrnKVJEnS/DHQSZIkdZyBTpIkqeMMdJIkSR1noJMkSeo4A50kSVLHGegkSZI6zkAnSZLUcQY6SZKkjjPQSZIkdZyBTpIkqeMMdJIkSR1noJMkSeo4A50kSVLHGegkSZI6zkAnSZLUcQY6SZKkjjPQSZIkdZyBTpIkqeMMdJIkSR1noJOkjtqwYWnsQ9LcLRt3AyRJszMxAcnC7qNqYbcvaX44QidJktRxBjpJkqSOM9BJkiR1nIFOkiSp4wx0kiRJHWegkyRJ6jgDnSRJUscZ6CRJkjrOQCdJktRxBjpJkqSOM9BJkiR1nIFOkiSp40Ye6JLcI8l7k6xOcl2SSrLTgHoTSd6e5NIk17f19xxQb4skhye5IMmGJD9I8vRRHIskSdJiMI4Rul2AA4ErgW9sot5HgBcDrwf2Ay4FTkzy4L56bwKOAN4HPBk4E/hskqfMb7MlSZIWp2Vj2OcZVXVXgCQvAp7YXyHJ7sBBwAur6mNt2enAWuCNwAFt2e8DrwT+uare0a5+WpJdgH8G/nOBj0WSlrQNG2BiYunsR1qqRj5CV1U3z6DaAcCNwHE9690EHAvsk2SbtngfYGvg6L71jwZ2S7Lz3FssSZuviQlIFv5lmJPmZrFeFPEgYF1VXddXvpYmwO3SU28jcP6AegC7LlgLJUmSFonFGuhW0Myx67e+Z/nk+1VVVdPUu5UkhyRZk2TN5ZdfPufGSpIkjdNiDXQB+kPaZPls6t1KVR1VVSurauX2228/yyZKkiQtDos10K1n8Oja8p7lk+/Lk/QHuP56kiRJS9ZiDXRrgZ2TbNtXvitwA7fMmVsLbAPcZ0A9gB8vWAslSZIWicUa6FYBWwHPnCxIsgx4FnBSVW1si79KE/Ce3bf+c4BzqmrdCNoqSZI0VuO4Dx1JntH+86Ht+5OTXA5cXlWnV9X3kxwHvDvJVsA64FBgZ3rCW1X9Ksm7gMOT/AY4myb0PRZ46ogOR5IkaazGEuiAz/Z9fn/7fjrwmPbfLwDeDBwJbAf8AHhSVZ3dt+5rgGuBvwHuBpwHHFhVX57/ZkuSJC0+ue0dPzYvK1eurDVr1oy7GdKc3OayoAVQtfD7WSr7GNV+lso+JvcjaXpJzqqqlf3li3UOnSRJkmbIQCdJktRxBjpJkqSOM9BJkiR1nIFOkiSp4wx0kiRJHWegkyRJ6jgDnSRJUscZ6CRJkjrOQCdJktRxBjpJkqSOM9BJkjYLGzYsrf1IvZaNuwGSJI3CxAQkC7+fqoXfh9TPETpJkqSOM9BJkiR1nIFOkjR2zjuT5sY5dJKksRvF/Dbntmkpc4ROkiSp4wx0kiTNo1GcPvYUtfp5ylWSpHnk6WONgyN0kiRJHWegkyRJ6jgDnSRJUscZ6CRJkjrOQCctIK9EkySNgle5SgvIq90kSaPgCJ0kSVLHGegkSZI6zkAnSZLUcQY6SZKkjjPQSZIkdZyBTpIkqeMMdJIkSR23JAJdknsmOT7J1UmuSfL5JDuOu12SJEmj0PlAl2Rb4FTgAcDBwHOB+wKnJbn9ONsmSZI0CkvhSREvBu4N3L+qzgdI8kPgZ8BLgHeOsW2SJEkLrvMjdMABwJmTYQ6gqtYB3wKeOrZWadHzOauSpKViKQS6BwHnDChfC+w64raoQyafs7qQL0mSRmEpBLoVwJUDytcDy0fcloFGMRLkaJMkbT6W0t+VUezn+usXfh/j/ju8FObQAdSAsinHR5IcAhzSfrw2yXnz3J67AFfM8zY3J0um/0YxSjdgHwvSf2M6lnHsY176b5Ecyzj2Mav+W6THMur9zLjvbne7We9jKVsyfzumca9BhUsh0F1JM0rXbzmDR+6oqqOAoxaqQUnWVNXKhdr+Umf/zY39Nzf239zYf7Nn383N5t5/S+GU61qaeXT9dgV+POK2SJIkjdxSCHSrgEcmufdkQZKdgEe3yyRJkpa0pRDoPgRcAHwpyVOTHAB8CfgF8MExtWnBTuduJuy/ubH/5sb+mxv7b/bsu7nZrPsvVYOuJ+iW9jFf7wKeQHMxxCnA31bVBeNslyRJ0igsiUAnSZK0OVsKp1znXZJ7Jjk+ydVJrkny+XYUcCbrTiR5e5JLk1yfZHWSPQfU2yLJ4UkuSLIhyQ+SPH3+j2b0Frr/ktwvyXuS/DDJtW3dVUl2X5gjGq1RfP/1rfMXSSrJxfNzBOM1qv5LskOSjya5LMnGJOuSvHV+j2b0RvT7787tz/DP23rrkrwvyfbzf0SjNcf+e0uSk5L8uv2ZfP4m6r44yU/a773zkrx03g5ijBa6/5LcPclbk6xp93F5klOm+z3ZCVXlq+cFbEvzHNhzgD+leXzYj4D/Bm4/g/WPAa6iecbs44DPA9cDD+6r92ZgI/BKYG+a+X43A08Zdx8s9v4D/jfwQ+Dv2757GrAa2AA8dNx9sNj7r6/+dsBlwKXAxeM+/q70H7AT8Evgm8CBwF7AwcCbxt0Hi73/aKbFfAu4HDgUeAzwV8Cv25/jjLsfxth/vwG+AXyC5v6qz5+i3ovbvxdvbn8HHtl+PnTcfbDY+w/YD/g58DqaaVr7Aie0/bffuPtgTv037gYsthfwN8DvgF16ynYGbgJeMc26u7ffRC/oKVsGnAes6in7fZow9099658C/HDcfdCB/rtL/y994Pdo7jv4yXH3wWLvv751jgJOBD7O0gh0I+k/4KvAd4Ctxn3MXes/4H5tvUP61n9pW37/cffDOPqvrbtF+74LUweSZcCvgE/0lX+U5qa6nf2eHFH/bQcsG9Cn5wFnjLsP5vLylOttHQCcWVXnTxZU1Tqa/1E+dQbr3ggc17PuTcCxwD5JtmmL9wG2Bo7uW/9oYLckO8/pCMZrwfuvqq6o9qewp97VwE+BHebjIMZoFN9/ACR5NPAc4GXz0/RFYcH7L8l9aH6G31tVN85v88duFN9/W7fv1/Stf1X73uW/S3PpP6rq5hns41HA9tz278engDsDe8y4tYvPgvdfVV3Vfl/2lt0EfJ+O//3o8g/OQnkQzXBvv7U0Nyuebt11VXXdgHW3pvlfw2S9jcD5A+oxg/0sZqPov9tIsgL4Q+DcmTd1URpJ/yXZimZ07u29v4PKwO4AAA3PSURBVDyXgFH036Pb9+uTnNzOYboyySeT3Hm2DV8kRtF/a4EzgNclWZnkDkkeDrwe+EpVdflneC79N8w+GLCfzf3vx6wl2ZomKHf5e89AN8AKBj8ybD3N48Rmu+7k8sn3q/pHmQbU66JR9N8g76WZm/Pu6Rq4yI2q/14NbAN0fhJ/n1H03x+07x+lGRV+Mk1/7gucmKTLv1cXvP/a33tPoTnF9V2aeU/fppnX1PULw+bSf8PsgwH72dz/fszFEcA9gH9ZwH0suKXwLNeFMOheLjN53HJmuO5M63XVQvffrRcmhwMHAX+5REabFrT/kuwCvAZ4WlVtGL55i95Cf/9NBravV9Xk6epTk1xNe3oR+MpMGrpIjeLn90PAI2nmzZ0LPBD4J+D4JPvP8NTjYrXQv9snt7VU7zk20r+NSQ4C/oHmgqZvLNR+RsFAd1tXMvh/OMsZ/D+HXuuBQZdXL+9ZPvm+PEn6Run663XRKPrv/2sv1X8L8Nqq+ugQ7VysRtF//wacCpyZZLu2bGsg7eeNVXX9UK1ePEbRf79u30/uq3dS+/4QuhvoFrz/kuwL/AXw+Ko6pV12RpKf0/Th/jRP++miufTfTPWOxF3aU76ib3kXjaL//r8k+9NcEPaRqnrDfG9/1Lp8amChrOWWOQq9dgV+PIN1d06y7YB1b+CWOXNraU533WdAPWawn8VsFP0HQJLnAu8H/rWq3jy75i46o+i/XWlOeV3Z8/oLmlOJV9Lt07Cj+vmFqUdIujy6NIr+2619/25fve+07w+cWVMXpbn03zD7YMB+Nve/H0NJ8jjgs8AXgJfM57bHxUB3W6uARya592RBkp1oJkKvmsG6WwHP7Fl3GfAs4KSq2tgWf5XmF9yz+9Z/DnBOe1VPV42i/0jyNOBjwIer6pXz1fhFYBT99+c0967qfZ1Ic8uDvYH3zcNxjMso+u9Mmnv3Palv/cnP/UGlS0bRf5e17w/vW/8R7fsvZ9PwRWIu/TdTq2l+Vgf9/VhPc0VoV42i/0jyKJpR4FOA53T8FP8txn3flMX2Am5P8z/JH9FcJn0A8AOaCbt36Kl3L5p747y+b/1jaUY5XkRzY83jaW54+8d99f65LX8FzY01P0DzP/v9x90Hi73/gD3bsrOBP6GZizP5esi4+2Cx998U+/04S+M+dKP6+T2YZoTu/wJPpLkx7pXAaXT7xrij+Pm9E01ou4TmxsJ7t++XARf17qdrr3nov72AZ9DcPL1o/nP1DOAZffVeSvP34kiavx9vbD+/bNx9sNj7D3gATfC9oO273r8fjxx3H8yp/8bdgMX4opkH8jma+yT9BvgisFNfnZ3ab5gj+spvB7yz/eW0gebqrccM2MeWwGuBC2luYfLD/h/arr4Wuv9orkiqKV4XjPv4F3v/TbHPj7MEAt0o+w94Ls0tFjbSzGV6Lx0OI6PsP+CewEeAdW29dTQXSuww7uMfc/99farfbQP28xKaq6w30jxd4a/Gfexd6D/g+VPVGdTPXXqlPUBJkiR1lHPoJEmSOs5AJ0mS1HEGOkmSpI4z0EmSJHWcgU6SJKnjDHSSJEkdZ6CTlqAkH05SSd45xfIjkizIPYsmt90+JWDetjeDehckOXo+9jlg2w9pj+mwTdR5U5Kbk+w8xHYf3253j/lp6egkuWOS/0ny1Fms+80kX5+ndmzbtuNp87E9qasMdNISk+R23PL4pWfPV7DanFXV92juXv/cQcuThObRS9+obj+6bxivAi6pqi+NsxFVdR3wDuCf/V7X5sxAJy09T6N5vNJ/Ar/PbZ85qtn5BPCHSR4yYNmeNHev/8RIWzQmSSZoHq/0f8fdltZHgZ1pHhUlbZYMdNLSczDN8zSfD1wPPG8mKyVZluTVSX6cZEOSy5N8NckDeurcP8kXklyV5PokZyaZKjDunOSEJNcmuTDJ65Pc6nfOkNubkSQvTnJ+ewxnJ9m7Z9krk2xMsn3fOkny8ySf3sSmjwF+x+BRuufR9PXxPdv8vSTvT3JpkhuSnJfkb2bQ/ouTfLivbFl7ava1PWVHtmX3S3Jykuvafn5eu/zgdp/XJjl10KngJC9N8sOer/eHkmw3XRuBp9P8p+Ezfdt7RJLPtcdwfbv/I9sAOOhY/yzJ2nb/5yZ5es+yg9rje+CA9U5Osmbyc1X9GvgazTNkpc2SgU5aQpL8AfB44LiqupzmOYgHJFk+g9WPBd5MM7L3p8CLgR8Dd+/Z9jeB3WlGZw4ErgJOSPLkAdv7AnBqu60vAv9EEzZ72zrM9mZiL+AVwGuAP6d5zuVXkty/Xf5RmoeYv6BvvSfSjPB8cKoNV9VlwInAQUm27DmOCZoHgH+hqq5py7YEvkIT9N4G7AecDLw7yRtneWxTOR5YRfMw8+8DH0/yFpqv32HAXwIPBG41vzDJO2ieP3sizcjWq9t2/md/8B7gScA5VXVlX/m9gLNpHh7/ZODf2nZ8mNu6P81zX99G03/rgM8m2bNd/gWaZ3neKkC33zePBT7Vt70zgL2TbD1N26WladwPk/Xly9f8vWj+KBfwqPbzPu3nl/bVO4JbP7D6sW29l29i2+8AbgJ26SnbEjgPOLt/28AL+tb/EXDSbLc3g2O/ALgB2LGn7I7AeuBTPWUfB86H5lnWbdnngZ/MYB8Htsf25J6yP2/LnthT9qdt2XP61v84zcPoV7SfH9/W26OnzsXAh/vWW9bWe21P2ZFt2UE9ZXehCayXA3fsKX9FW3eH9vN9aEYb/7FvP3u19fabph9+Bnximjpp2/38dl/b9Sz7Zrufh/V97c8HTusp+xhwYd/X6pXAjcDv9+1v8nv94eP6+fPla5wvR+ikpeV5wM+qanX7+WvAJUx/2vWJNH8MP7SJOnsCZ1bV+ZMFVfU74NPAg5Pcqa/+CX2fzwF2nMP2ZuLMqrqoZ3u/advxqJ4676cJNI8DSHJ3YH82MTrX40s0o4i9o0bPo+njr/WU7UkTVo/tW/9oYBvgETPY10x9ZfIfVXUFcAXwX+2xT/pJ+37P9v2JNGdojmlP5y5rLyj4FnBd2/5N+QOa0HgrSbZL8vYkP6cZHb2RJpRtAezSV31dVX23p+2/Az4LPLK9yASaUbgdaYLmpOcCJ1bVr/q2N9meP5im7dKSZKCTlogkDwN2BT7f/mHdjmaE6vPAo5LcbxOr3xlYX1XXb6LOCuDSAeWX0YzG9J/WXd/3eSPQO5dq2O3NxP9MUbbD5Ieq+g6whua0IDTzrm5iBhc0VNVG4DjgT9PctuOuwBOAo6vq5p6qK4Arquqmvk1c1rN8XtRtT3veQDOHsr8Mbun/32/fL6AJXb2vbWm+HzZlG5qvZ79P0JxifTdNvzwMeHnfvidN9bWa4Jb+OQ34BW2ATrIb8Efc9nQrNHMYAW43TdulJclLvKWlY3J+2qvbV7/nAa8dUA7NqM6KJLfbRKhbD9xtQPndaEb3+gPcdOZ7ewB3naLsl31lHwA+mGQHmkD32aqa6f4+AbyEZt7XdjS/Rz/ZV2c9cJcky/pC3eTx/noT298A9M8Dmy5gDWty/48Drhmw/Ipp1l9PX+BOsi3NHLzXVNW/9ZQPuioYpv5abWi3T1VVkmOAQ5O8jCbYXUMzZ7DfZAicru3SkuQInbQEtBPB/xz4NrD3gNf3gef2nMrqdxLNqNimrhI8neZ02E49+90SeBbwvb5TfDMx39uj3d7kaUWS3BHYF1jdV+/TNBPu/53mlN6Mb7/Rns7+KU24eB5wVlWt7at2Ok3Qe3pf+bNpAsu3N7GLC4E/7Cvbd6btm6GTaELzPatqzYDXBdOs/xPg3n1lEzR/U26cLGi/354/xTZ2TrKyp+6WNCH5zKrqvZH0p4Dfo5mXeBBN+B70n47Jq3jPm6bt0pLkCJ20NOxHM4rz91X19f6FST5IMyr1GJrTWLdSVacl+RzwzjYQnQpsRTOX6oR2m++i+eN8cpI30IyU/BVwP2YXOOZ7e9CcsjspyRE0pwRfDdweeFNvpaq6PsnHgb8DflRV/zXkfj7ZbjPcckqx13/QhMgPJbkbcC7N1+j5wJsGnCbtdSxwVHsV6leABzN1KJqVqvppu/0PtLcFOYOmv+5JM7/uA1X1jU1s4gyaUbNMhq+qWt/eSuSwJP9Dc9r3RQweiYPm9PPx7df+CuBlNHMbX9jX1h8nOYvmIpodGHy6FZp5iRf2zqGUNieO0ElLw8E0I06fnWL5p2nmGB08xXJoRviOoBkJWUVzi48H0c5zq6pLgD2AtTTh8Hia01z7VtVXh23wfG+vdTrwr8BbaOa6TdBckfrTAXUn+2omF0P0+xTNCNeNNH17K+0E/yfTXATxDzQBbx/gb6vq9dNs+6M0t3g5kObr8Hjgz2bRxk2qqsOAQ2lGcI+nubXMYTSnY/97mtWPo/la/Ulf+bOA79F8PT9GM//tFVNs4zyaQP1qmnmeOwMHThEkP0UT5i6iCZOD7MttL0KRNhu59ci2JG0ekrwZ+BvgD6q9f5xmLsk3ae5F99JpKy98Wx5NE+bvV1U/H3d7pHEw0EnarLST9O9Pc7Pbo6pqqhEkbUJ7A+CvAvepqkFXK4+yLV8GLq2qQ8bZDmmcnEMnaXPzBZp5XScCbxhzWzqrqs5I8kqap0OMLdC1V9euYfE8V1YaC0foJEmSOs6LIiRJkjrOQCdJktRxBjpJkqSOM9BJkiR1nIFOkiSp4wx0kiRJHff/AI4QnuMzmaJcAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -1062,12 +1122,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1154,7 +1214,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1182,7 +1242,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1237,7 +1297,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1253,7 +1313,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1281,7 +1341,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1290,7 +1350,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { diff --git a/notebooks/02_Seeing_Stats.ipynb b/notebooks/02_Seeing_Stats.ipynb index 3f26a3d..1193c0a 100644 --- a/notebooks/02_Seeing_Stats.ipynb +++ b/notebooks/02_Seeing_Stats.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -101,7 +101,7 @@ "pandas.core.frame.DataFrame" ] }, - "execution_count": 22, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -303,7 +303,7 @@ "[424 rows x 8 columns]" ] }, - "execution_count": 23, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -314,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -325,7 +325,7 @@ " dtype='object')" ] }, - "execution_count": 24, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -365,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -377,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -444,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -501,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -526,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -587,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -609,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -656,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -690,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -758,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -767,7 +767,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -776,7 +776,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 36, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -787,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -830,7 +830,7 @@ " 'Low Alcohol Beer'], dtype=object)" ] }, - "execution_count": 37, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -855,63 +855,63 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "American Double / Imperial Stout 9\n", - "Schwarzbier 9\n", "Herbed / Spiced Beer 9\n", + "Schwarzbier 9\n", + "American Double / Imperial Stout 9\n", "Belgian Strong Pale Ale 7\n", - "Doppelbock 7\n", - "Bock 7\n", "American Dark Wheat Ale 7\n", "Bière de Garde 7\n", - "American Wild Ale 6\n", + "Doppelbock 7\n", + "Bock 7\n", + "Belgian Strong Dark Ale 6\n", "California Common / Steam Beer 6\n", + "American Wild Ale 6\n", + "Baltic Porter 6\n", "Foreign / Export Stout 6\n", "English Dark Mild Ale 6\n", - "Belgian Strong Dark Ale 6\n", - "Baltic Porter 6\n", "Dortmunder / Export Lager 6\n", - "Dubbel 5\n", - "Irish Dry Stout 5\n", "Euro Dark Lager 5\n", - "Maibock / Helles Bock 5\n", "Mead 5\n", - "Quadrupel (Quad) 4\n", + "Irish Dry Stout 5\n", + "Dubbel 5\n", + "Maibock / Helles Bock 5\n", "English Strong Ale 4\n", - "Dunkelweizen 4\n", + "Quadrupel (Quad) 4\n", "Munich Dunkel Lager 4\n", + "Dunkelweizen 4\n", + "American India Pale Lager 3\n", "Keller Bier / Zwickel Bier 3\n", "English Barleywine 3\n", - "Radler 3\n", - "English Bitter 3\n", - "American India Pale Lager 3\n", - "Shandy 3\n", "Chile Beer 3\n", - "English Pale Mild Ale 3\n", + "English Bitter 3\n", "American Barleywine 3\n", + "Radler 3\n", + "English Pale Mild Ale 3\n", + "Shandy 3\n", + "Rauchbier 2\n", + "Abbey Single Ale 2\n", "English Stout 2\n", + "Euro Pale Lager 2\n", "Old Ale 2\n", - "American Double / Imperial Pilsner 2\n", "Roggenbier 2\n", - "Rauchbier 2\n", - "Euro Pale Lager 2\n", - "Abbey Single Ale 2\n", - "Other 1\n", + "American Double / Imperial Pilsner 2\n", + "Grisette 1\n", + "American Malt Liquor 1\n", + "Low Alcohol Beer 1\n", "Braggot 1\n", + "Flanders Red Ale 1\n", + "Wheat Ale 1\n", "Flanders Oud Bruin 1\n", "Kristalweizen 1\n", - "Grisette 1\n", - "Wheat Ale 1\n", - "Low Alcohol Beer 1\n", "Smoked Beer 1\n", - "Flanders Red Ale 1\n", - "American Malt Liquor 1\n", + "Other 1\n", "Name: style, dtype: int64\n" ] } @@ -923,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -932,7 +932,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 39, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -943,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -952,7 +952,7 @@ "99" ] }, - "execution_count": 40, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -979,12 +979,12 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1025,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1034,7 +1034,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1043,7 +1043,7 @@ "1405" ] }, - "execution_count": 75, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1055,7 +1055,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1064,7 +1064,7 @@ "1405" ] }, - "execution_count": 76, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1090,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1140,7 +1140,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1156,7 +1156,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1165,7 +1165,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1174,7 +1174,7 @@ "pandas.core.series.Series" ] }, - "execution_count": 80, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1185,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1194,7 +1194,7 @@ "90" ] }, - "execution_count": 81, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1223,7 +1223,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1232,7 +1232,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1344,7 +1344,7 @@ "[90 rows x 2 columns]" ] }, - "execution_count": 83, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1371,7 +1371,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1380,7 +1380,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1399,7 +1399,7 @@ "Name: style, dtype: int64" ] }, - "execution_count": 85, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1417,7 +1417,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1455,7 +1455,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1465,7 +1465,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1506,7 +1506,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1578,6 +1578,33 @@ " scatter plot with `beers_filled`. What differences do you notice between the plots? " ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Gordon Moore created an empirical prediction that the rate of semiconductors on a computer chip would double every two years. This prediction was known as Moore's law. Gordon Moore had originally only expected this empirical relation to hold from 1965 - 1975 [[1](https://en.wikipedia.org/wiki/Moore%27s_law),[2](https://spectrum.ieee.org/computing/hardware/gordon-moore-the-man-whose-name-means-progress)], but semiconductor manufacuturers were able to keep up with Moore's law until 2015. \n", + "\n", + " In the folder \"../data\" is a comma separated value (CSV) file, \"transistor_data.csv\" [taken from wikipedia 01/2020](https://en.wikipedia.org/wiki/Transistor_count#Microprocessors). \n", + " Load the csv into a pandas dataframe, it has the following headings:\n", + "\n", + " |Processor| MOS transistor count| Date of Introduction|Designer|MOSprocess|Area|\n", + " |---|---|---|---|---|---|\n", + "\n", + " a. In the years 2017, what was the average MOS transistor count? \n", + " Make a boxplot of the transistor count in 2017 and find the first, second and third quartiles.\n", + "\n", + " b. Create a semilog y-axis scatter plot (i.e. `plt.semilogy`) for the \n", + " \"Date of Introduction\" vs \"MOS transistor count\". \n", + " Color the data according to the \"Designer\"." + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/notebooks/03_Linear_Regression_with_Real_Data.ipynb b/notebooks/03_Linear_Regression_with_Real_Data.ipynb new file mode 100644 index 0000000..21438c1 --- /dev/null +++ b/notebooks/03_Linear_Regression_with_Real_Data.ipynb @@ -0,0 +1,974 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Modified under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2020 R.C. Cooper" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear regression with real data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Earth temperature over time\n", + "\n", + "In this lesson, we will analyze real data of Earth temperature over time.\n", + "\n", + "Is global temperature rising? How much? This is a question of burning importance in today's world!\n", + "\n", + "Data about global temperatures are available from several sources: NASA, the National Climatic Data Center (NCDC) and the University of East Anglia in the UK. Check out the [University Corporation for Atmospheric Research](https://www2.ucar.edu/climate/faq/how-much-has-global-temperature-risen-last-100-years) (UCAR) for an in-depth discussion.\n", + "\n", + "The [NASA Goddard Space Flight Center](http://svs.gsfc.nasa.gov/goto?3901) is one of our sources of global climate data. They produced the video below showing a color map of the changing global surface **temperature anomalies** from 1880 to 2015.\n", + "\n", + "The term [global temperature anomaly](https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php) means the difference in temperature with respect to a reference value or a long-term average. It is a very useful way of looking at the problem and in many ways better than absolute temperature. For example, a winter month may be colder than average in Washington DC, and also in Miami, but the absolute temperatures will be different in both places." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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\n", + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo('gGOzHVUQCw0')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How would we go about understanding the _trends_ from the data on global temperature?\n", + "\n", + "The first step in analyzing unknown data is to generate some simple plots using **Matplotlib**. We are going to look at the temperature-anomaly history, contained in a file, and make our first plot to explore this data. \n", + "\n", + "We are going to smooth the data and then we'll fit a line to it to find a trend, plotting along the way to see how it all looks.\n", + "\n", + "Let's get started!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1: Read a data file\n", + "\n", + "We took the data from the [NOAA](https://www.ncdc.noaa.gov/cag/) (National Oceanic and Atmospheric Administration) webpage. Feel free to play around with the webpage and analyze data on your own, but for now, let's make sure we're working with the same dataset.\n", + "\n", + "\n", + "We have a file named `land_global_temperature_anomaly-1880-2016.csv` in our `data` folder. This file contains the year on the first column, and averages of land temperature anomaly listed sequentially on the second column, from the year 1880 to 2016. We will load the file, then make an initial plot to see what it looks like.\n", + "\n", + "\n", + "Let's start by importing NumPy and pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To load our data from the file, we'll use the function [`numpy.loadtxt()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html), which lets us immediately save the data into NumPy arrays. (We encourage you to read the documentation for details on how the function works.) Here, we'll save the data into the arrays `year` and `temp_anomaly`. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "fname = '../data/land_global_temperature_anomaly-1880-2016.csv'\n", + "\n", + "temp_data = pd.read_csv(fname,skiprows=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercise\n", + "\n", + "Inspect the data by printing `temp_data`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Plot the data\n", + "\n", + "Let's first load the **Matplotlib** module called `pyplot`, for making 2D plots. Remember that to get the plots inside the notebook, we use a special \"magic\" command, `%matplotlib inline`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "#Import rcParams to set font styles\n", + "from matplotlib import rcParams\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `plot()` function of the `pyplot` module makes simple line plots. We avoid that stuff that appeared on top of the figure, that `Out[x]: [< ...>]` ugliness, by adding a semicolon at the end of the plotting command." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#Set font style and size \n", + "rcParams['font.family'] = 'sans'\n", + "rcParams['font.size'] = 16\n", + "rcParams['lines.linewidth'] = 3" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8/t5Nx48Ob8VPN5T2s9VP/4ufQ/Xu4Hk14N+rc/Hvm6X9KdQ51xYMSHoQWG1mj+BHb4/Gz5JwJn56p/ODU36IH7i1Cv/65ODv75uA2+IG5TyG/0zNJcR7VEaYwZ4vRw89+vug5/xqqR4/Do4tBH6MH3zSCrwC/Av+P74e84MFx1eSYu7BVPvwI4b/jq/dqQH+D/8FnrKsFOUvwNccRoLHI/gvxfuDWCfGHHsVSeakwzedfxw/ZcUBfGf3J4BLklxveVDGUvwAnWiTdiX+C310krI/g/8SasHXWvx3cI8Tnit9mMcwOP6tQaxN0dcxbv8k/MTJ0etH8EnHL4Gz4451wKok11hKivkSY+9HsvuMnyPu+eDau/FfuAlzDAbnXYBPtmrxfyTsxM+L9wWgNN0148rJA76JH5DRiq/dWh5sT3iO+GTqv4LXpjP+ueIHJPwPh2ojn8EnEAnvJ5LMoZcixtOAO4N70o4f0LE6iHtWyNf+fcG9PRCc/2t8/79VSd4HKe9Zqn3Be/S7wX1sx893eDe+72Z8GQnX7O/7Cv/H3t/x/Yj34edHXUiSz0ZfXwN8E/BPg/dEG35+xZfwtdaxc2W+Hz+dzubg/u7D18B/ksTPeGV8XHocOQ8L3gQiMoQFTZZbgHHOuYE0P0k/mF8i7lfAR5xzvx7caEREskd9DEWGmGT9tPB9A2fjJ3EWERHJCvUxFBl6NpjZGnxT6Sj8JNKn4OdgWz6IcYmIyAinxFBk6LkZP3DlFGAcvq/VL4HrnHNh5kMUERHpF/UxFBERERFAfQxFREREJKDEUEREREQAJYYiIiIiElBiKCIiIiKAEkMRERERCSgxFBERERFAiaGIiIiIBJQYioiIiAigxFBEREREAkoMRURERARQYigiIiIiASWGIiIiIgIoMRQRERGRgBJDEREREQGUGIqIiIhIQImhiIiIiABKDEVEREQkoMRQRERERAAlhiIiIiISUGIoIiIiIoASQxEREREJKDEUEREREUCJoYiIiIgERg92AENdaWmpmzNnTlav0dzcTEFBQVavMVLoXoWnexWO7lN4ulfh6D6Fp3sVXth7tW7dulrn3OT+XmdYJIZmNgP4EnAysBgYC8x1zlWGOLcSmJ1k13udc/f0dv6cOXNYu3Ztn+Ltq1WrVrF06dKsXmOk0L0KT/cqHN2n8HSvwtF9Ck/3Kryw98rMqgZynWGRGAILgPcB64AngHP7eP7DwPK4ba8PPCwRERGRkWO4JIZ/c86VAZjZx+h7YljrnFud+bBERERERo5hMfjEOdc92DGIiIiIjHTDIjHMgHeb2QEzazOz1WZ28WAHJCIiIjLUmHNusGPok6Ap+eeEH3xyA/AssBUoA64GzgT+0Tl3W4pzPgF8AqCsrGzJ73//+8wEn0JTUxPjx4/P6jVGCt2r8HSvwtF9Ck/3Khzdp/B0r8ILe6/OOuusdc65k/t7nRGfGCY5PwdYDZQ752b2dvzJJ5/sNCp56NC9Ck/3Khzdp/B0r8LRfQpP9yq8PoxKHlBieKQ0JR/knOsC7gRmmNnUwY5HREREZKg44hLDgAU/h1d1qYiIiEgWpZyuxsy29aM8B7zTOfdy/0PKLjMbDVwObHPOVQ92PCIiIiJDRbp5DGcADwA1IcsaBfwDkDfQoJIxs8uCfy4Jfl5gZjVAjXPu8eCYTuAW59xHg98/CFyEfx7b8YNPPhOU8cFsxCkiIiISb8eOJqZPL8DMej94EPU2wfV1zrk1YQoKauL+ceAhpXRn3O83BT8fB5YG/84JHlFbgSnA9UAJcAA/Qvl859zDWYtUREREJNDe3sVllz3An//8bkpLxw52OGmlSwy/ga9lC8U512lm3wB2Djiq5OX3mmLHHxOsdrIsG/GIiIiIhLFmzR7mzy8a8kkhpEkMnXP/3tfC+nOOiIiIyEi2YsUOli2bMdhhhJJ2VLKZHWdmKZ+Jmc00s+MyH5aIiIjI8Nfd7VixYjtnn93r1MlDQsrEMFg2bi0wOc35k4C1ZnZhpgMTERERGe5eeWUf48fnMmfOhMEOJZR0NYYfAX7nnHs+1QHOuReA24GPZzowERERkeHONyMPj9pCSJ8YngLcH6KMPwOnZiYcERERkZFjOPUvhPSJYQmwJ0QZe4NjRURERCSwbVsj+/e3cvzxpYMdSmjpEsM6oDxEGeVAfWbCERERERkZVqzYzrJlMxg1amhPah0rXWK4Br90XG/eFxwrIiIiIoHh1owM6RPDm4DLzexzqQ4ws88DlwI3ZjowERERkeGqrq6V116r49RTpw52KH2SboLrB83sR8APzewj+IEoVcHu2cC7gTcBP3bOPZT1SEVERESGiVWrdnLaaeXk5+f0fvAQknatZOfcF8zseeArwFfjdm8ArnDO3Z6t4ERERESGozVr9nD66dMGO4w+S5sYAjjnbgNuM7OpQHQinu3Oud1ZjUxERERkmNqz5wDTp48f7DD6rNfEMCpIBJUMioiIiPSitraF0tIxgx1Gn/W2VvJ0M3uvmb3pcAUkIiIiMtzV1rZSWjp2sMPos3RrJb8XeBH4MPCImX3jsEUlIiIiMky1t3fR1NRBcXH+YIfSZ+lqDP8L+Ffn3GXABcC3zEwrnIiIiIiksX9/KyUl+cNqYuuodIlhMVAd/HtPcGxh1iMSERERGcaGazMypE8MbwZuMLPPAL8HVjnnqtIcLyIiInLEG64DTyD9BNdfMbP1wNuAe4D/PWxRiYiIiAxTPjEcnjWGvU1w/Xt8baGIiIiIhDBSm5JFREREpI+Gc1NyuulqrjCzSX0pLDhn4sDDEhERERmeRmqN4a+AeWELMrOc4Jy5Aw1KREREZLgaqX0MDfiYmV0Qsiw1S4uIiMgRr6Zm+DYl97ZW8scPSxQiIiIiI0RtbSuTJ4+wGkPnnGoARURERPqgubkDcIwb11vd29Ck5E9EREQkQ6IDT8yG33J4oMRQREREJGOG88ATUGIoIiIikjHDeQ5DUGIoIiIikjHDeQ5DUGIoIiIikjGqMRQRERER4AjpY2hmT5nZP5pZfrYDEhERERmujpSm5A7gFmCXmf3QzBZlMSYRERGRYemIaEp2zi0FKvDJ4RXAK2a2yszeb2a5WYxPREREZNg4UmoMcc697pz7V2A6cBWQA/wW2GFm3zOzedkJUURERGTo6+527NvXyqRJI7zGMJZzrs059xvgs8ATwGTg34A3zOxOMyvPcIwiIiIiQ15DQxsFBaPJy8sZ7FD6rU+JoZmNNbN/MrM1wLP4pPCzwDTg08BbgdszHqWIiIjIEDfcm5EBQq3wbGbHAZ8EPgwUAPcCX3LOrYw57OdmVg3cmfEoRURERIa4mprhPVUNhEwMgfXALuDHwM+cc7tTHLcJeDoTgYmIiIgMJ8N9RDKETwwvB+5xznWlO8g5twE4a8BRiYiIiGTAnXdupKmpg4985JisX2skNCWHna7mrt6SQhEREZGh5tln9/Dii7Whjt26tYFf/OKVfl9rRNcYmtk3+1COc859OwPxiIiIiGTMxo0N5ORYqGOffXYvf/zjJj72sWP7da3a2hYWLZrYr3OHinRNycv7UI4DlBiKiIjIkNHZ2c3WrQ2MGROu51xlZYTt2xtpaelk7Niwve0OGdFNyc65UX14DN8Je0RERGRE2ratkbKycXR3O+rr23o9vqqqEedg8+aGfl1vJDQl93mCaxEREZHhYOPGehYuLGbGjPHs2NHU6/FVVRGOPrqYjRvr+3W9EV1jKCIiIjIcXHHFI0lrBKOJ4axZhWzfnj4x7OzsZseOJpYtm9mvxLC9vYumpg6Ki/P7fO5QEjoxNLNPmNnzZnbAzLriH9kM0sxmmNkNZvZ0cH1nZnNCnjvKzL5iZpVm1mpm683s0mzGKyIiIodHU1M7a9fuZd26vQn7YmsMt29vTFvO7t3NTJo0luOOm8SmTX1PDPfvb6WkJJ9Ro8INdBmqQiWGZnYFcAN+GbwxwK+A24AIsBm4LlsBBhYA7wPq8Osz98W38QNpfgpcAKwG7jSzCzMZoIiIiBx+O3Y0A6RNDGfO7L0pubKykTlzClm4sJiNG/vex3AkNCND+BrDzwH/gV8PGeAm59yVwDygBdiXhdhi/c05V+acu5A+LLlnZlOALwLfc879wDm30jn3SWAl8L0sxSoiIiKHyY4dTRQX5yckhq2tneza1czs2YXMnFnYa41hZWWE2bMnMHVqAY2N7TQ09D5YJf784T7wBMInhguBvwHdwSMPwDlXB/w78NmsRBdwznX389Tz8LHeFrf9NuA4M5s7oMBERERkUO3c2cQ558xk06Z6DhzoPLh9y5YIs2YVkpeXEzQlp68xrKqKMGdOIaNGGQsWFLNpU++1hps3N3DTTS/y3vf+me9/fx0XXDBnoE9n0IVNDFuAUc45B1TjawqjmoBpmQ4sQ44F2vBrOMeKTmue/fVxREREJGt27Ghi3rwiFi0q6bHCyaZNvhkZYOrUAmpqWmhvTz0koqqqkdmzJwCwcGFRrwNQtmxp4MMffpiGhna+9rW3sGrVJVx00by05wwHYRPDl/D9/MD38fuqmZ1mZm/G9997LQuxZUIJUB8ktLH2x+wXERGRYWrnziamTx/PkiVTejQnR/sXAuTmjqKsbBy7djWnLMc3JRcCsHBhca8DUP7yl0ouvngeX/nKyZx88hRyckbGRC9hp/X+GYdqCb8BPAo8GfzeCFyc4bgyxfCrsiTbnvoks08AnwAoKytj1apVmY8sRlNTU9avMVLoXoWnexWO7lN4ulfh6D6Fl4l79frre6iu7iA3t4tHH23i2GN93c/q1bW87W0FrFrlh0GMH9/BX/7yd449NrEfYEeHY8+eZjZvXkdlpXHgQCvPPhth1aoDSa/pnOOuu/Zw5ZUTD9trfdjeV865Pj+AAuAdwHuA0v6U0d8H8DF8sjcnxLHfB1oBi9v+lqCMd/ZWxpIlS1y2rVy5MuvXGCl0r8LTvQpH9yk83atwdJ/CG+i96u7udied9DvX2Njm6utb3ZIlv3Pt7V3OOeeWLbvbVVZGDh77zW8+7X7729eSlrNxY5274IJ7Dv6+d+8Bd9ppf3Dd3d1Jj3/llX3uHe+4O+X+bAh7r4C1bgB5Vt8XAvTJZDO+1nCoewXIB+bTs59htG/hq4c9IhEREcmI/fvbyMsbxfjxeQDMmDGeDRv2M2/eBOrrW5kxo+DgsbNmFbJtW/IBKH6qmgkHf4+OLq6tbWXy5MQpaB58sJILLpiD2fCeszCZPiWGZjYTmImfy7AH59yKTAWVQQ8B7cCHgWtjtv8D8LJzbuugRCUiInIE6OzsZvTo7PW927GjiRkzxh/8PdrPsLvbMW9eUY9+fzNmjOeFF2qTFUNV1aH+hQBmdnAASnxi6JzjgQequPHGpZl9MkNEqMTQzOYBt+ObYOFQHz3HoX58ORmPrmcMlwX/XBL8vMDMaoAa59zjwTGdwC3OuY8COOf2mtmPgK+YWSPwHPB+YBlwUTbjFREROZI1N3dw/vn3smLFJeTmZic5jA48iVqyZAoPPFDJ+PG5BweeRM2cmXr1k8rKCMceO6nHtugAlLe+dWqP7evX1zJ2bA5HH92z/JEibI3hL4BZ+ImuX8PXwh1u8RNb3xT8fBxYGvw7h8QE9Wv4KXU+C5QDrwPvc87dn50wRUREZPfuZvbta2XLlgaOPnpiVq6RrMbwO995lvLyAhYsiE8M/XrJzrmEJuCqqkbe+c6eUxsvWFDMK68krt/xwAMjtxkZwk9X82bgX5xzNzjn/uqcezz+kc0gAZxzluKxNO6Yq+LO63LOfcc5N9s5l++cO94598dsxysiInIk27PHj+h99dX9vRyZqLm5gxtvfJHu7mQTixyyc2fPxLCsbBzjx+eyYsX2hBrDwsI88vJGsW9fa0I5lZV+cutYyZbG6+rq5qGHqrjggtl9fUrDRtjEcAeDU0soIiIiw1B19QHMYMOGvieGTz65ixtvfJE//Wlz2uN27OjZlAy+1rC6+gALFxYlHD9rVmHCmslNTe00N3cwZcq4HtsXLChi06b6Hsnp2rV7KS0dy7x5iWWPFGETw+8CXzKzgl6PFBERkSNedfUBTjhhcr9qDJ98cheXXE3sY58AACAASURBVDKfH/3oefbvT6zhi/JNyT1TkyVLJlNYmEtZ2biE45MtjbdtWyOzZvml8GIVFeVTWJjH7t1+Uuyurm7uuWcLF144p8/PZzgJ1cfQOfcbM1sEVJrZaqAu8RB3ZcajExERkWFpz54DnHnmdH7+81fo7nYJiVcqzjmefHI3v/rVOygszOUHP3iO7373rQnHdXV1U119gGnTetYYnnHGdHbvPpC0D6DvZ9hzAEpl5aGl8OItXFjE3XdvZv/+Vh57bDuTJo3l858/IdTzGK5C1Ria2VXAV4Bi4CTg7UkeIiIiIoCvMTz66GKKi/PYti35aOBkNm1qIC9vFLNnF3L11YtZvbqaNWuqE47bs6eFiRPzyc/vOeZ0ypRxXHPN4qRlz5w5PqEpOXYpvHinnFLOk0/uYvr08dx667n86U/vTGhyHmnCNiVfC/wJmOycm+6cmxv3GP6rRouIiEjGVFc3U15eQEVFSZ/6GT7xxC5OP30aZkZBQS5f/erJXHvtGtrbu3ocFz/wJIwZM8YnJKl+4EnyGsOPfvRY7rjjAj72sWNTHjPShE0MJwE3OefSrygtIiIigm9KLisbxzHHlPSpn+GTT/rEMOrss2cyZ04ht9yyocdxyQae9GbmzMTBJ1VVjUdM0hdG2MTwSaAim4GIiIjIyNDc3EFHRzdFRXlUVExkw4b4oQmpz3vxxVre8payg9vMjM9+9gR+97s36OrqPrg9fg7DMMrKxlJf30ZraycAW7c2sHVr4lQ1R7KwE1x/FviDmdXhl5lLeIWdc90JZ4mIiMgRp7ra1xaa2cEaw2QTS8d79tk9HHdcKQUFuT22H3XURCZPHstTT+3mjDOmAz4xPOWU8j7FlZMzimnTCvje99bx3HN7aWho5wMfOIri4vy+PcERLGxiGK2/vTXFfteHskRERGQE27PnAOXlfhqZyZPHkZNjVFcfYOrU9LPe+WbkqUn3XXLJfO6+e/PBxND3Mez7LHrvfvdcIpEOrr32VBYvLg09WvpIETaZuw6f/ImIiIikVV19gPLyQ6N3o7WGvSWGTzyxixtuODPpvgsvnMMPf+jnNSwpGdOvPoYAn/708X0+50gSdh7D5VmOQ0REREaIPXuae0wwHR2ZfPbZM1OeU1XVSFtbV8JSdlGFhXksWzaT++7bwgc/eDR1dW09kk/JjLCDTw4ys/FmNlOroIiIiEgyyWoMe5uyJjoaOV0/xEsvnc9dd21m164mysvHkZPT5zRGehH6jprZeWa2FqgHKoEGM1tjZudkKzgREREZfuITwzAjk+OnqUlmyZIpdHZ288ADVf1qRpbehV355DzgL8B44NvAPwPfAQqBB5QcioiISFR0DsOoGTPG09zckXLdY+cc69fXsmTJlLTlmhmXXDKfW27Z0OepaiScsDWGy4FHgGOcc9c6524O+h0eC/wVvzKKiIiISEKNoZmlXQFl//42nHOUlo7pteyLL55HS0unagyzJGxiuBi4MX6uwuD3m4CRvaK0iIiIhNLc3EFbW1fC3IAVFalXQNm8uZ7584t6necQ/PQ3558/m0WLkg9SkYEJO11NG5BqvZjCYL+IiIgc4fbuPTS5dayKiomsWrUz6TmbNzcwf35R6Gtcf/3pA4pRUgtbY7gK+LaZzY3daGaz8M3MKzMbloiIiAxH8c3IUenWTN68OdKnxFCyJ2xi+CWgCHjdzP5mZneY2ePARqA42C8iIiJHuFSJ4dy5E6ipaaGpqT1h3+bNDcybp8RwKAiVGDrn3gCOB34C5AMnAWOA/wZOcM5tzFqEIiIiMiQ456ivT997LH5EclROziiOOqqY115LnLYm2sdQBl/oeQydc7udc190zp3inFsY/Pw359zubAYoIiIiQ8MLL9Tymc+sSntMqhpDiE503TMxbGhoo6WlS6uYDBGaMlxERERC2b27mdralrTHVFc3U16efHG0ioqJCf0Mt2yJMG/ehFAjkiX7wo5KxsyuBD4IzMI3I8dyzrn5mQxMREREhpaampZ+NyWDrzG87bbXe2xTM/LQEioxNLNv4Cexfhl4AU1PIyIicsSpqWmhsbGDzs5uRo9O3uiYril5wYJitm1rpLW1kzFjfArS16lqJLvC1hh+FPhv59znsxmMiIiIDF01Nb4ZORJpp6QkcZWSlpZOWlo6mTgxP2EfQF5eDnPmTGDjxnqOO64U8FPVvOUt5dkLWvokbB/DScD92QxEREREhrZo/8JUzcnRZuR0/QXjV0BRjeHQEjYxfBy/LJ6IiIgcoWpqWhg3bnTKxDBdM3JU7Mjk1tZu6upamT49+WAVOfzCJoafAz5iZleYWamZjYp/ZDNIERERGXw1NS0sWFDca41hOhUVE9mwwdcYVld3Mnv2BHJylEYMFWH7GL4R/PxViv2uD2WJiIjIMNPe3kVzcydz5hRSX5+4egn46WxSTVUTdfTRE9m0qZ6Ojm6qqztYsEDNyENJ2GTuOnzyJyIiIkeg2tpWSkvHMHFiftoaw4ULi9OWU1CQS3l5AVu3NlBd3cmiRUoMh5JQiaFzbnmW4xAREZEhrKamhdLSMRQVpU8MTz99Wq9lRfsZ7t7dwYUXKjEcStSoLyIiIr2qqWlh8uSxFBfn09CQOjGcMmVsr2UtWuRXQKmu7tSI5CGmLyuf5AEXAEeTfOWTb2cyMBERERk6amtbKC2NJobJ+xjW1rYyeXLvieExx5TwyCMvUFfXyaxZhZkOVQYg7Mon04AngTn4vobRCYpi+x0qMRQRERmhYmsMkzUld3X5qWeSTXwdr6KihJde2kd5+Whyc9V4OZSEfTWuB2rw6yQbcAowD/h3YFPwbxERERmhamt9YlhUlJc0Mayvb2f8+Dzy8nJ6Lau4OJ9p0wqYOjU3G6HKAIRtSn478EVgV/B7t3OuEvimmeUAPwEuynx4IiIiMhT4wSepawx9U3PvtYVRFRUl5OXVZzJEyYC+LIm3yznXDTQDE2P2rQCWZjguERERGULim5Kd6zmLXbQPYlif+tSbOO00rXgy1IRNDHcApcG/NwPnxux7C9CayaBERERkaIkmhmPHjsbMaGnp6rE/Os9hWMceO4nSUq2NMdSEfUVWAmcC9wA3Azea2QlAB3BesE1ERERGoO5ux/79bUya5BO/6JQ148YdSiP6WmMoQ1PYxPDrQAmAc+5/zGw08H5gHPCf+JVRREREZASqq2ujsDD34MCS4mI/AGXq1ENNwX5wSvp1kmXoC7vySS1QG/P7DcAN2QpKREREho7owJOoZANQamtbqagoOdyhSYZp8iAREZER7okndtHW1tX7gSlE+xdG+WXxek5yrabkkUGJoYiIyAjW3e343Oce5w9/2NjvMqJzGEYVFeUlLItXUxNu1RMZ2pQYioiIjGDV1QcA+PnPX+bAgc5+lRFfY5i8Kblv8xjK0KTEUEREZASrrIxw/PGlvPnNZfz2t6/3qwzfx/BQ0hcdlRzV3t7FgQOdFBXlDzheGVxKDEVEREawLVsamDNnAp/5zPH8+tev0tjY3vtJcZLXGB4qp7a2lUmTxjBqlGUkZhk8fU4MzWy8mc02My1wKCIiMgRs2lTPffdtSbqvsjLCvHlFzJtXxBlnTOfWW1/rc/nxfQzjm5L9fjUjjwShE0Mze5eZPQc04Fc/OS7Y/gsz+1CW4ou9/kwz+6OZNZhZxMzuNrNZIc91KR4nZDtuERGRbHvooaqUCd+WLRHmzp0AwKc/fRy33/560rWO04mfrqaoKC8hMdSI5JEhVGJoZhcD9+LnMvxS3HlbgSszH1qP64/Dr8m8KLjWPwILgZVmFnahxV8Dp8U93sh4sCIiIofZiy/W8sYb9bS3J05Js3XrocRw5sxCzj13Fv/3f6+GLts5F6LGsFWJ4QgRtsbwW8CvnHPnAj+O2/cy8KaMRpXo48A84GLn3D3OuXuB9wCzgU+GLGOnc2513ONAtgIWERE5HLq7HS++uI/i4jw2b27osa+5uYOGhp4rlFx55SL+/Oetoctvbu4AjIKCQz3IkjUla0TyyBA2MawA7gj+7eL21QGTMhZRcu8BVjvnNkU3OOe2Ak8BF2X52iIiIkPW1q0RioryOPXUcl59dX+PfZWVEWbPLuwxKGTGjEJqa1vp7OwOVX78wBOACRPyaGrqoKvLl6Eaw5EjbGIYAUpT7JsD1GQkmtSOxddMxnsFOCZkGZ82szYzO2BmK8zs7ZkLT0REZHC88EINixeXUlFRwoYNdT32+Wbkoh7bcnNHUVKSz969LaHKr61NnLh69OhRFBTk0tjYERyjPoYjRdjE8K/AV8ysOGabM7N84GrgwYxH1lMJvmYy3n5gYojzbwP+GXgH8Al8DecKM1uaqQBFREQGw/r1tSxePJljjilhw4aeNYax/QtjlZcXUF3dHKr8ZDWG0HMAipqSR47RIY/7GrAGeB14AN+c/GXgeKAIuDgr0fUU34QNEGrCJOfcP8b8+oSZ3YuvgfwOcHpCoWafwCeQlJWVsWrVqj4H2xdNTU1Zv8ZIoXsVnu5VOLpP4elehRN7n/bv76S93VFennyGtw0bWqmoGFhC9fe/V7NgQYR9+0bz6qu1rFix8mDT8TPP7GPx4rGsWtWzbmX06GZWrFhLJDKu1/KffrqR1tauhNc+J6eNlSufZu7cfLZvr2Pz5heJRMKmFZ7eU+EdtnvlnAv1AGYAvwR2AO3AbuBXwMywZfT3AewBbk6y/Sagpp9l3gS09XbckiVLXLatXLky69cYKXSvwtO9Ckf3KTzdq3Bi79OPf/y8W758ddLj2to6XUXFb1xTU3u/rxWJtLmTTvqda2/vcs45d845f3KbN9cf3H/xxfe7V16pTTjve99b637xi5dDXeP669e5n/3spYTtH//4Y27Vqu2uu7vbnXjib/v1PPSeCi/svQLWugHkXKFSezMrChKwj2Y0Kw3vFXw/w3jHAOHH3PdkJK+FFBERyYht2xrp7k7+VRNtho1E2nuM+O2Ll17axzHHlJCb63uGRZuT580rorvbUVXVyOzZiU3JU6eOY9u2plDXqK1tYcGCooTtxcV51Ne309zcwahR1u/nIENLr30MzWw0sA84N/vhpHQfcKqZzYtuMLM5wNuCfX1iZhOAdwLPZCg+ERGRBNu2NaacTLquzm9vaOj7EnVR69f7gSdRixZNPDgyeffuZoqL85MmbFOnDryPYXTKGo1IHll6TQydc534ptzEWTMPn58DlcC9ZnaRmb0HP+H2duDm6EHBUn2dZvbNmG1fNLOfm9mHzGypmV2Jn+amHPj6YX0WIiJyxHDOpU0Mo9sbGvq2CkmsF16o5YQTDiWGvsbQ9yeMXfEkXnn5OHbvzlRiqIEnI0nYUcm3AR/LZiDpOOeagWX4lUp+A9yOX3FlmXMuti7cgBx6Pq/X8U3OP8GPrv5hcO7pzrknsh+9iIgcierr22hq6jhYMxhvoDWGzjlefLG2R41hRUUJr766H+cclZXpEsMCqqt7X+PBOcfevcmnoikuzqehoZ2aGtUYjiRhhw9VAh8ys2fxNXW7ieuf55z7v8yG1pNzbhtwaS/HVBI3Utk5dz9wf/YiExERSbRtWyMLFhRTWRnBOYdZz4k0BlpjWFkZoaAgl8mTD40snjx5LPn5Oeza1cyWLQ0sWFCc9NxJk8bQ3NxBa2snY8akTgU2bWpg/PhcJk7MT9jXs8ZQieFIETYxvDH4OR1YkmS/A7KaGIqIiAwnVVWNLFxYxI4dTRw40JnQ16+urhXof43h+vU9m5GjKiomsmHDfrZujXDOObOSnjtqlFFWNo7q6gPMmZO8VhHgsce2s2zZjISkFtSUPFKFbUqe28tjXupTRUREjjzbtjUya1YhEyfmJ21OrqtrY9KkMf2uMXzhBT+xdbxoP8NUk1tHhelnuGLFdpYtm5l0X3SC62Qro8jwFSoxdM5V9fbIdqAiIiLDSc/EsDVhf319G3PmTBhAjWHPEclRFRUlrFmzh6amDsrKUk9g7Ucmp+5nuGfPAbZvb2LJkilJ96vGcGQKW2MoIiIifbBtW9PBxLC+PjH584lhYb9qDJubO9i2rZFFixJXhT3mmBLWrdvL3LkTDq6Akkx5eUHaGsOVK3dwxhnTD86RGM8PPlEfw5Em7ATXW+llMmjnnJqTRUREAtEaw+Li5DWGdXVtnHJKOU88savPZb/wQg0VFSXk5eUk7Js2rYAJE/LSNiODb0qOznmYzGOPbeeyyxak3D9u3Gg6Ohw7dzYrMRxBwtYYPp7k8TIwIShjVTaCExERGY4aGtpob+9i0qQxB5tc49XVtTF7dv9qDNet25uyidfMqKgo6TUxTDfJdWNjOy+8UMvpp09Leb6ZUVycRyTSTkmJmpJHilA1hs65q5JtN7Ni4CHg0QzGJCIiMqxt3+6bkc0s7eCT2bP718dw3bq9fPSjyVaK9a68chHTphWkLWPq1HHs2pW8j+GTT+7ipJMm97rMXXFxPl1dLmVzsww/A3olnXP1wPXAN3s7VkRE5EgRbUYGmDhxTEJi2NbWRWdnN9OmFfS5xrC9vYuXX97PiScmjkiOWrp0Bkcdldj/MJaf5LoZ5xJ7iq1YsYNly2b0Gktxcb6akUeYTKT4rUDv7x4REZEjRGximKwpub6+jYkT8w/202trC7/q7Cuv7Gf27EIKC/MGFGNhYS5mRmNjR4/t7e1dPPHErlCJYVFRvkYkjzD9TgzNbLSZnQAsB17JWEQiIiLD3LZtjcyeHa0xTEwM6+p8YmhmFBXlEYmErzVM17+wL8ws6VyGa9fuZc6cCT1WVEmluDhPNYYjTKjE0My6zawr9gG0AeuABcDnsxmkiIjIcBJfYxjflFxX10pxsV9mrqgo+XQ2qWQqMQTfzzB+AIqf1DpcQ2BxcT6TJqnGcCQJuyTedSROV9MKVAEPOucaMhqViIjIMFZVFdvHMHG6mvr6toOJYXFxXugBKN3djuefr+G6607NSJxTpxawe/ehAShdXd088sg2brnlnFDnX3bZwozEIUNH2FHJy7Mch4iIyIjQ2tpNc3PHwWXifB/DdpxzB9cc9n0MfU1bUVFe6AEoGzfWU1ycn7El6OKbkteu3cvkyWOZO7co1PnR5nIZOcI2Ja8ws0Up9h1lZisyG5aIiMjwVFPTycyZhQdXHcnLyyE/P4empkODPKJ9DME3JYetMcxkMzJERyYfqjF84IFKLrxwTsbKl+En7OCTpfjJrJMpBM7MSDQiIiLDXE1N58Fm5Kj4ASh1dW0xfQzD1xhmOjGMneS6o6Obv/51O+efPztj5cvw05dRyamWxJsPNGUgFhERkWHPJ4bje2wrLs7rMQAlto9h2BpD51wWagzHHawxfPrp3cydO4Hp08f3cpaMZCn7GJrZR4CPBL864Gdm1hh32FjgTcBj2QlPRERkeKmp6eSEE3rWGBYX95zkumdTch579tT3Wu7Onc10d7uEpHMgoolhd7fjwQeruOAC1RYe6dLVGHYDXcHD4n6PPvYB/wN8NLthioiI9F9bWxeVlZHDcq0wTcnRCa4hfI1htLYwOoAlE8aMGU1hYS67dzezcuUOzjtPieGRLmWNoXPuFuAWADNbCXzaOffa4QpMREQkU556ahe33/46v/zlO7J+rZqarqSJYeyUNf3pY5jpZuSo8vIC/vCHjVRUlGRstLMMX6H6GDrnzlJSKCIiw1V9fTuRSPhJpPurpaWTpqYuyst7rhoSP8l1fX1rzHQ14WoMX3yxlhNOSL0+cn+Vl4/j979/gwsvVG2hhJ/gGgAzWwwcDSRMc+6cuzVTQYmIiGRSJNJ2WBLDysoIpaWjycnpWe9SXJx/cL7AlpZOurocY8fmHNzXW41hd7ejqqqRuXNTTRDSf1OnFtDS0sk558zKeNky/IRKDM2sGPgLEJ1qPdrBIXakshJDEREZkiKRdhobO3o/cIDWrt3L/Pn5Cdtj+xhGJ7eO9hX0Tcnpk9bq6gMUFeVRUJCb8ZinTSvgtNOmHmzaliNb2BrD7wKTgDOAJ4D3Ag3APwGnAR/ISnQiIiIZEIl00NjYc/WRbHjmmWqOPjp5YhhtSo6dqgZg/PhcWls76ejoJjc3eQ+vysoIc+ZkvrYQ4PLLF/Cud83JStky/ISdx/A8fHK4Ovh9h3NulXPuCuBR4LPZCE5ERCQTIpE2urocLS1dWbtGV1c3a9fu5aijkiWGh6ariZ2qBsDMKCzMo7Exda1hVVUka8vPjR+fx+TJ43o/UI4IYRPDqcAW51wX0Ipf7STqbuCdmQ5MREQkU6L9C9MlXwO1YUMdU6aMZcKEnIR9fr3k2KbknsljbyOTq6oamT07OzWGIrHCJobVQHHw7yp883HUgoxGJCIikmHRxDCbA1CeeaaaU04pT7ovmvg553pMVXNof/qRyb4pOTs1hiKxwvYxfBKfDP4Z+A3wLTObA3QCVwL3ZSM4ERGRTIhE2hk/PjerNYbPPFPN+953FLA5YV9eXg5jxoymsbGDurrWJIlh+hrDykrVGMrhEbbG8FrgoeDf1wM34puPP4hPCq/JfGgiIkPXs8/uYdWqHYMdxpD0l79s5dVX9w12GD00NLQzffr4fiWGr79ex333bUl7THt7F88/X8ub35x6AuroAJT6+vYkTcmpaww7OrrZvbuZmTO1hrFkX9gJrjc7554I/t3hnPuCc26Gc67EOfch59zQ+h9ARCTL/vCHjTzyyLbBDmPIqapq5Gtfe5qnn64e7FAOcs4RibQzY8Z4IpG+T1nz3HN7+dWvNqQ95qWX9jFnTiFFRamnfPH9DFuDya2T1RgmTwx37myivHwceXmJfRdFMq3XxNDM8sxsv5m953AEJCIy1DnnWL26mr17WwY7lCHFOcd3vrOGSZPGhlrJ43Bpa+vCDCZPHtuvGsOGhnbeeKMubf/EdP0Lo6I1hn5Ucs91IoqKeq6lHKuyMqJmZDlsek0MnXPt+L6Erb0dKyJyJNi8uYFIpJ3q6gODHcqQ8tBDVezd28I//dMxodb+PVwikXYmTMgL+hj2vcawoaEd5+D552tSHhMmMYwui+cHn+T12JeuxtAnhhp4IodH2D6G9wCXZTMQEZHh4pln9nDmmdPZu1eJYVRjYzvf//46li8/hZKSMUOqxrChwSeGEyaknyswlUiknbKycaxbtzfp/paWTl5+eT8nnZR+HePo6id+guvEGsNUyXRVVWPWJrcWiRc2MXwQuMDM/mhm/2BmZ5vZsthHNoMUEemvL33pKaqqGjNa5jPPVHPOObPo7OymuTn7y6yl84UvPDEkauf++7/Xc+aZ0znxxMmhlng7nKI1hoWF/RuVHIm0c9ZZM3juueSJ4fPP11BRMbHX5eqKi8cEg09SzWOYqsawUVPVyGETNjG8C5gOXIJfE/mv+BVPYn+KiAwpXV3dPPxwFX/848aMlrlmzR5OPbWMKVPGsWfP4NUadnZ288gj29i5s2nQYgB49dV9PPJIFZ///ImAT3IikcFPVqOiieGECXn9mscwEmnjjDOmsWHDftraEldOCdOMDL7GcPfuZgDGju05W1z6GsPsLYcnEi9sYnhWiseymJ8iIkPKrl3N5OSM4t57t9DR0Z2RMl9/vY7S0rFMnjyOsrKxg5oY7tlzgK4uR03N4HYB//GPX+Cf//n4g3Pz9TZZ8+F2qMYwr999DMvLC5g/v4iXXkqchGP16mpOOaWs13ImTsxn69ZIwhyGkLrGsKWlk7q6NsrLtWSdHB6hJrh2zj2e7UBERDJt69YIJ544mdbWTv72t52cffbMAZe5erWvLQQoKxs3qCOTo7VPtbWDF8PatXvZujXCJZfMP7htaDYl5/e6HnH68/NYsmQK69bt5eSTD81VuHlzA7t2NXPCCen7F4IffLJ1a/KBJMXFyWsMt21rZMaM8eTkhK3HERmYPr3TzKzUzN5lZleaWUmwbYyZ6R0rIkPO1q2+Ce6SSxZw992bMlJmbLNhWdm4QR2ZvHNnNDHMTI2hc67Px//kJ762MHaOvYKCXFpbOzNWSztQkUg7RUV5TJjQvz6GDQ3+/GhiGOvWWzfwgQ8cFWqOweLifFpaOhP6FwIUFubS1NRBd3fP18AvhadmZDl8QiV05l0P7MCvdPJ/wJxg973A17ISnYjIAGzdGmHevAmcd94s1q2roaZmYElce3sXzz1Xw5vf7GsMp0wZN6gjk3fubGL8+NyM1BiuX1/LBz7wUJ+Sw6efrqa2toV3v3tuj+2jRlm/a+ey4dB0NX1vSu7o6Kajo4tx40Zz0klTWL++hq4un/DW1bXy8MPb+MAHjgpVVjQhTJYY5uSMoqAgMXH1S+Fp4IkcPmFr+r4CXA1cB5wCWMy++4F3ZTguEZEB27q1gTlzJlBQkMu5587innvSL2vWm5df3sesWeMP9hErLx/cwSe7djVz3HGTMpIYvvzyPl56aR+vvLI/1PG+tnA9V1+9mNGjE79K+jvQIxsikXYKCw8NPkmW/C5f/kzSPxyiSaWZUVIyhsmTx/L66/UA3HHHRt7xjplMmjQm4bxkoquixE9Vc2h/YhO8H3iixFAOn7CJ4ceA65xz3wWei9u3CZifeIqIyODassXXGAJceul87r57c9oasQcfrOSVV1Kv8Bk/+nTwawybOf740ow0JW/cWM+0aQXcdVe4JvfHH99JS0sn558/O+l+n+QMjZHJkUgbEybkkZ+fgxlJRxY/9th2KisTpzVqaPDnRkWbk9vbu/jd797giisWhY4jN3cUEybkJa0xhOT3THMYyuEWNjGcDqxOsa8dKMhMOCIimRGJtNPS0klZmR/NefzxpYwePSrlJMXNzR18+9vPcs01j7NvX/JE65ln9vRIDMvKxg5qH8Ndu5pYvLg0IzWGmzbV8y//spgHH6yipaUz7bHR2sJrrlnMqFGW9Bi/xNvQqDGM9hEEkjZxd3Z2s39/K/v3J77u5PBteAAAIABJREFU0YErUdHE8MEHq1iwoIijjprYp1iKivKSjkr2+xLvmZbDk8MtbGK4E3hTin2Lga2ZCUdEJDOiA0/MfOJiZlx66Xzuumtz0uPvvXcLJ588hYsumscXv/jkwX5kUa2tnbz00r4eI1JLS8dSX98+KIMsurq6qa4+wHHHDbzG0DnHxo31nH76NBYvLuWvf92W9vitWyM0NLRz9tkzUh7j5zIcGolhtDkYok3cPfsZ1tW14hzU1SXWcEZXTYmKJoa33LKBK66o6HMsEyeOCV1j2NDQRltbF6Wl4ZqqRTIhbGJ4J/BNM3tbzDZnZkcBXwB+n/HIREQGoLIywty5PWtaLrpoHo8/vpPKykiP7d3djt/85jWuuKKCq68+HjP4yU/WH9y/cWM9V131KEuXTu+xusXo0aMoKckflOliampaKC7Op6Qkn87Obg4cSF/Ll86ePQfIy8uhpGQMl166oNfm5BdeqOXEE0sPJt3JDK2m5EPJXWFhHk1NPRPW6DyQyWqKoyOao6ZNKyA3dxTt7V28/e3T+hzL2WfPYNGi5LWM8fM/RpuR091nkUwLmxguB14D/gZElxC4E3gp+P17GY9MRGQAtmxpONi/MGrixDF86lNv4tpr1/Toa/j44zsZPz6XJUsmk5Mzih/84HTuv38rDz9cxQ03rOeqq/7KJZfM5/rrT0+4TlnZ4AxA2bmzmenTCzAzSkvHsG9f/5PTjRsbWLiwGIClS6ezZUsk7TKC69fXsHhx+nn7htLgk8bG2MQwN6HGMJrY19UlJoYNDW09EkMz49xzZ/HJTx6Xshk9nY9//E3Mm1eUdF98Mu2nqtHAEzm8QiWGzrkWYClwFfB3/DJ4zwKfAM5xzg2NT7+ISMA3JSd+AX/oQ0fT0NDGn/9ceXDbrbdu4MorKw7WzJSUjOFHP3o7/+//PcVrr9Vx993v5H3vW5g0ERi8xLCJ6dPHA75JeyDNyRs31h9MDPPycnjXu+amnfdx/fpaFi8uTVvmUFn9pL29i46ObsaN8+s5JOtjWFvbypgxOSlrDGObkgG+/OWTE6boyYTYexaJtPPAA5Upk0iRbAm18gmAc64L+E3wEBEZ0qJzGMYbPXoUy5efwtVXr+KMM6axfXs7lZVNnHdez9G1ixdPZsWK9zJp0pi0TXl+ZHL62rrq6mZuvvlluroO1VJecsn8UKtlpLJrVzNTp/pxfz4x7H+N4aZN9Zx00qFYLr10Ph/72GNcc03iVDTNzR1s396Ysjk0qqgojw0bwk19k03RqWqir6GvMYxPDFtYsKAoaR/DSKT9sC1HF71njz66ne98Zw1nnTWDK6/sez9GkYEInRgCmNkC4C34Uco7gGedc5lZTkBEJEM6O7vZvj31xMDHH1/KOefM4kc/eoGqqiY+9KGjyM1NbEApLR3b67X8yOTmtMf8/OevEIm0c+qpfkTzyy/v47bbXhtwYnjssSVBnGMGlBhu3FjP+99/aJLmBQuKmTq1gCef3MXSpT0HmLz4Yi2LFpX0utLHUGlKjq/xmzAhWY1hC0cdNZH162uTnn/00X0bedxfRUX5PPzwNtavr+X6608/OJG6yOEUduWTMWb2f8AG4Dbg+8DtwAYz+4WZJR9ilUFmNtPM/mhmDWYWMbO7zWxWyHPHmNn1ZrbbzFrM7GkzOyPbMYvI4Ni5s5nJk8cyZkzqv30/+9kTWLlyBy++2MLlly/s97V6Wy+5vr6Nv/ylki9/eQmXX76Qyy9fyKc/fRx//3t1wsjnvshUU3JXVzdbtjSwYEHPJsuLL57HffclTjgRphkZhk5Tcnxi6JuS4/sYtnLUUcUp+hgmNiVny/HHl3LNNYv505/eqaRQBk3YwSc/AD4MfAtYABQGP5cD/whcn43gosxsHLACWARcGVxzIbDSzMLMofhL4OPAN/GrtOwGHjazE7ITsYgMpq1bG5g7N33frAkT8rjuulM4//wJKeeVC6O3PoZ33rmRpUunM3nyoebI8vICJk8ey8sv97+pddeuZqZNizYl///2zjs8jupq3O9Vl6xqq7nbsmRcscE2NmDcqHECGMiPJMCXBEihpJEGJAQCMQl8ISRAygeEGkILJqEEMKYYbIpNc2+yZEs2km313nV/f9xZabtmV7uq532eeVY7c2fm7tHszNlTg7cYHj7cwMiRcS7Z1gBnnTWBjRtLaGhwVaK2bi23ZelMTvbs4tEfONcwBN8Ww9zcVGprWz2Udffkk3AyalQcV1wxw+8PGkEIN3YVw68Ct2qtf6u1LtRaN1ivt2Pa5F0SvikCRqnLAVZprf+jtX4BOA+YCHzX345KqTnW/K7TWj+otX4TuBgoxsxdEIQhRmGhZ6kabyxdOo4zz+xd1qc/xbCtrZMnn9zntd7d4sWj2bixJKhzdnZqSkudFcPgYwydE0+cSUuL44QTMli//nDXOq21bYthaurAKFfjbjFMTPTsR1xe3kR2dgIjRkR7KLPekk8EYShjVzGMBTb72LYJCPe35jzgQ+d4Rq31AeA94Hwb+7YBzzjt246pvXh2X7jBBUHoW7zVMAwXjuQTb6321q4tYuLEJGbMGOmxbfHiMUErhuXlTSQlxXRZlnrjSt6/v5rcXE/FEGDlykm88kpR1/uysnbi4iK7usn4IznZuJL9tSDsLW+9dYgXXvDf/9qUqum+zScleRa4Li9vJj09nlGj4jy6n4hiKAw37CqGbwBn+dh2FsbNG05mAju8rN8JzLCx7wGttftP+p0YhTa399MThOHB+++Xkp9f3d/T6BFvNQzDRUJCFLGxkR6WJq01jz++22cv3XnzMtm/v4bq6sCtap9/3m0tBONKLisLrcUQYMWKcXz00dEuy9+BA622rIUAsbGRREUpmpo8+xKHiuee28/vfvex3ySXnpJPmpraaW3tICkpmrQ074phSorYD4Thg13F8G7gYqXUX5RSy5RS063Xv2LcsncppXIcSxjmORKo8rK+EugpXczfvo7tgiDY4L77tvLSSwO/A2ZfWgwBMjM9eyZ/+mkZtbVtHlm9DmJiIlmwIJMPPigN+HwlJd2JJ2AshhUVzUFZ5/wphklJMSxalM2bbxp3cmFha0CZ1CYBJTDF98CBGo+4Rm+0t3fy8cfHOPHEDB57bLfPcTU1rSQldcdPJiW5upIrKoy1UCnFyJGxVFZ2z7elpYOODk1cnP8MbEEYStiNcH3Her0auMppvXLb7iAc3yJvdzw7ZedVoPsqpb6DKd5NVlYW69evtzO/oKmvrw/7OYYKIiv7hFpW9fUdbNtWTmtrHSeeWBOy44aa+voOGhtb2bFjk61WYqGQU0xMM2+88SFHjnSXt7n//nIWLYrj3Xfdb4/dZGU18K9/fUp8/MGAzrdhQy0dHZ0u846K0rzyytuMGGH39z60t2uKimo4dGgLR454l9XEiY3885+fMnLkIQoKmlm0qJj164/aOn5kZCtvvvke48bZc8V2dmpuueUIS5Yk9hj7eeBAC8nJsGJFB3feuZPJk8tJTPR89OzbV0l7eyzr15cBUF3dQXl59/+8sLCFmJg21q9fT3NzFZs21REXZ3781NR0EB8P77zj+3/oDblP2UdkZZ++kpVdxfDysM6iZ6rwbtlLw7s10JlKwFtZmzSn7S5orR8AHgCYP3++XrZsme2JBsP69esJ9zmGCiIr+4RaVq++epBp05o5erSRpUuXDtj+rZ99VkZeXivLly+3NT4UcnrjjQ/Iykpn2TJT9ubQoTqKil7j738/2yPb15mcnDouu2xtwPJ8++0PmTMnjWXLjutaN3r0i0ydeqJP65838vOrGT++jjPP9C2rhQvbefbZNeTmzqe8/AUuvfSMHmsYOnj44deZOvV4Tjop29b4deuKaW4+RklJfI//k337dnD66aP48pfns3PnJvbujeYnPznRY9yaNe+wYMFkli0zj4GmpnZuvfVfXcdvby8mJ6eQZcuWsX37VpSCZcvmACb+Mj393YCvD7lP2UdkZZ++kpXdlniPBbKEYZ47MbGC7swAdtnYd7JV8sZ931ZACnQLgg02bCjhwgtNSG6oWsAdO9bIU0/t5fLL3+D73/dtlfnZzzaybZtn8WFvHDjQt25kMJnJzq7kJ57Yy4UXTvGrFAJMmJBEQkI0e/cGFrdpMpITXdYFk5mcn+878cRBfHwUS5aM5e67P2Ps2GjbSiEEXsvwscd2c+ON89m+vbxHd/KmTUdYuNDU+rvqqtk899x+r3GWtbUtLjGGcXGRdHRoWltN7KMj8QRMK0Tn7icSXygMR+z7HPqXF4FFzvGLSqlJwKnWtp72jQb+n9O+UcBXgNe11v1fT0EQBjhaazZuLOG008YwY8ZIdu/uyVDvn5aWDr797Tc577yX2bKlnLPOGs/OnRU+x+/YUeFSNsUf+/dXM2lS3yqGmZnxHDtmFMO6ulZeeKGQSy89roe9DMFkJ3/+eQNjx7qWcDW1DAPLTN63z3d8oTMrV07itdeKmDw5sOzcQLqfbN9ezpEjjZx33mRmz05n82bf7urW1g62bi1n/nyjGGZlJbBqVQ4PPOCZo+iefKKUsuIMjeJZVtZEenocACNHxrr0S5aMZGE4YlsxVEqdo5R6SCn1ulLqXbclsACMwHkQOAi8oJQ6Xyl1HvACcAi432mOE5VS7Uqpmx3rtNZbMKVq/qSU+pZS6nRMqZrJmILdgiD0wN691SQkRDNhQhLTpo1k167e9cB98MEdxMVF8e67F3Hnnady4YW5lJf7Tp4oK2ti06ae49q01rz11mEWLx7Tq/kFSnZ2AkePGmvVmjX7Wbx4dFcf45447bTA6hlqra3i1qGxGNpRDE89dTTJyTEBK4YpKTG2s64ff3wPl112HFFRET3WeNy6tZwpU1JclLZvfWsWL798wKM9YW1tm0eBatP9xCis5eVNbhbDbsWwL7ueCMJAwW5LvJ8Dr2C6howAOtyW4Ps62UBr3QCsAPYB/8C04zsArNBa1ztPFZP44v65LgceAVYD/wXGA+dorT8N57wFYaiwcWNJl7JlLIbBK4YHDtTw5JP7+OUv53e5JWNjI0lIiPLqdmxoaKO9vZM9e6p6dC/u2lWJ1rqrh3BfYWoZNtLe3skTT+z1WtDaFwsWZLNjR4WtTFwwWbQJCVEkJLiGiAejGO7fX01env8OMWAyqP/4x9OYNSsuoOPbtRiWljawYUMJF11kQhVOO20MGzaU+PyhYNzIrnGLo0bFcdppY3jvPdcs79raVpKSXJW75OTornm5u5IrKlxdyaIYCsMNuxbD72Esc2O01qdqrZe7L2GcIwBa62Kt9UVa62StdZLWepXW+qDbmINaa6W1/rXb+iat9Y+11tla6zit9UKt9fpwz1kQBgJaa9tKhy+MG3k0ANOnB+9K1lpz220fcdVVs8jOdneFxnuNESsvbyYzM4FZs0byySfH/B7/lVcOsnLlpD5PjHHEGL7xxiGyshI4/nh7tf7A1EHMy0u1LVNvbmQI3JXc0dFJaWkD48bZ6/xy8smjiY0NLPooNdVejOGTT+7l/PNzuhS4vLxUWls7KCqq8zp+06ajXfGFzsydm8GWLWVd79vbO2lubicx0TXW091imJFhFN60tFg3i2GLKIbCsMPutzwZ+JfWOnyVSgVBCAsff3yM73wn+Br0DQ1t7NhRwYIFxkIzfnwidXWtQRVmfumlA9TWtnDJJZ7xdxkZ3hXDsrImMjLiWbgwm02bjvg8dmen5tVXi1i5clLA8+otaWmxNDe38+CDO3wWtPbH6NEjumIUe6KkpN7DjQyBWwxralpJTIwhOjp8oeamX7L/66ShoY01awq47LLua0Ip5TP2srGxnV27KjnhhEyPbXPnprN1a3eSUl1dKyNGRBMR4fpDwbn7ibPFMDU11qVfcm1ta6/6aAvCYMTuHWEtsCicExGE4cbGjSUsWvQsCxc+07W8/ba9BItAKCioYc+eKjo7g2tN9uGHR5gzJ73LdRkRoZg2LS3gOMPq6hbuuutTbrllIVFRnrceX907XBVD33GGW7aUkZgYHVC5llChlCIzM4GamlZOP318wPtnZSVQWtrgddv775dy7rkvcffdn7FtW7lH1xMHgbbFq6xsZuTI8Co9KSk9u5Kff76ABQsyGT/e1XLpSzH87LNjzJgx0sOVDjB1aholJQ1d1sCamlaP+EIwCmt9vWnXV17exKhRxmIYFRVBUlIM1dXd+4vFUBhuBOJKXqmUulEpNc+5y0kYu50IwpBmz54qzj13MuvWXcC6dRdw4YVT2Lu3d9m+3igqqqOpqZ3PP6/vebAXnOMLHQQTZ3j//ds588wJPt2sGRneLV4mOSCO2bNHUVRU59NS+cor/WMtdJCdndCVPBHMvr5KAO3eXcmUKSkoBTfc8B733LOFceO8WQzjArIYGsUwsJjBQOmpXE1jo7GyXnXVbI9tJ5+czSeflNHS4uqo8uVGBqPYzZgxku3bTYa7rxjBxEQTY1hb20pcXGRXz2kwcYaOtngSYygMR+zewTRQB9wObAbyvSyC0Kf8978HPB4ag4kjRxqYMCGJ5OQYkpNjGDcuiWPHgut364/i4jqioiKC6nHsKFPjrhiaOMPAFMNduyo580xvteYN/lzJ6enxxMREcsIJ6Xz8safVsL29k7VrizjnnIkBzSmU/O53p3DppYG7kcG/YlhS0sC8eZlcd90J/Pe/5/HCC19i1aopHuPS0mKpq2ulrc1eLmBlZUvYFcOekk+eemov8+ZlMn26Z7JQSkosU6emesSVbtp0hEWLfBfMdo4z9KXYmX7JbS5uZAemLZ4ohsLwxa5i+ChwCvBHTEu8K7wsgtBnrFmzn5/97L1el03pT0pLG1xKmmRl+VYOekNxcR2LFmWRnx94G7uDB2vp6NDk5rpmrs6Y4VmypqOjk4MHa30ey+ES9oUvxdAkB5j9Fi7M5sMPPRXDjz46SnZ2Qp/XL3Rm7NjEoOP1srMTPHotOzDFrM11opQiJyfFqxs1MjKC1NRupaYnKiubSUsLvyvZV4xhfX0rjzyyi+99b47P/RcvHs2GDd3u5M8/r6egoMZvcs/xx6ezZYuJM6yr867YOZJPHNZoZ4zF0MzZlytaEIYydlviLQO+p7V+NHxTEQR77NpVwd13f8bs2aM4fLieE07I6O8pBUVpaSOjR3c35MnKig+5YtjZqTl8uJ5LLpnKp5+W9byDGx9/fIyFC7M9snwnT07m6NFGGhraurp7PP10Pg88sIN33rnI67HKypr9Koa+spKdFcqFC7O58cb3Pca8+moRX/jCJLsfa8Dh3jnFGfcfEP5wJKBkZbk3evKkL1zJSUkxNDS009HRSWSkq9L8+ON7OPXUMUyZ4rtczuLFY/j5z98jKyuedesOUVBQwze/Od1v95U5c9K56aYP6OzUPmMEk5KMK9m5hqEDV1dyiyiGwrDD7s/bcsBe13RBCCMNDZ386EcbuOmmBSxalB103NxA4MiRRpeSLeGwGB492khKSgzHH58elCt5165KZszwdPNFRUWQm5vKnj0mJvLYsUb++tdtVFU1097u6cpsbGynra2DpCTfLeJ8xRg6XMkA06enUVbW5KJAtrZ2sG5dcb+6kXtLRkY8VVUtXt3Apph1YIqhHaqqwu9KjohQJCZ2dxlxUF3dwhNP7OWaa473u//MmaPIyIinsLCW7353Fu++e5FfCyMYWSYlRXPwYG0PruRWr67ktDRjddXaoVhKVrIwvLCrGN4LXKOUGiwt9IQhSGen5rHHKlm+fBxf+MIkxo1L5PDhwakYNjW109TU7pIVOmpUHDU1rV09XENBcXEdEyYkkZOTQlFRne34Mwe7d1d5VQzBNc7wjjs+4f/9vzzS0uJcWoo5cLiD/dUXNK5k3/uCcZfOn5/J5s2mbE1jYzv/+7+fkJeXalt5GohERkaQnh7nUbKmrq6V9nZt22oVSC3DiorwWwzBe/eTRx7ZxRlnjGfiRP81FCMiFI8/fha33baIJUvG2u7TPHduBlu3lvtUDJOSYqivb/PqSh41ylgMm5s7iIhQxMba7w0tCEMBu4peGjAL2KWUuk8pdZvbcmsY5ygIgHmYNDZ28pOfnACYmK7BqhgeOdJAVlaCi6LkUA68uVODxaEYxsdHkZ2dQFGR7xhAd9rbO8nPr2LatDSv2x1xhhs2lLBzZwVXXTWLzEzv7vCe4gvBuPfa2ztpbGzvWtfW1kltbauLAu0oW/PBB6WsWvUytbWt/OlPS2x/roGKN4uxI77QbsFud3f82rVFrFtX7HVsVVX4y9WAZ8mampoWnnkmn+9+d1bYzjlnTjpbtpT5UQwdrmRvFsM4qqpaJPFEGLbYjTH8pdPfU71s10jfYSGMFBfX8dBDu/jJT0Z2WQ0Gs8XQPb7QgWmt1sTYsZ7lSILBoRiC6SaRn19Dbq69On8HDtSSlZXQFUPozvTpaTz++G4++eQYN910EnFxUWRmxnvNrLajGCqluhRjhyWpoqKJtLQ4l/i0RYuy+cMfPmXjxhJuuWUhS5eOtfV5Bjq+FEO78YVgrK6ObiFr1uzn1ls3ceaZE7xmg1dUhN+VDJ4la957r5R58zJCdo17Y86cdJ57bj+TJiV7dQWb5JM2rzGGo0YZV3JtrSSeCMMTWxZDrXVED4vY2oWwobVm9erNXHnlDEaN6v4tM3r0CMrKmgJ2jw4EfD3wQ52A4q4Y7t9vP85w9+5Kr2VEHEydmkZRUR0zZ47itNNMORtHz2B3vD2AveEeZ2jiC12Vl9zcFG65ZSEvvXTukFEKAUaPTqC01FV2JSUNXn9A+MIRY/j00/v4y1+28atfneTzejIWw/Arhu7dT7yVPwo1xx2XxqFD9ZSWNvgscN2dfOIqg7Q040qWdnjCcEViBoUBz2uvFXP0aBNf//p0l/XR0RGkp8dz5Ij3jhEDGZN44vnAD3UCirNimJubGlACiq/EEwexsZFce+3x3HDDvK51vbEYgmfJGm/7KaW44IIpPi2ZgxXvFsNGr+3vfJGREcd775Xy0EM7efzxszjppGyv/4+Ojk7q6lpJTQ2/4pOSEtvlSu7s1GzYEH7FMCYmkunT09i5s9KrcpeQEEVrawelpY0+spJbpFSNMGyxrRgqw3lKqbuUUo8opSZa65cqpcL7LReGLXV1rdx558fccstJXmvEjRs3YlC6k0tLG1wykh2EUjHUWlNcXM+ECUaxyMtLCUgx7MliCHDVVbPJzOxWcH1ZDINVDMvL/Ze4GUpkZY3w+N+XlNQHZDGcODGJ6dPTeOyxsxg3LpGsrHiOHWtEa9d2iNXVrSQlxXiUkAkHzrUM9+6tIjEx2qP9XTiYMyedzk5NUpKncqeUyZaur2/ziLNMTY3p6gUuGcnCcMTWXUEplQa8D/wH+BbwdWCUtfnbwA1hmZ0w7Lnnnq0sWTKWE0/M9Lp97NjEQVmy5siRRq+uZF+KVTDU1naSkBBFYqJ5ME6cmMyRI400N7f3sKex7OzeXcX06d4TT3zhiJF0x64r2T15wq5CORQYPdqzlmHgFsMEHn/8rK4M7bi4KOLioqiqcs0KNsWtw+9GBtfuJ33hRnYwZ46pb+rL6pecHENaWqyHchwZGUFycgxFRXXiShaGJXZ/Lv4eGA+cCqQDzilybwCnh3hegsC+fVWsXVvEj398gs8xJgFlMLqSG7y6kv11wAiUsrL2LjcyGNf7xIlJFBb23AHl8OF6EhOjA1YejCvZm8WwmYyMno/lPcZweCiGpsi167UcaIyhN7y12zPFrfvGGuacfNKXiuHcuelddRS9kZQU4/PaGjkyjgMHasWVLAxL7CqG5wO/1Fp/gMlAdqYYozQKQkj56KNjnH76eFJTfT/Axo0bfBZDrbWVfOI7KzkUuCuG0J2Z3BM9xRf6IivLt8XQjuXPvUDzcLIYZmTEU1nZXeS6ra2TiopmW11M/OHNCt0XfZIdOJJP6utb2bmzkgULsvrkvJmZCfz7318kKsr7Yy4pKdrnj5WRI2M5eLBWLIbCsMSuYpgIfO5jWxyuFkRBCAkFBTV+22VBaGoZaq37VLmsqWklKiqiy8XrjK+YsGA4dqy9K77Qgd0EFDvxhd5ISYmhudkU73bQ3t5JTY09RcQzxnD4KIZRURGMGhXXpRgfPdpIenqcT8XGLllZ8R5W6L7KSAYTs1dT08qHHx5l7twMr32ew0Venu/STD1ZDIuLxZUsDE/s3nH2Amf52LYU2B6a6QhCNwUF1T0qhqGoZfjZZ2V873vre3WMQHBvhedMXFwU8fGeMWHB4N1i6JmA0tnpqYTu3h2cxVAp5WH1rKhoJjXVM5bLG8aV3N25YzhZDMG1Z7KjuHUojuluxTVdT/rWlbxxY0lXWaOBQE+KYVtbp7iShWGJXcXwL8CPlFK/BByVUlOVUpcD37O2C0JIKSys7VExzMiIp76+zcVCFSgHD9Z5beMWLkxGsm/3YKgyk327krsVw/z8alaseJ7XXivqWqe1ZteuyoATTxxkZsZTVtY9/0CsfiNHxlJT00J7eydaa6+dKYYyzjGmJr4wNIqh+/XUF32SHThcyX0ZX2iHvLwUjjvOu0UxLc0ozSkpkpUsDD/sFrh+ELgbuBXYb61eBzwA/Elr/c/wTE8YrlRVNdPS0kFmpn+lICJCMXp0Qq9cwcXFdVRXt4TEfWuHI0f8P/BDkZmstfaqGI4dm0hNTSt1da3s2VPFlVe+wbnnTuaOOz7uyhx1WJeCjW3LyHAt0h1IAklkZASpqbFUVDRTXd1CQkLUsOpVa5Q4k4ASKothZqb35JO+ykpOSYmhvLwZrSEnJ7lPzmmHb35zBl/84mSv20aNMrIRV7IwHLEd7KG1vkEp9TfgTCATqADWaa0LwzU5YfhSUGCshXZ6xDoSUOy2enOnuLiO9nZNfX2b15pnoaa01Htxawem+0nvElCqqlqIjFS34jE0AAAgAElEQVQeFo+ICEVubgr/+U8hDzywg5tuWsDZZ0+ktraVe+/dwk03ndQVX2i3P6/n/F1dl4G6gx1xhrGxkcPKWgieFsNg3PnejulNMRw1qm+sYXFxUcTERLB48eigr6m+xqE0i2IoDEfs1jFcopRK1FoXaa3/rrX+rdb6fq11oVIqUSk1+DvYCwOKwsIacnL8u5EdmASU4EvWFBeb3rKhiOuzQ0/9b0PhSi4uriMjw7ulLTc3lbvu+pRbb13I2WdPBOC6607g9deL2bat3HIjB6+QZGTE91oxLC9vGnbxheCqGIYzxrCysqXPLIZgXLIDKb6wJ8RiKAxn7MYYvg3M8LHtOGu7IIQMOxnJDnqTgGK6g9QxfnxinymGprh1eGMMjWLo3SFwySVTeeih01mxorvKVGpqLD/96Yn8+teb2LGjoleKoVFEnGMMA+te4ihybRTDvlNeBgLO//uefkDYJSUlhpaWDhobu+NwjcWw72T74x+fwKmnDh7FMC0tlvj4SGJihk8YgyA4sKsY+rP/xwIdIZiLIHQRiGLYm+4nVVUtREQoJk1Kprq6bxJQfLXDcxCKGMOiIt+K4cyZo5g/37OW3LnnTiYlJZb16z/vlQvTvV9yoEWqHa7k4VTc2kF29giOHGnsqnUZCouhyRTvLjze3t5JfX3f9gE+//wc4uP7rkxNbxk7NpEvfzmvv6chCP2Cz2+qUmoSkOO0ar5Syr03UzxwBabItSCEjMLCGnJzw28xLC6uY+LEJNLSYqmubg3qGIHQ0dHJsWNNfmMMQ9H9xJ/F0BdKKW655SRuvPF9xo+334bNHXfFNhhXcn5+NTExkX7lNBTJyIinoqKZ8vJmoqMjGTHCe9eOQHFYIs0PoJY+65M8WImPj+LGG+f39zQEoV/w9+T4BnALptOJBu7D1XKorfftwLXhmqAwtHj55QN88skxrrvuBJ/xO/X1rdTUtNh2o/Wm+0lxcR0TJhjFsKoq/BbDiopmUlJi/LqofHUPCYTi4jqmTQvcQjNpUjJPPXVOr87tiDHUWqOUCrhIdUZGPB98cISYmAhmzx7V8w5DiOjoCNLSYtm2rTwk1kIHzi7qvixVIwjC4MPfk+NRYD1G+XsLo/ztchvTAuzTWleGY3LC0OPllw/Q0NDOeee9xE03LeCMMyZ4jCkoqGXy5BQiIuxlMKakxNDZCTU1LQHXHSsqMophTExkn8QY+mqF54xzTFgwXSJaWzsoKqojM7N/lKoRI6KJjo6gtraV5OQYysubSE+3r4g4YgxjYiKGXfIJGIvxZ5+VhSS+0IGzYtiXxa0FQRh8+HzqaK2LgCIApdRy4BOt9eBqSisMKDo6OtmypZyXXz6XgwfruPnmD3j55YPcfvvJLi6zwkL78YVgXKDjxo3g88/rA1YMi4vrWLx4DM3N7ZSUhP/yNqVq/D/wnWPCJk0KvO7b7373MYsWZZOU1BnsNHuNo/uJ1hAbG0lcnH0F1xFjGBMTMexiDKFbMQxFqRoHWVkJFBU5su/7rh2eIAiDD7sFrt8RpVDoLfn5NYwcGUt6ejzz52fy739/iebmdtas2e8yrqDAfqkaB8GWrOl2Jcf1icWwp+LWDoLNTH7hhUI2bTrC6tWLgpleyHAotsGUnBnO5WrA/O937KgIqcXQOfmkslJcyYIg+Eaij4U+45NPjjFvXmbX+9jYSK64YgZr1hS4dB0JJCPZQbAJKK4xhn3hSvZf3NpBMIrh3r1V/O//fsI99ywlMbF/669lZpoi3YHGF4K5LuLjo+jo0CQlhSb5YjCRnT2CtrbOsMUYmq4n4koWBME7ohgKANxxx8e8+GJ4m9h8+ukxTjwx02XdggVZNDe3s317Rde6ggL7GckOgilZU13dQnu7ZuTIWFJTY6mu7huLYU+uZHAoVvYVw7q6Vn74w3e54Yb55OUF1wEmlDgyk4MtOZOeHk9GRvyg6ZQRShw/HMIVY9jXNQwFQRhciGIoAPDBB0fYtq08bMfXWntYDMHE0114YW6XO7mpqZ2ysibGjQusXEowFsNDh+qYMCERpZRVrqYvFMNGxozp2WKYnT0ioLZ4t9yyiVNPHc2553rv/drXOGoZBqsYZmTEBZSwMpRw9KgOpcUwPT2eysoW2ts7+7RPsiAIgw9RDAUaG9spKKghP786bOc4fLgerfFaH2/VqhzWri2msbGdAwdqmTAhiaiowC7NceMSu1rb2cXhRgbTsqumpoXOTt3DXq6Uljbw2mtFtseXlNi3GNotcv3OO4fZtauSn/98nu15hBtH95NgXMnQbTEcjmRnJxAdHRFSq150dASpqSZDvLKyRSyGgiD4RBRDgd27K8nOTiA/v8Yl1i+UOKyF3lyDWVkJnHBCBq+/XhRUfCHA5Mkme/fDD4/Y3sdZMYyOjiA+Poq6usCKXN9992fcfvtHthTK1tYOampabVnC7MYYNjW1s3r1R9x880nExg6c9l2ZmSazONB2eA4yMoavYjhmzAgeeuh02+Wa7JKdncDRo01UVUmMoSAIvvHX+eStAI6jtdanh2A+Qj+wY0cFS5aM5bXXioJ+kDvT0NDm0bHBmxvZmYsumsJjj+1h/vzMoBTDqKgIrr32eO69dwsLF55tKzatuLie+fO755SaahJQ7Ja8yc+v5sMPjxAfH8WePVU9lhcpLq5jzJgRtjpO2FUM//a37cydm8Epp4y2Nee+IiPDzD8+Piqo6+nssyfS3t5/5Xb6E6WU15aFvcUR92nqGIrFUBAE7/h7QkVgils7lmnAMmASphXeJOv9cfjvpSwMcHbsqGDWrFHk5aX02p3c0dHJ0qVr2LTJ1XLXk2K4dOk4Dh6sZf36wwGXqnHwhS9MpL6+jXffLbE13tEOz0FaWhw1NfbjDP/8561cccUMli8fx4YNPZ9z794qpk1Ls3Vs55gwX+TnV7Nmzf4B5UJ2kJERT2VlM0eONAYVKzhnTrrf60UInKysBD7/vJ6GhrY+7ZMsCMLgwqdiqLVeprVerrVeDtwDtAGLtNY5WuuTtdY5wMnW+nv6ZrpCONi+vYLZs0eRl5fK/v29UwxLShpobe3ktts209raAdAV15SX51vhi46O4LzzJrN3b3XAGckOIiMj+P7353DvvVtsucSdXclAQCVrdu6sYOvWcr72taksXjyGjRvtKIbVTJ1qL2PYEWPmy2rY2am59dZNfP/7cwakyzU6OoKUlFgOHaobkPMbjmRlJbBvXzXJydInWRAE39i9O/wG+JXWerPzSq31JuDXwOoQz0voI2pqWigvbyInJ5nc3NReWwwPHqxjwYJMJk9O5qGHdgLw6adlzJ2b3uPD6KKLcomOjnCx4gXKGWeMB+CNNw75HVdf30pjY5uL0uJwJdvhvvu28t3vziIuLooFCzLZvbuyx/jEvXurOO44exZDgGnT0ti1y3u3ybVri2hr6+Tii/NsH6+vycyMJzo6wmdPbKFvycpKYPfuSnEjC4LgF7uKYR5Q5mPbMSA3NNMR+pqdOyuZPn0kkZER5OWlkp9f06vjHTxYy6RJyfziFwv4xz/2UlRU16Mb2UFOTgrr1q0iJib4JAqlFD/4wVzuu28rHR2+3bDFxfWMH5/kEotoVzH87LMy9u+v4aKLzGUfFxfFiSdm9Jj4EogrGWDWrFEu9R2d2bChhAsumBLyBIVQkpmZMGxrEQ5EMjPjKSioEcVQEAS/2FUMDwDf9bHtu8DBkMxG6HMc8YUAubkp7N9fHXDJFmeKiuqYNCmZMWNG8O1vz+Q3v9lsWzEEo0z0liVLxpCYGM0rr/guI+PuRgZs1zK8996tXH31bBcFtid3clVVM42N7QHVpps1ayQ7dnhXDLdtK2fu3Azbx+oPMjPjh2Wv44FKdnYC7e1aMpIFQfCLXcXwVuBcpdQOpdSvlVJXW687gC9i3MnCIGTHDhNfCKaWX1JSDKWlgfccdlBUVNulcF122TQqKpooKKjpUj77AqUU1157PA8+uMNnrKEvxbCqqtnvsQ8erKWgoJrzz89xWb948Rg2bCjxeT5HfGEg1rNZs9LZtavS45jV1S0cPdrkN2ZzIOCwGAoDA8ePLqlhKAiCP2wphlrrp4GzgRrgRuAv1ms1cLbW+pmwzVAIKzt2VDBzZrfS1tvMZIcrGUwCwm23LeKrX83rlXs4GE45ZTRKKT74wLt717fF0H+c4FtvHWb58nEeBbgnT04mIkJRUODdFb9vX2DxhWAe4CNGRFNU5Fq4e9u2cmbNGjngEwjmzk1n4cLs/p6GYDFiRDSJidHS9UQQBL/YfrJord/QWp+KKVWTDcRrrRdrrd8M2+yGOa2tHXz722+ydauv8M7eUVbWRGNjOxMmdHcj6U0CSktLB2VlTYwd2+0unT07neuvn9/ruQaKUoqvf30ajz222+t2b4qhiTH0bzF8661DrFgx3uv5/LmT9+wJXDEEmD17lIc7eevWcubMGdhuZIBTTx3DpZce19/TEJzIykoQi6EgCH4J2OSgte7UWh/TWg/P6rN9yB13fMKmTUf59NPwKIY7d5r4Qmf3pklACU4xPHSojrFjEwNuZxcuvvSlyezaVelhxXO0APSmGPqLMaysbCY/v5pFi7xbwYxiWOp1W6AZyQ5mzfKlGKYHfCxBGD16xLDtQS0Igj1sP8GVUjlKqRuUUn9VSj3stjwUzkkOR158sZAPPijlhz+c49M92Vu2b6/wiP3rTWbywYN1vSo1E2piYyP5ylfy+Mc/9nSt09rU/1uyZKxHIkhaWpzfrOT16z/n5JNH+2w9t2hRFlu2lNHY2O6yvr29kwMHasnLs1fD0JmZM10TUDo7Ndu3i2IoBMftt5/MkiVj+3sagiAMYGwphkqp84E9mHqFq4DlXhYhROzbV8Wdd37CPfcsYc6cDAoLw6MYOmckO8jJSeHgwdqg2pEVFdUycWJyqKYXEr761am89lpRlyXwmWfy2beviptvPsljbEpKDLW1rT6zst988xArVozzea7ExBhmzhzJRx+5xjUeOFBLVlYCCQk+O1D6ZObMUezeXdX1/ygoqCE1NVZKjghBkZER3+fxvoIgDC7sWgxXA+uB0VrrMVrryW5LTg/7Czapq2vlhz98l+uvn8fUqWnk5qZQUFBjq5NHIGitLcXQtb9vQkIUmZnxFBfX+djTN6ZUzcCxGIJpLXf66eN55pl8tm0r589/3sqf/rSU+HhPJS0qKoIRI6KprfVMQGlqamfz5qM9WlvOOGMCL710wGVdMIknDpKTY8jKiu+yGg+GMjWCIAjC4MWuYpgD3KW1Dk+wmwCYfsJf/eprLF06lvPOM7p2amos0dERlJU1hfRcJSUNREYqsrI86wYGG2d48ODAsxgCfOMb03jyyb1cd90Gbr11kV93t684w/ffL2XWrFGkpvqvAXfBBTls3FhKSUl3yZ+9e6s57rjA3cgOnOMMt2wxXWQEQRAEIRzYVQz3AH1XiM4NpVSEUupGpdRBpVSzUmqrUuoim/s+qpTSXpY/hXvedmlu7mT16s385Ccb+NGP5nLDDa5ZvFOmpIQszvDo0Ub++c+9/PSnG5k/P8trXb3eKIYDzWIIMHVqGtOmpfGlL03i9NM9M4qd8dUv+a23Dvt1IztITIxh1aocnnxyb9e6YDOSHcyend6lGEriiSAIghBO7AY9/Rz4k1Jqk9a6MJwT8sFvgJ8CvwQ+Ab4K/Esp9SWt9Ss29i8DznNb5z19tI8pLq5j9eqjLFsWzwsvfImUFE+L1JQpqRQU1HDyyaODOsfhw/W8/noxb7xRTGFhLUuXjuXKK2eyeLH34+XlpfL668UBnaO+vpWGhraQdC4JB3/96zJbdf+8Fbnu6OjknXcOc801s22d67LLpnHxxa9y9dWzGTEiuleuZDAJKC++WEhdXSslJQ1MnRr8sQRBEATBH3YVw19jLIa7lVL5QKXbdq21XhrKiTlQSmVilMI7tNZ3WavfVkrlAncAdhTDVq31h+GYX28ZM2YEl18+kiuvPNnnmJyc5KAshv/6Vz5PP72Po0cbWbFiPFdffTwLF2b1GHyel5fKX/6yLaBzmbqAyQO2d6/dYtDe+iVv2VJORkYCY8cm+tjLlXHjElmwIJP//KeQlSsn0tQUWCs8d6ZPH0lBQQ2ffHKMGTNGDphyQIIgCMLQw65i2AHs7XFUeDgbiAGecFv/BPCwUmqy1vqA526Dg6ioCKZM8R+3NmVKSsAWvNdeK+KBB3awevXJzJuXGZAyMXFiEiUlDbS0dPgszeLOQCtVEyze+iW/+eYhTj+9ZzeyM1//+nR+8Yv3mTw5malT0wJqhedOfHwUEycm8eyz+ZJ4IgiCIIQVW4qh1npZmOfhj5lAC7Dfbf1O63UG0JNimKmUKgdSgULgIUwyTUcoJxoucnNTAipZU1hYw29+s5kHHljh0u7OLjExkcyePYrXXy/m3HMn29rHuRXeYMZb8sm7737OnXeeGtBxTjwxg9TUWB54YEevEk8czJo1iuefL+DCC3N7fSxBEARB8EXghdX6npFAtfas11LptN0fWzBxiTuBOOAC4HdAHvAtbzsopb4DfAcgKyuL9evXBzVxu9TX1/s9h9aapqZWXnrpTZKS/Fvwmps7+f3vj7FyZRJlZdsJduqnnNLJXXdtIiHhIJGRPVu7Nm+uZNq0WNavD77Psh16klVvOXasgcLCFtavrwWgoqKdsrJ6jh7dRllZYFa/+fM1jzxylNzcll7POSamHq2hsXEf69cX2Non3LIaKoic7COysofIyT4iK/v0may01rYXIA04CVjivgRwjDMAbWNZb41/ECj1cpw8a9z/BPIZrH3/aO2b19PYefPm6XDz9ttv9zjma197VX/00RG/Yzo7O/VPf7pB/+IX7+vOzs5ezamzs1N/4xuv6+eey7c1/uKLX9GffXasV+e0gx1Z9YZ164r1Ndd0n+Ppp/fq66/fGNSxWls79Dnn/Efv3l3Z63nt3VupV658IaB9wi2roYLIyT4iK3uInOwjsrKPXVkBH+sA9SLnxZbFUCkVBzwMXAz4MpvYLaf/PjDdxrhG67USSFNKKesDO0hz2h4oTwE/AuYD+UHs3+dMmZLC/v01zJ+f5XPM888XsH9/DU89dXavYtoAlFL84Adz+PnP3+Pccye7JKy88UYxlZUtXHxxHmB+XBQVDc0Yw40bSzn77AlBHSs6OoKXXjo3JMkiU6em8e9/f7HXxxEEQRAEf9h1Jf8KWAZ8A/gHcC3QDHwTGA380O4JtdaNmLqIdtkJxAJTcI0znGG97grgWA4cWlNo24mEkSlT/McZaq159NHd3HzzScTFhSZC4MQTM5kyJYU1a/bzta8dB5gezn/4w2eAyb495ZTRXVm8PRV/Hgw4l6tpbe1g8+Yj3HrrwqCPF8oMYmllJgiCIIQbu0+ti4DbgKet95u01o9oU6JmK3BOOCZn8RrQClzqtv4yYIcOLiP5EoxS+FEv59Zn5OT4L3K9bVs5bW2dzJ+fGdLz/uAHc7j//h00NbXzr3/l88c/buHhh8/g978/leuvf4+SkgarFV5yr62UAwHncjVbt5YzaVKy9CUWBEEQhg12TUsTgJ1a6w6lVBvgXJTtYeARArAaBoLW+phS6o/AjUqpOuBT4CvACuB857FKqTeBiVrrXOv9RIyF82mMtTEWk3zyTeB+rbW9KP4BQE/dT9asKeCii6aEXDmbOXMUxx+fzjXXrKe4uI5HHz2TiROTmDIlhW9+czrXXfcuF144ZUB2PAmG5OQY6uvb6OjoZMOGEhYvHtPfUxIEQRCEPsOuxbACcFT3PQTMcdqWDsSHclJe+CWwGqN8rgVOBS7WWr/kNi4SV2W3DhODeD3wIvAsMBf4AcYdPmgYPXoEdXVt1NW1emxraGjj9deLWbUqJyzn/v7359Dc3M7jj5/pEkd4xRUzyMxM4I9/3DIk4gvBuH4TE6OprW1l40ZRDAVBEIThhV2L4YfACcCrwBrgN0qpJKAd+AmwMTzTM2hTb3C1tfgbt8ztfSWwKnwz6zsiIhRTpiRTWFjDnDmuRY7Xri1m3rwMMjLC044uLy+Vp57yjBZQSvHb357MxRe/OqTatKWlxbJvXzUlJQ3Mnt1vLcIFQRAEoc+xqxjeiXEng1HOcjExh5EYpfGa0E9NcCcnx2QmuyuGzz+/n8svn+Fjr/CSlBTDCy98iejoodOmLS0tjv/+9yAnn5wt7ecEQRCEYYWtp57W+mOt9fPW33Va64swruVUrfUpWuuicE5SMHiLMywsrKG4uI4lS8b206xMtuxQSDxxkJoaw9q1ReJGFgRBEIYdQZtDtNYtWutapdQZSqltoZyU4B1vJWuef76A88/PGVIWu/4mNTWOuro2UQwFQRCEYUcoCt6lYPoZC2FmypRU9u+vob7eJKB0dGheeKGQxx8/s59nNrRIS4tl6tRUsrLCE7MpCIIgCAOVwdArWbAYN24ESsHy5f/uWnfKKdlMnpzSj7MaeuTmppCcHNPf0xAEQRCEPkcUw0FEZGQE69Zd0N/TGPKsWjWlv6cgCIIgCP2CBKYJgiAIgiAIgB+LoVLKbrXk7BDNRRAEQRAEQehH/LmS92P6CfeEsjlOEARBEARBGMD4Uwwv77NZCIIgCIIgCP2OT8VQa/1YX05EEARBEARB6F8k+UQQBEEQBEEARDEUBEEQBEEQLEQxFARBEARBEABRDAVBEARBEAQLUQwFQRAEQRAEQBRDQRAEQRAEwUJpLbWp/aGUKgOKwnyadKA8zOcYKois7COysofIyT4iK3uInOwjsrKPXVlN1FpnBHsSUQwHAEqpj7XW8/t7HoMBkZV9RFb2EDnZR2RlD5GTfURW9ukrWYkrWRAEQRAEQQBEMRQEQRAEQRAsRDEcGDzQ3xMYRIis7COysofIyT4iK3uInOwjsrJPn8hKYgwFQRAEQRAEQCyGgiAIgiAIgoUohr1AKTVOKXWfUuoDpVSjUkorpSZ5GTdBKfWYUqrYGrdPKbVaKTXCbVyCUupWa3uTUuqQUupxH8dcrJR63xp3RCl1t1IqPlyftTcEIKfJSqnnlFLVSqkGpdTbSimPDCylVIRS6kal1EGlVLNSaqtS6iIf5/62UmqPUqpFKbVXKXVV6D9h6AilrJRSU5VS9yiltiml6pVSpUqpF5VSc3yce9jKyss+X7OOd9jH9kEjq3DISSk1Vin1sHXvaVFKHVBK/c7LuEEjJwjLvWqU9R0stO7VB5RSf1ZKeZQSUUqtUkp9Zt3TipRSNymlIsPzSXuHUurLSqk11jybrP/t75RSSW7j0pRSf1dKlVtyekMpNdvL8eKUUr+37lFNlvyXeBln+94/EAilnJRS85VSD1jfp0Zl9Il/KqUmezlv7+SktZYlyAVYBhwFXgHWAhqY5DZmBLAPOAB8A1gO/BxoAp5xG/sk0Aj8zBr3DeAgUAAkOo073tr/P8DpwLeAKvfjDZTFppxGAZ8De4CvAOcCbwN1wHS3sbcDLcBPLTndD3QCK93Gfdtaf7s1brX1/ur+lklfyAr4HrAN+In1+S8APgCagXkiK9frymmfVOAIUAoc9rJ9UMkqDN+/SdbYjcDFwFLMveo3g1lOoZYVoID3gDLgauvY1wAV1vdQOY09G+jAxJAtB35sfU/v7G+Z+JDTh8CzwKXW//9HQLW1PsLp828ADgNfA84B3sHU4Rvndrx/Wvt/G/NMex7zjJvrNs7WvX+gLKGUE3CXdT1dYx3rEmC3dT2ND6Wc+l1wg3lx/GOtv7/l4yZylrX+LLf1dwDtQIL1Pt56/1u3cedY+5/ttO7fQD4Q7bTu69a4E/tbLkHK6Sbr8+c6rRuBuUk/67Qu07rgb3Xb/01gm9P7KOAY8JjbuIetL1x0bz7TIJFVOk4PH2tdCuZHxOMiq25Zue3zAEYpeBQ3xXAwyirUcgJeAzb7+6yDUU6hlhUw1dr/O277X2WtP85p3WfAO27jbgZagez+losXOWV4Wed4Bq2w3p9vvV/uNCYFqATudVo3xxp3udv1sxd40WmdrXv/QFpCLCdvx5qIUfhuC6WcxJXcC7TWnTaGxVivtW7rqzGufGW9jwIifYzDGotSKhqjLD6rtW5zGvcs5iZyvq3J9yE25bQIyNda73farwHzS+pLSqkoa/XZGJk+4bb/E8BsJ7P6yUCGl3H/wPziXxzQh+gjQikrrXW5tu4ITuNqMBbssU6rh72sHCilTgUuA671cbxBJ6tQykkpNQXzHbzP7f7jzqCTE4T8mvJ374fue/p4YC7eZRUNfMH2B+gjtNZlXlZ/ZL067i3nASVa67ed9qsBXsL1OXUe0AY84zSuHXgaOFspFWuttnvvHzCEUk7ejqW1LsJYpJ3v572WkyiG4ecNjHXvTqXUDKVUolJqBfBD4P+sGwpa6zrMjeAHSqnl1riZwO+BrRhtH2AKEAfscD6J1roZ43Ke0RcfKgx0YBRbd1ow1tQp1vuZ1rr9buN2Wq8znMaBm5y8jBuM2JWVB0qpkcAsjAvCgciKrh9dDwC/d37ouzFUZWVXTqdar01KqXVW7GCVMrHQo5z2G6pyAvuy2gm8C/zKig9LVEqdhLEEvqq1dnwHvcpKa30AE1o0WGS11Hp1/lzu/38wcpmglEp0GndAa93oZVwMkOs0zs69f6ATrJw8UEpNx1gI3e/nvZKTKIZhxlLYFmNkvRMTh/Im8DImBsyZyzFu4rescTswvxjP1Fo7bkQjrdcqL6erdNo+2NgL5Dk/XJRSEcBJ1tuRTq/V7pYwzGd3HweecnIfNxixKytv3IexUv/JaZ3IynA9EAt4JFE4MVRlZVdOY6zXhzGW5y9g5PZFYK21j/P4oSYnsCkr6x610hr/EeaevgkoBJwTAfzd06sYBLJSSo0FbgPe0Fp/bK0eie/nFECazXGB3vsHLL2Uk/uxooD/w1gMH3La1M/7buEAAAh9SURBVGs5iWIYZpRScRgTeSbwP5hfCz/DBC3/xW34aowb66fWuP/BuF1eVd0ZzA7Xs/s/3XnbYOT/MNfj40qpKUqp0cC9gMPs7XDxKOx9dn9yGuzYlZULSqkbMQHL33OziA17WSmlcoFfYmTT7Od4Q1VWdq8pxzNjvdb6Wq31W1rrBzAB8fMwbiwYunKCwL5/D2Jcz1dh7ulXAfOB55yU6EF9T7csWi9g4i4vd96E/Xt1KMcNSEIgJ3f+DJwCXKa1dlYsey0nUQzDz5WYbLSVWusntNbvaq3vwmSKXqWs0iGW2/gG4Mda6z9Y457A/OKchwmEBv9af5rT9kGF1roQk7k1D2MCL8HEKf3RGlJqvVYCaUop94s8zWm786u7nEa6bR90BCCrLpQpE/Jb4Cat9cNum0VW5sH+FvChUipVKZWKcWMp672jFNSQlFUAcqqwXte5HeJ16/UE63VIygnsy0op9UVMlun/aK3vt+7p92N+8K/EZDOD/3t6KgNYVpbh40UgB5Mg6VzeyZcHy3GvrrI5zvmebufeP+AIkZycj/c74DvAFVrr190291pOohiGn9lAlda6wG39Zut1utM46A5MBUBrnY8JVnaMK8DED8x0HmddeDnArtBMu+/RWq/BBNHOwGT8zQMSgUNa62Jr2E6Mu889js4RN7HLaRy4ycnLuEGJTVkBoJT6H+CvwB+01rd7OZzIymxbibkJO5avYVynVXS7l4esrAL4/oFvS2Cn27ghJyewLSuv93Q87/1eZaVM/cQEBqisrJjcNRgX+kqt9Xa3ITvx/P+DkVmx1rreadxkpVSCl3GtdMfK2b33DyhCKCfH8X6JMSL9UGv9Dy/79VpOohiGnyMY7T3Xbf1C6/Vzp3HQHacCmCLFmF+NnwNYsYavARe7ZVR+GXMxvBi6qfc9WusOrfVurXWBUmoMxuX+N6chr2FuFpe67XoZsMMK2AZTJ6zcx7hKTD2oQY0NWaGUugB4BPi71vqnPg4lsoKvYup9OS9rMXJZjnHbwBCXlQ05fYi5V53jtqvjvUMJGtJyAluy8npPx+3ebymSW/Euqzbg1ZBOPARYbvB/YmoOnq+1/tDLsBeBsUqppU77JWMspS+6jYsG/p/TuCiMPF/XWrdYq+3e+wcMIZYTSqkfYELOfqm1vs/HaXsvJ281bGQJqE7Rl63lb5hf0Vdb75da2ydhyhXso7vA9c+sdR/TXeQyEtgC1OBa4NphMZzgdM65mOKfz2MuuCsxN9t/9bc8eiGnaIwrZhWwAvg+xkWzAYhxO9YdmOKvP8a46f+GsVSc6zbuKmv9amvcbdb7a/tbHn0hK2CJJadPMbEoi5yWE0RWrteVl2M/ivcC14NOViH+/n3DOsb/Yeq0XoOxqr6Na9HmQSenUMoKSMYofyXWMZZbr0eAYlybFqy0ZHO/JavrrO/u7/tbHj5k5JDNarf7yiKsoswYw9P7wCHMD6+zgfWYZ5V7QeanrWvoW5hn2nPW5z/RbZyte/9AWUIpJ2tbJ+aHgvuxZoRSTv0uuMG+WP90b8t6pzEzMHUGD2EUun2YKuZpbscaBfwBoww2WeOfwakQqtPYJXR3sTiKyTJN6G95BCsnTB3Hl63P0oJxma/29pkwSvRNQJE1dhvwZR/n/a4l7xZLrtf0tyz6SlbAr/0c66DIqsdjP4oXxXAwyirUcsLEye2wxpZist0TvYwbVHIKtayA8ZiM0QOYe/UBTELKWC9jL8RYDlswiuPNQGR/y8OHjA76kdOvncaNxGSwV2JK77wJzPFyvHjgbozS3IzJ3l7mZZzte/9AWEIpJ+t+1KO+EQo5KesggiAIgiAIwjBHYgwFQRAEQRAEQBRDQRAEQRAEwUIUQ0EQBEEQBAEQxVAQBEEQBEGwEMVQEARBEARBAEQxFARBEARBECxEMRQEQQgApdRzSqlKpVSWl23LlFKdSqkf9sfcBEEQeovUMRQEQQgASyHcCbyttXZu4xWPKSR7DDhNa93p4xCCIAgDFrEYCoIgBIDW+ijwI+DLSqlVTpt+DYwDrugrpVApFenWM10QBKFXiGIoCIIQIFrrJzBt0f6qlEpVSp2I6Uv6a631XuexSqmvKKU2K6UalVJVSqmnlVJj3cZ8XSn1jlKqTClVp5T6RCl1iduYOKWUVkrdrJT6lVKqCGgF8sL7aQVBGE6IK1kQBCEILOVuJ/BvYC7QDizSWnc4jfkRpgfsg8B/gFTgNkx/07la60Zr3C0YF/R+a9flwPXAlVrrR60xcZge6iXAXkx/4mZgs9a6IpyfVRCE4YMohoIgCEGilPoWRulrA+Zprbc7bUsFPgce01pf47R+KrAL+J7W+v+8HDMC4815BJiqtV5orXcohsVAnta6NWwfTBCEYYu4kgVBEIJEa/13oBT4j7NSaHEakAD8UykV5ViAQmtZ4hiolJqulHpWKVWCsTy2AZcBx3k57X9FKRQEIVxI0LIgCELvaLUWdzKt140+9jsAXZbFdUAl8DNrfStWgouX/Up7M1lBEAR/iGIoCIIQHhxxf5cA+V6211qvpwFjgVVa648dG5VS0T6OK/E/giCEDVEMBUEQwsO7mJjAHK31U37GJVivbY4VSqlMYGUY5yYIguAVUQwFQRDCgNa6Uil1A/AHpdQYYC1Qh7EOLgde1Vo/B2wAGoD7lVK3AcnAzcBRTF1EQRCEPkMUQ0EQhDChtb7Xqjf4Y+DrQCQmU/kdYLs1pkQpdRHwv8Aa4DCmxM1ETJyhIAhCnyHlagRBEARBEARAytUIgiAIgiAIFqIYCoIgCIIgCIAohoIgCIIgCIKFKIaCIAiCIAgCIIqhIAiCIAiCYCGKoSAIgiAIggCIYigIgiAIgiBYiGIoCIIgCIIgAKIYCoIgCIIgCBb/H8yY8dfO4izpAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#You can set the size of the figure by doing:\n", + "plt.figure(figsize=(10,5))\n", + "\n", + "#Plotting\n", + "plt.plot(temp_data['Year'], temp_data['Value'], color='#2929a3', linestyle='-', linewidth=1) \n", + "plt.title('Land global temperature anomalies. \\n')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Land temperature anomaly [°C]')\n", + "plt.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3: Least-squares linear regression \n", + "\n", + "In order to have an idea of the general behavior of our data, we can find a smooth curve that (approximately) fits the points. We generally look for a curve that's simple (e.g., a polynomial), and does not reproduce the noise that's always present in experimental data. \n", + "\n", + "Let $f(x)$ be the function that we'll fit to the $n+1$ data points: $(x_i, y_i)$, $i = 0, 1, ... ,n$:\n", + "\n", + "$$ \n", + " f(x) = f(x; a_0, a_1, ... , a_m) \n", + "$$\n", + "\n", + "The notation above means that $f$ is a function of $x$, with $m+1$ variable parameters $a_0, a_1, ... , a_m$, where $m < n$. We need to choose the form of $f(x)$ _a priori_, by inspecting the experimental data and knowing something about the phenomenon we've measured. Thus, curve fitting consists of two steps: \n", + "\n", + "1. Choosing the form of $f(x)$.\n", + "2. Computing the parameters that will give us the \"best fit\" to the data. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is the \"best\" fit?\n", + "\n", + "When the noise in the data is limited to the $y$-coordinate, it's common to use a **least-squares fit** [2], which minimizes the function\n", + "\n", + "$$\n", + "\\begin{equation} \n", + " S(a_0, a_1, ... , a_m) = \\sum_{i=0}^{n} [y_i - f(x_i)]^2\n", + "\\end{equation}~~~~~~(1) \n", + "$$\n", + "\n", + "with respect to each $a_j$. We find the values of the parameters for the best fit by solving the following equations:\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\frac{\\partial{S}}{\\partial{a_k}} = 0, \\quad k = 0, 1, ... , m.\n", + "\\end{equation}~~~~~~(2)\n", + "$$\n", + "\n", + "Here, the terms $r_i = y_i - f(x_i)$ are called residuals: they tell us the discrepancy between the data and the fitting function at $x_i$. \n", + "\n", + "Take a look at the function $S$: what we want to minimize is the sum of the squares of the residuals. The equations (2) are generally nonlinear in $a_j$ and might be difficult to solve. Therefore, the fitting function is commonly chosen as a linear combination of specified functions $f_j(x)$, \n", + "\n", + "$$\n", + "\\begin{equation*}\n", + " f(x) = a_0f_0(x) + a_1f_1(x) + ... + a_mf_m(x)\n", + "\\end{equation*}~~~~~~(3)\n", + "$$\n", + "\n", + "which results in equations (2) being linear. In the case that the fitting function is polynomial, we have have $f_0(x) = 1, \\; f_1(x) = x, \\; f_2(x) = x^2$, and so on. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear regression \n", + "\n", + "When we talk about linear regression we mean \"fitting a function to the data.\" In this case,\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " f(x) = a_0 + a_1x\n", + "\\end{equation}~~~~~~(4)\n", + "$$\n", + "\n", + "The function that we'll minimize is:\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " S(a_0, a_1) = \\sum_{i=0}^{n} [y_i - f(x_i)]^2 = \\sum_{i=0}^{n} (y_i - a_0 - a_1x_i)^2 \n", + "\\end{equation}~~~~~~(5) \n", + "$$\n", + "\n", + "Equations (2) become:\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\frac{\\partial{S}}{\\partial{a_0}} = \\sum_{i=0}^{n} -2(y_i - a_0 - a_1x_i) = 2 \\left[ a_0(n+1) + a_1\\sum_{i=0}^{n} x_i - \\sum_{i=0}^{n} y_i \\right] = 0\n", + "\\end{equation}~~~~~~(6) \n", + "$$\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\frac{\\partial{S}}{\\partial{a_1}} = \\sum_{i=0}^{n} -2(y_i - a_0 - a_1x_i)x_i = 2 \\left[ a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i \\right] = 0\n", + "\\end{equation}~~~~~~(7) \n", + "$$\n", + "\n", + "Let's divide both equations by $2(n+1)$ and rearrange terms.\n", + "\n", + "Rearranging (6) and (7):\n", + "\n", + "$$\n", + "\\begin{align}\n", + " 2 \\left[ a_0(n+1) + a_1\\sum_{i=0}^{n} x_i - \\sum_{i=0}^{n} y_i \\right] &= 0 \\nonumber \\\\ \n", + " \\frac{a_0(n+1)}{n+1} + a_1 \\frac{\\sum_{i=0}^{n} x_i}{n+1} - \\frac{\\sum_{i=0}^{n} y_i}{n+1} &= 0 \\\\\n", + "\\end{align}~~~~~~(8)\n", + "$$\n", + "\n", + "$$\n", + "\\begin{align}\n", + " a_0 = \\bar{y} - a_1\\bar{x}\n", + "\\end{align}~~~~~~(9)\n", + "$$\n", + "\n", + "where $\\bar{x} = \\frac{\\sum_{i=0}^{n} x_i}{n+1}$ and $\\bar{y} = \\frac{\\sum_{i=0}^{n} y_i}{n+1}$.\n", + "\n", + "Rearranging (7):\n", + "\n", + "$$\n", + "\\begin{align}\n", + " 2 \\left[ a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i \\right] &= 0 \\\\\n", + " a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &=0 \\\\\n", + "\\end{align}~~~~~~(10)\n", + "$$\n", + "\n", + "Now, if we replace $a_0$ from equation (8) into (9) and rearrange terms:\n", + "\n", + "$$\n", + "\\begin{align*}\n", + " (\\bar{y} - a_1\\bar{x})\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &= 0 \\\\ \n", + "\\end{align*}~~~~~~(11)\n", + "$$\n", + "\n", + "Replacing the definitions of the mean values into the equation, \n", + "\n", + "$$\n", + "\\begin{align*}\n", + " \\left[\\frac{1}{n+1}\\sum_{i=0}^{n} y_i - \\frac{a_1}{n+1}\\sum_{i=0}^{n} x_i \\right]\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &= 0 \\\\ \n", + " \\frac{1}{n+1}\\sum_{i=0}^{n} y_i \\sum_{i=0}^{n} x_i - \\frac{a_1}{n+1}\\sum_{i=0}^{n} x_i \\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &= 0 \\\\ \n", + "\\end{align*}~~~~~~(12)\n", + "$$\n", + "\n", + "Leaving everything in terms of $\\bar{x}$, \n", + "\n", + "$$\n", + "\\begin{align*}\n", + " \\sum_{i=0}^{n} y_i \\bar{x} - a_1\\sum_{i=0}^{n} x_i \\bar{x} + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i = 0 \n", + "\\end{align*}~~~~~~(13)\n", + "$$\n", + "\n", + "Grouping the terms that have $a_1$ on the left-hand side and the rest on the right-hand side:\n", + "\n", + "$$\n", + "\\begin{align*}\n", + " a_1\\left[ \\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_i \\bar{x}\\right] &= \\sum_{i=0}^{n} x_iy_i - \\sum_{i=0}^{n} y_i \\bar{x} \\\\\n", + " a_1 \\sum_{i=0}^{n} (x_{i}^2 - x_i \\bar{x}) &= \\sum_{i=0}^{n} (x_iy_i - y_i \\bar{x}) \\\\\n", + " a_1 \\sum_{i=0}^{n} x_{i}(x_{i} -\\bar{x}) &= \\sum_{i=0}^{n} y_i(x_i - \\bar{x}) \n", + "\\end{align*}~~~~~~(14)\n", + "$$\n", + "\n", + "Finally, we get that:\n", + "\n", + "$$\n", + "\\begin{align}\n", + " a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})}\n", + "\\end{align}~~~~~~(15)\n", + "$$\n", + "\n", + "Then our coefficients are:\n", + "\n", + "$$\n", + "\\begin{align}\n", + " a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})} \\quad , \\quad a_0 = \\bar{y} - a_1\\bar{x}\n", + "\\end{align}~~~~~~(16)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's fit!\n", + "\n", + "Let's now fit a straight line through the temperature-anomaly data, to see the trend over time. We'll use least-squares linear regression to find the slope and intercept of a line \n", + "\n", + "$y = a_1x+a_0$\n", + "\n", + "that fits our data.\n", + "\n", + "In our case, the `x`-data corresponds to `Year`, and the `y`-data is `Value`. To calculate our coefficients with the formula above, we need the mean values of our data. Since we'll need to compute the mean for both `x` and `y`. \n", + "\n", + "It is good coding practice to *avoid repeating* ourselves: we want to write code that is reusable, not only because it leads to less typing but also because it reduces errors. If you find yourself doing the same calculation multiple times, it's better to encapsulate it into a *function*. \n", + "\n", + "Remember the _key concept_ from [02_Working_with_Python](../../CompMech01-Getting-started/notebooks/02_Working_with_Python.ipynb): A function is a compact collection of code that executes some action on its arguments. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise \n", + "\n", + "Calculate the mean of the `year` and `temp_anomaly` arrays using the NumPy built-in function, `np.mean`.\n", + "\n", + "Assign the means to `mean_x` and `mean_y`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have mean values, we can compute our coefficients by following equations (12). We first calculate $a_1$ and then use that value to calculate $a_0$.\n", + "\n", + "Our coefficients are:\n", + "\n", + "$$\n", + " a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})} \\quad , \\quad a_0 = \\bar{y} - a_1\\bar{x}\n", + "$$ \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We already calculated the mean values of the data arrays, but the formula requires two sums over new derived arrays. Guess what, NumPy has a built-in function for that: [`numpy.sum()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html). Study the code below." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "xi = temp_data['Year'].values\n", + "yi = temp_data['Value'].values\n", + "\n", + "x_mean = np.mean(xi)\n", + "y_mean = np.mean(yi)\n", + "\n", + "a_1 = np.sum(yi*(xi - x_mean)) / np.sum(xi*(xi - x_mean)) " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.01037028394347266\n" + ] + } + ], + "source": [ + "print(a_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "a_0 = y_mean - a_1*x_mean" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-20.148685384658464\n" + ] + } + ], + "source": [ + "print(a_0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Exercise\n", + "\n", + "Write a function that computes the coefficients, call the function to compute them and compare the result with the values we obtained before. As a hint, we give you the structure that you should follow:\n", + "\n", + "```python\n", + "def coefficients(x, y, x_mean, y_mean):\n", + " \"\"\"\n", + " Write docstrings here\n", + " \"\"\"\n", + "\n", + " a_1 = \n", + " a_0 = \n", + " \n", + " return a_1, a_0\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def coefficients(x, y, x_mean, y_mean):\n", + " \"\"\"\n", + " Write docstrings here\n", + " Arguments\n", + " ---------\n", + " ??\n", + " Returns\n", + " -------\n", + " ??\n", + " \"\"\"\n", + " # your code here\n", + " \n", + " return a_1, a_0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have the coefficients of a linear function that best fits our data. With them, we can compute the predicted values of temperature anomaly, according to our fit. Check again the equations above: the values we are going to compute are $f(x_i)$. \n", + "\n", + "Let's call `reg` the array obtined from evaluating $f(x_i)$ for all years." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "reg = a_0 + a_1 * xi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the values of our linear regression, we can plot it on top of the original data to see how they look together. Study the code below. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "\n", + "plt.plot(xi, yi,'s', color='#2929a3', linewidth=1, alpha=0.5,label='Measured anomoly') \n", + "plt.plot(xi, reg, 'k--', linewidth=2, label='Linear regression')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Land temperature anomaly [°C]')\n", + "plt.legend(loc='best', fontsize=15)\n", + "plt.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Apply regression using NumPy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above, we coded linear regression from scratch. But, guess what: we didn't have to because NumPy has built-in functions that do what we need!\n", + "\n", + "Yes! Python and NumPy are here to help! With [`polyfit()`](https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.polyfit.html), we get the slope and $y$-intercept of the line that best fits the data. With [`poly1d()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.poly1d.html), we can build the linear function from its slope and $y$-intercept.\n", + "\n", + "Check it out:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# First fit with NumPy, then name the coefficients obtained a_1n, a_0n:\n", + "a_1n, a_0n = np.polyfit(xi, yi, 1)\n", + "\n", + "f_linear = np.poly1d((a_1n, a_0n)) " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.010370283943472659\n" + ] + } + ], + "source": [ + "print(a_1n)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-20.148685384658457\n" + ] + } + ], + "source": [ + "print(a_0n)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "0.01037 x - 20.15\n" + ] + } + ], + "source": [ + "print(f_linear)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The assignment `f_linear = np.poly1d((a_1n,a_0n))` creates a 1D polynomial. This means that the function only has one independent variable i.e. f(x) = (some value). You can create your own polynomial functions in a similar way using _anonymous functions_ i.e. `lambda`.\n", + "\n", + "```python\n", + "f_linear = lambda x: a_1n*x + a_0n\n", + "```\n", + "\n", + "In the line of code given above, we create the same assignment for `f_linear(x)`. One benefit of writing this out yourself is that you can see how each input is used directly. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "\n", + "Use the `lambda` function to assign `f_linear` to our 1D polynomial instead of the `np.poly1d` assignment. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "\n", + "plt.plot(xi, yi,'s', color='#2929a3', linewidth=1, alpha=0.5,label='Measured anomoly')\n", + "plt.plot(xi, f_linear(xi), 'k--', linewidth=2, label='Linear regression')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Land temperature anomaly [°C]')\n", + "plt.legend(loc='best', fontsize=15)\n", + "plt.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## \"Split regression\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you look at the plot above, you might notice that around 1970 the temperature starts increasing faster that the previous trend. So maybe one single straight line does not give us a good-enough fit.\n", + "\n", + "What if we break the data in two (before and after 1970) and do a linear regression in each segment? \n", + "\n", + "To do that, we first need to find the position in our `year` array where the year 1970 is located. Thankfully, NumPy has a function called [`numpy.where()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) that can help us. We pass a condition and `numpy.where()` tells us where in the array the condition is `True`. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'numpy' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1970\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'numpy' is not defined" + ] + } + ], + "source": [ + "numpy.where(year==1970)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To split the data, we use the powerful instrument of _slicing_ with the colon notation. Remember that a colon between two indices indicates a range of values from a `start` to an `end`. The rule is that `[start:end]` includes the element at index `start` but excludes the one at index `end`. For example, to grab the first 3 years in our `year` array, we do:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "year[0:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we know how to split our data in two sets, to get two regression lines. We need two slices of the arrays `year` and `temp_anomaly`, which we'll save in new variable names below. After that, we complete two linear fits using the helpful NumPy functions we learned above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "year_1 , temp_anomaly_1 = year[0:90], temp_anomaly[0:90]\n", + "year_2 , temp_anomaly_2 = year[90:], temp_anomaly[90:]\n", + "\n", + "m1, b1 = numpy.polyfit(year_1, temp_anomaly_1, 1)\n", + "m2, b2 = numpy.polyfit(year_2, temp_anomaly_2, 1)\n", + "\n", + "f_linear_1 = numpy.poly1d((m1, b1))\n", + "f_linear_2 = numpy.poly1d((m2, b2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "\n", + "plt.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1, alpha=0.5) \n", + "plt.plot(year_1, f_linear_1(year_1), 'g--', linewidth=2, label='1880-1969')\n", + "plt.plot(year_2, f_linear_2(year_2), 'r--', linewidth=2, label='1970-2016')\n", + "\n", + "plt.xlabel('Year')\n", + "plt.ylabel('Land temperature anomaly [°C]')\n", + "plt.legend(loc='best', fontsize=15)\n", + "plt.grid();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Discussion exercise\n", + "We have two different curves for two different parts of our data set. A little problem with this and is that the end point of our first regression doesn't match the starting point of the second regression. We did this for the purpose of learning, but it is not rigorously correct. \n", + "\n", + "How would you fix this issue? \n", + "\n", + "What would your new function, $f(x)$, look like?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## We learned:\n", + "\n", + "* Making our plots more beautiful\n", + "* Defining and calling custom Python functions\n", + "* Applying linear regression to data\n", + "* NumPy built-ins for linear regression\n", + "* The Earth is warming up!!!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "1. [_Essential skills for reproducible research computing_](https://barbagroup.github.io/essential_skills_RRC/) (2017). Lorena A. Barba, Natalia C. Clementi, Gilbert Forsyth. \n", + "2. _Numerical Methods in Engineering with Python 3_ (2013). Jaan Kiusalaas. Cambridge University Press.\n", + "3. _Effective Computation in Physics: Field Guide to Research with Python_ (2015). Anthony Scopatz & Kathryn D. Huff. O'Reilly Media, Inc.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Problems\n", + "\n", + "1. There is a csv file in '../data/primary-energy-consumption-by-region.csv' that has the energy consumption of different regions of the world from 1965 until 2018 [Our world in Data](https://ourworldindata.org/energy). \n", + "We are going to compare the energy consumption of the United States to all of Europe. Load the data into a pandas dataframe. *Note: we can get certain rows of the data frame by specifying what we're looking for e.g. \n", + "`EUR = dataframe[dataframe['Entity']=='Europe']` will give us all the rows from Europe's energy consumption.*\n", + "\n", + " a. Plot the total energy consumption of the United States and Europe\n", + " \n", + " b. Use a linear least-squares regression to find a function for the energy consumption as a function of year\n", + " \n", + " energy consumed = $f(t) = At+B$\n", + " \n", + " c. At what year would you change split the data and use two lines like we did in the \n", + " land temperature anomoly? Split the data and perform two linear fits. \n", + " \n", + " d. What is your prediction for US energy use in 2025? How about European energy use in 2025?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "energy = pd.read_csv('../data/primary-energy-consumption-by-region.csv')\n", + "CAN = energy[energy['Entity']=='Canada']\n", + "USA = energy[energy['Entity']=='United States']\n", + "EUR = energy[energy['Entity']=='Europe']\n", + "plt.plot(USA['Year'],USA['Primary Energy Consumption (terawatt-hours)'],'-o')\n", + "plt.plot(EUR['Year'],EUR['Primary Energy Consumption (terawatt-hours)'],'-s')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. We plotted Gordon Moore's empirical prediction that the rate of semiconductors on a computer chip would double every two years in [02_Seeing_Stats](./02_Seeing_Stats.ipynb). This prediction was known as Moore's law. Gordon Moore had originally only expected this empirical relation to hold from 1965 - 1975 [[1](https://en.wikipedia.org/wiki/Moore%27s_law),[2](https://spectrum.ieee.org/computing/hardware/gordon-moore-the-man-whose-name-means-progress)], but semiconductor manufacuturers were able to keep up with Moore's law until 2015. \n", + "\n", + "We can use a linear regression to find our own historical Moore's Law. \n", + "\n", + "Use your code from [02_Seeing_Stats](./02_Seeing_Stats.ipynb) to plot the semilog y-axis scatter plot \n", + "(i.e. `plt.semilogy`) for the \"Date of Introduction\" vs \"MOS transistor count\". \n", + "Color the data according to the \"Designer\".\n", + "\n", + "Create a linear regression for the data in the form of \n", + "\n", + "$log(transistor~count)= f(date) = A\\cdot date+B$\n", + "\n", + "rearranging\n", + "\n", + "$transistor~count= e^{f(date)} = e^B e^{A\\cdot date}$\n", + "\n", + "You can perform a least-squares linear regression using the following assignments\n", + "\n", + "$x_i=$ `dataframe['Date of Introduction'].values`\n", + "\n", + "and\n", + "\n", + "$y_i=$ as `np.log(dataframe['MOS transistor count'].values)`\n", + "\n", + "a. Plot your function on the semilog y-axis scatter plot\n", + "\n", + "b. What are the values of constants $A$ and $B$ for our Moore's law fit? How does this compare to Gordon Moore's prediction that MOS transistor count doubles every two years?\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + }, + "widgets": { + "state": {}, + "version": "1.1.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/03_stats_and_montecarlo.ipynb b/notebooks/03_stats_and_montecarlo.ipynb deleted file mode 100644 index cdba9fe..0000000 --- a/notebooks/03_stats_and_montecarlo.ipynb +++ /dev/null @@ -1,19908 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Statistics and Monte-Carlo Models\n", - "\n", - "Monte Carlo models use random numbers to either understand statistics or generate a solution. \n", - "The main element in a Monte Carlo model is the use of random numbers. We can generate random numbers in many ways, but most programming languages have 'pseudo'-random number generators. \n", - "\n", - "In Python, we use the numpy library as such\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "x=np.random.rand(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([2., 0., 1., 0., 0., 2., 1., 1., 0., 3.]),\n", - " array([0.13831169, 0.22124331, 0.30417493, 0.38710655, 0.47003817,\n", - " 0.55296979, 0.6359014 , 0.71883302, 0.80176464, 0.88469626,\n", - " 0.96762788]),\n", - " )" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.hist(x,bins=10, color='b', histtype='bar', edgecolor='w')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `numpy.random.rand(10)` function generates 10 random numbers between 0 and 1. The pyplot function `hist` then displays a histogram of these randomly generated numbers. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise\n", - "\n", - "Try generating more random numbers and plotting histograms of the results i.e. increase `10` to larger values. \n", - "\n", - "What should the histogram of `x` look like if Python is generating truly random numbers?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Examples of Monte Carlo models:\n", - "\n", - "Monte Carlo models have a wide array of applications. We are going to use Monte Carlo models in later modules to explore how uncertainty in measurements can be incorporated into computational models. Here is a brief list of Monte Carlo model use cases in real-world applications:\n", - "\n", - "- [Eigenvlaues in supercritical systems](https://link.springer.com/chapter/10.1007%2FBFb0049064)\n", - "- [average time between failures for reliability](http://www.egr.msu.edu/~mitraj/research/pubs/proc/singh-mitra_em_stdby_ias95.pdf)\n", - "- disordered materials (physics)\n", - "- [Calculation of the energy output of a wind farm](http://www.mdpi.com/1996-1073/9/4/286/pdf)\n", - "- [US Coast Guard rescue missions](https://en.wikipedia.org/wiki/Search_and_Rescue_Optimal_Planning_System)\n", - "- [Radiation shielding](http://www.sciencedirect.com/science/article/pii/S0920379612000580)\n", - "- [Predict number of asteroids that hit body of water](https://cneos.jpl.nasa.gov/sentry/intro.html)\n", - "- [Financial modeling](https://en.wikipedia.org/wiki/Monte_Carlo_methods_in_finance)\n", - "\n", - "We will explore Monte Carlo modeling through the use of three examples:\n", - "\n", - "1. Calculate the value of $\\pi$\n", - "\n", - "2. Calculate the integral of a function\n", - "\n", - "3. Propagate uncertainty in manufacturing into uncertainty in failure load" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "\n", - "\n", - "# Example 1: Calculate $\\pi$ with random numbers. \n", - "\n", - "Assuming we can actually generate random numbers (a topic of philosophical and heated debates) we can populate a unit square with random points and determine the ratio of points inside and outside of a circle.\n", - "\n", - "![Unit circle and unit square](../images/MonteCarloPi.gif)\n", - "\n", - "![1/4 Unit circle and 1/4 unit square](../images/MonteCarloPi_rand.gif)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "The ratio of the area of the circle to the square is:\n", - "\n", - "$\\frac{\\pi r^{2}}{4r^{2}}=\\frac{\\pi}{4}$\n", - "\n", - "So if we know the fraction of random points that are within the unit circle, then we can calculate $\\pi$\n", - "\n", - "(number of points in circle)/(total number of points)=$\\pi/4$" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.65636947, 0.58895963, 0.82791297, 0.19407244, 0.6149529 ,\n", - " 0.07214425, 0.51728665, 0.74773674, 0.35505096, 0.72987358])" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.rand(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "def montecarlopi(N):\n", - " '''Create random x-y-coordinates to and use ratio of circle-to-square to \n", - " calculate the value of pi\n", - " i.e. Acircle/Asquare = pi/4\n", - " Arguments\n", - " ---------\n", - " N: number of random points to produce between x=0-1 and y=0-1\n", - " \n", - " Returns\n", - " -------\n", - " our_pi: the best prediction of pi using N points\n", - " '''\n", - " \n", - "\n", - " x=np.random.rand(N,1);\n", - " y=np.random.rand(N,1);\n", - " R=np.sqrt(x**2+y**2); # compute radius\n", - " num_in_circle=sum(R<1);\n", - " total_num_pts =len(R);\n", - " our_pi = 4*num_in_circle/total_num_pts;\n", - " return our_pi" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mean value for pi = 3.123600\n", - "standard deviation is 0.056948\n", - "actual pi is 3.141593\n" - ] - } - ], - "source": [ - "test_pi=np.zeros(10)\n", - "for i in range(0,10):\n", - " test_pi[i]=montecarlopi(1000);\n", - "\n", - "print('mean value for pi = %f'%np.mean(test_pi))\n", - "print('standard deviation is %f'%np.std(test_pi))\n", - "print('actual pi is %f'%np.pi)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Exercises\n", - "\n", - "1. Why is there a standard deviation for the value of $\\pi$ calculated with a Monte Carlo method? Does it depend upon how many times you run the function i.e. the size of `test_pi`? or the number of random points `N`? Alter the script above to discover correlations\n", - "\n", - "2. How well does our function `montecarlopi` converge to the true value of $\\pi$ (you can use `np.pi` as a true value)? Plot the convergence as we did in [03-Numerical_error](https://github.uconn.edu/rcc02007/CompMech01-Getting-started/blob/master/notebooks/03-Numerical_error.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example 2: Calculate the integral of a function\n", - "\n", - "One way to calculate the integral of a function, $f(x)$, is to approximate it as a Riemann sum as such\n", - "\n", - "$I=\\int_{a}^{b}f(x) dx \\approx \\Delta x \\left(f(a)+f(a+\\Delta x)+f(a+2\\Delta x)+...+f(b)\\right))$.\n", - "\n", - "Another way to integrate this function is with a [Monte Carlo approach](https://en.wikipedia.org/wiki/Monte_Carlo_integration). We can approximate the integral as such\n", - "\n", - "$I=\\int_{a}^{b}f(x) dx \\approx \\frac{1}{n}\\sum_{i=1}^n f(x_i)$. \n", - "\n", - "where $x_i$ are uniformly random values of $x$ over the interval $[a,b]$. We were actually doing this in two dimensions in Example #1 when we compared the area of the circle to the area of a square. \n", - "\n", - "Visually, this approximation can be represented by the following figure.\n", - "\n", - "![Integration approximation](../images/integrals.png)\n", - "\n", - "The figure above shows the exact integral on the left as compared to two approximations, the Riemann sum (top-right) and Monte Carlo method (bottom-right). \n", - "\n", - "## Exercise (Discussion)\n", - "\n", - "Why does the Monte Carlo method work? Is there a benefit to random numbers as opposed to using an ordered set divided into equally-spaced intervals?" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.0999546133631974\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "f= lambda x:4/(1+x**2)\n", - "\n", - "n=100\n", - "xi=np.random.rand(n)\n", - "\n", - "plt.bar(xi,f(xi),width=1/n,color=[0.1,0,1,0.5],edgecolor='white')\n", - "plt.plot(np.linspace(0,1),f(np.linspace(0,1)),color='k')\n", - "#plt.hist(f(xi),cumulative=True)\n", - "#plt.hist(xi,cumulative=True)\n", - "\n", - "mypi=sum(f(xi))/n\n", - "print(mypi)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Example 3: Determine uncertainty in failure load based on geometry uncertainty\n", - "\n", - "In this example, we know that a steel bar will break under 940 MPa tensile stress. The bar is 1 mm by 2 mm with a tolerance of 1 %. What is the range of tensile loads that can be safely applied to the beam?\n", - "\n", - "$\\sigma_{UTS}=\\frac{F_{fail}}{wh}$\n", - "\n", - "$F_{fail}=\\sigma_{UTS}wh$" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "N=10000;\n", - "r=np.random.rand(N,1);\n", - "wmean=1; # in mm\n", - "wmin=wmean-wmean*0.01;\n", - "wmax=wmean+wmean*0.01;\n", - "hmean=2; # in mm\n", - "hmin=hmean-hmean*0.01;\n", - "hmax=hmean+hmean*0.01;\n", - "\n", - "wrand=wmin+(wmax-wmin)*r;\n", - "hrand=hmin+(hmax-hmin)*r;\n", - "\n", - "uts=940; # in N/mm^2=MPa\n", - "\n", - "Ffail=uts*wrand*hrand*1e-3; # force in kN\n", - "plt.hist(Ffail,bins=20,)\n", - "plt.xlabel('failure load (kN)')\n", - "plt.ylabel('relative counts')\n", - "plt.title('Failure load is {:.2f}+/- {:.2f} kN'.format(np.mean(Ffail),np.std(Ffail)));" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Normally, the tolerance is not a maximum/minimum specification, but instead a normal distribution that describes the standard deviation, or the 68% confidence interval.\n", - "\n", - "So instead, we should generate normally distributed dimensions." - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "N=100000;\n", - "wmean=1; # in mm\n", - "wstd=wmean*0.01; # standard deviation in mm\n", - "hmean=2; # in mm\n", - "hstd=hmean*0.01; # standard deviation in mm\n", - "\n", - "\n", - "wrand=np.random.normal(wmean,wstd,size=N);\n", - "hrand=np.random.normal(hmean,hstd,size=N);\n", - "uts=940; # in N/mm^2=MPa\n", - "\n", - "Ffail=uts*wrand*hrand*1e-3; # force in kN\n", - "plt.hist(Ffail,bins=20)\n", - "#plt.xlabel('failure load (kN)')\n", - "#plt.ylabel('relative counts')\n", - "plt.title('Failure load is {:.2f}+/- {:.2f} kN'.format(np.mean(Ffail),np.std(Ffail)));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this propagation of uncertainty, the final value of failure load seems to be independent of wheher the distribution is uniformly random or normally distributed. In both cases, the failure load is $\\approx 1.9 \\pm 0.03$ kN.\n", - "\n", - "The difference is much more apparent if you look at the number of occurrences that failure will occur whether the dimensions are uniformly random or normally distributed. \n", - "\n", - "For the uniformly random case, there are approximately 500 parts out of 10,000 that will fail at 1.9 kN. \n", - "\n", - "For the normally distributed case, there are approximately 17,500 parts out of 10,000 that will fail at 1.9 kN. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Thanks" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Using data from Dart board experiment" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "Gnuplot\n", - "Produced by GNUPLOT 5.0 patchlevel 3 \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t \n", - "\t \n", - "\t\n", - "\t\n", - "\t \n", - "\t \n", - "\t\n", - "\n", - "\n", - "\n", - "\n", - "\t\n", - "\t\t\n", - "\t\n", - "\n", - "\n", - "\n", - "\n", - "\t\t\n", - "\t\t-20\n", - "\t\n", - 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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### Modified under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2020 R.C. Cooper" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Linear regression with real data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Earth temperature over time\n", - "\n", - "In this lesson, we will apply all that we've learned (and more) to analyze real data of Earth temperature over time.\n", - "\n", - "Is global temperature rising? How much? This is a question of burning importance in today's world!\n", - "\n", - "Data about global temperatures are available from several sources: NASA, the National Climatic Data Center (NCDC) and the University of East Anglia in the UK. Check out the [University Corporation for Atmospheric Research](https://www2.ucar.edu/climate/faq/how-much-has-global-temperature-risen-last-100-years) (UCAR) for an in-depth discussion.\n", - "\n", - "The [NASA Goddard Space Flight Center](http://svs.gsfc.nasa.gov/goto?3901) is one of our sources of global climate data. They produced the video below showing a color map of the changing global surface **temperature anomalies** from 1880 to 2015.\n", - "\n", - "The term [global temperature anomaly](https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php) means the difference in temperature with respect to a reference value or a long-term average. It is a very useful way of looking at the problem and in many ways better than absolute temperature. For example, a winter month may be colder than average in Washington DC, and also in Miami, but the absolute temperatures will be different in both places." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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\n", - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import YouTubeVideo\n", - "YouTubeVideo('gGOzHVUQCw0')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How would we go about understanding the _trends_ from the data on global temperature?\n", - "\n", - "The first step in analyzing unknown data is to generate some simple plots using **Matplotlib**. We are going to look at the temperature-anomaly history, contained in a file, and make our first plot to explore this data. \n", - "\n", - "We are going to smooth the data and then we'll fit a line to it to find a trend, plotting along the way to see how it all looks.\n", - "\n", - "Let's get started!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Read a data file\n", - "\n", - "We took the data from the [NOAA](https://www.ncdc.noaa.gov/cag/) (National Oceanic and Atmospheric Administration) webpage. Feel free to play around with the webpage and analyze data on your own, but for now, let's make sure we're working with the same dataset.\n", - "\n", - "\n", - "We have a file named `land_global_temperature_anomaly-1880-2016.csv` in our `data` folder. This file contains the year on the first column, and averages of land temperature anomaly listed sequentially on the second column, from the year 1880 to 2016. We will load the file, then make an initial plot to see what it looks like.\n", - "\n", - "\n", - "##### Note:\n", - "\n", - "If you downloaded this notebook alone, rather than the full collection for this course, you may not have the data file on the location we assume below. In that case, you can download the data if you add a code cell, and execute the following code in it:\n", - "\n", - "```Python\n", - "from urllib.request import urlretrieve\n", - "URL = 'http://go.gwu.edu/engcomp1data5?accessType=DOWNLOAD'\n", - "urlretrieve(URL, 'land_global_temperature_anomaly-1880-2016.csv')\n", - "```\n", - "The data file will be downloaded to your working directory, and you will then need to remove the path information, i.e., the string `'../../data/'`, from the definition of the variable `fname` below.\n", - "\n", - "Let's start by importing NumPy." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To load our data from the file, we'll use the function [`numpy.loadtxt()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html), which lets us immediately save the data into NumPy arrays. (We encourage you to read the documentation for details on how the function works.) Here, we'll save the data into the arrays `year` and `temp_anomaly`. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "fname = '../data/land_global_temperature_anomaly-1880-2016.csv'\n", - "\n", - "year, temp_anomaly = np.loadtxt(fname, delimiter=',', skiprows=5, unpack=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Exercise\n", - "\n", - "Inspect the data by printing `year` and `temp_anomaly`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(137,)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "temp_anomaly.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Plot the data\n", - "\n", - "Let's first load the **Matplotlib** module called `pyplot`, for making 2D plots. Remember that to get the plots inside the notebook, we use a special \"magic\" command, `%matplotlib inline`:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `plot()` function of the `pyplot` module makes simple line plots. We avoid that stuff that appeared on top of the figure, that `Out[x]: [< ...>]` ugliness, by adding a semicolon at the end of the plotting command." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(year, temp_anomaly);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have a line plot, but if you see this plot without any information you would not be able to figure out what kind of data it is! We need labels on the axes, a title and why not a better color, font and size of the ticks. \n", - "**Publication quality** plots should always be your standard for plotting. \n", - "How you present your data will allow others (and probably you in the future) to better understand your work. \n", - "\n", - "We can customize the style of our plots using parameters for the lines, text, font and other plot options. We set some style options that apply for all the plots in the current session with [`plt.rc()`](https://matplotlib.org/api/_as_gen/matplotlib.plt.rc.html)\n", - "Here, we'll make the font of a specific type and size (serif and 16 points). You can also customize other parameters like line width, color, and so on (check out the documentation)." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rc('font', family='sans', size='18')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll redo the same plot, but now we'll add a few things to make it prettier and **publication quality**. We'll add a title, label the axes and, show a background grid. Study the commands below and look at the result!" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#You can set the size of the figure by doing:\n", - "plt.figure(figsize=(10,5))\n", - "\n", - "#Plotting\n", - "plt.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1) \n", - "plt.title('Land global temperature anomalies. \\n')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Land temperature anomaly [°C]')\n", - "plt.grid();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Better, no? Feel free to play around with the parameters and see how the plot changes. There's nothing like trial and error to get the hang of it. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Least-squares linear regression \n", - "\n", - "In order to have an idea of the general behavior of our data, we can find a smooth curve that (approximately) fits the points. We generally look for a curve that's simple (e.g., a polynomial), and does not reproduce the noise that's always present in experimental data. \n", - "\n", - "Let $f(x)$ be the function that we'll fit to the $n+1$ data points: $(x_i, y_i)$, $i = 0, 1, ... ,n$:\n", - "\n", - "$$ \n", - " f(x) = f(x; a_0, a_1, ... , a_m) \n", - "$$\n", - "\n", - "The notation above means that $f$ is a function of $x$, with $m+1$ variable parameters $a_0, a_1, ... , a_m$, where $m < n$. We need to choose the form of $f(x)$ a priori, by inspecting the experimental data and knowing something about the phenomenon we've measured. Thus, curve fitting consists of two steps: \n", - "\n", - "1. Choosing the form of $f(x)$.\n", - "2. Computing the parameters that will give us the \"best fit\" to the data. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What is the \"best\" fit?\n", - "\n", - "When the noise in the data is limited to the $y$-coordinate, it's common to use a **least-squares fit**, which minimizes the function\n", - "\n", - "$$\n", - "\\begin{equation} \n", - " S(a_0, a_1, ... , a_m) = \\sum_{i=0}^{n} [y_i - f(x_i)]^2\n", - "\\end{equation} \n", - "$$\n", - "\n", - "with respect to each $a_j$. We find the values of the parameters for the best fit by solving the following equations:\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " \\frac{\\partial{S}}{\\partial{a_k}} = 0, \\quad k = 0, 1, ... , m.\n", - "\\end{equation}\n", - "$$\n", - "\n", - "Here, the terms $r_i = y_i - f(x_i)$ are called residuals: they tell us the discrepancy between the data and the fitting function at $x_i$. \n", - "\n", - "Take a look at the function $S$: what we want to minimize is the sum of the squares of the residuals. The equations (2) are generally nonlinear in $a_j$ and might be difficult to solve. Therefore, the fitting function is commonly chosen as a linear combination of specified functions $f_j(x)$, \n", - "\n", - "$$\n", - "\\begin{equation*}\n", - " f(x) = a_0f_0(x) + a_1f_1(x) + ... + a_mf_m(x)\n", - "\\end{equation*}\n", - "$$\n", - "\n", - "which results in equations (2) being linear. In the case that the fitting function is polynomial, we have have $f_0(x) = 1, \\; f_1(x) = x, \\; f_2(x) = x^2$, and so on. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Linear regression \n", - "\n", - "When we talk about linear regression we mean \"fitting a function to the data.\" In this case,\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " f(x) = a_0 + a_1x\n", - "\\end{equation}\n", - "$$\n", - "\n", - "The function that we'll minimize is:\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " S(a_0, a_1) = \\sum_{i=0}^{n} [y_i - f(x_i)]^2 = \\sum_{i=0}^{n} (y_i - a_0 - a_1x_i)^2 \n", - "\\end{equation} \n", - "$$\n", - "\n", - "Equations (2) become:\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " \\frac{\\partial{S}}{\\partial{a_0}} = \\sum_{i=0}^{n} -2(y_i - a_0 - a_1x_i) = 2 \\left[ a_0(n+1) + a_1\\sum_{i=0}^{n} x_i - \\sum_{i=0}^{n} y_i \\right] = 0\n", - "\\end{equation} \n", - "$$\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " \\frac{\\partial{S}}{\\partial{a_1}} = \\sum_{i=0}^{n} -2(y_i - a_0 - a_1x_i)x_i = 2 \\left[ a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i \\right] = 0\n", - "\\end{equation} \n", - "$$\n", - "\n", - "Let's divide both equations by $2(n+1)$ and rearrange terms.\n", - "\n", - "Rearranging (6) and (7):\n", - "\n", - "$$\n", - "\\begin{align}\n", - " 2 \\left[ a_0(n+1) + a_1\\sum_{i=0}^{n} x_i - \\sum_{i=0}^{n} y_i \\right] &= 0 \\nonumber \\\\ \n", - " \\frac{a_0(n+1)}{n+1} + a_1 \\frac{\\sum_{i=0}^{n} x_i}{n+1} - \\frac{\\sum_{i=0}^{n} y_i}{n+1} &= 0 \\\\\n", - "\\end{align}\n", - "$$\n", - "\n", - "$$\n", - "\\begin{align}\n", - " a_0 = \\bar{y} - a_1\\bar{x}\n", - "\\end{align}\n", - "$$\n", - "\n", - "where $\\bar{x} = \\frac{\\sum_{i=0}^{n} x_i}{n+1}$ and $\\bar{y} = \\frac{\\sum_{i=0}^{n} y_i}{n+1}$.\n", - "\n", - "Rearranging (7):\n", - "\n", - "$$\n", - "\\begin{align}\n", - " 2 \\left[ a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i \\right] &= 0 \\\\\n", - " a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &=0 \\\\\n", - "\\end{align}\n", - "$$\n", - "\n", - "Now, if we replace $a_0$ from equation (8) into (9) and rearrange terms:\n", - "\n", - "$$\n", - "\\begin{align*}\n", - " (\\bar{y} - a_1\\bar{x})\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &= 0 \\\\ \n", - "\\end{align*}\n", - "$$\n", - "\n", - "Replacing the definitions of the mean values into the equation, \n", - "\n", - "$$\n", - "\\begin{align*}\n", - " \\left[\\frac{1}{n+1}\\sum_{i=0}^{n} y_i - \\frac{a_1}{n+1}\\sum_{i=0}^{n} x_i \\right]\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &= 0 \\\\ \n", - " \\frac{1}{n+1}\\sum_{i=0}^{n} y_i \\sum_{i=0}^{n} x_i - \\frac{a_1}{n+1}\\sum_{i=0}^{n} x_i \\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &= 0 \\\\ \n", - "\\end{align*}\n", - "$$\n", - "\n", - "Leaving everything in terms of $\\bar{x}$, \n", - "\n", - "$$\n", - "\\begin{align*}\n", - " \\sum_{i=0}^{n} y_i \\bar{x} - a_1\\sum_{i=0}^{n} x_i \\bar{x} + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i = 0 \n", - "\\end{align*}\n", - "$$\n", - "\n", - "Grouping the terms that have $a_1$ on the left-hand side and the rest on the right-hand side:\n", - "\n", - "$$\n", - "\\begin{align*}\n", - " a_1\\left[ \\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_i \\bar{x}\\right] &= \\sum_{i=0}^{n} x_iy_i - \\sum_{i=0}^{n} y_i \\bar{x} \\\\\n", - " a_1 \\sum_{i=0}^{n} (x_{i}^2 - x_i \\bar{x}) &= \\sum_{i=0}^{n} (x_iy_i - y_i \\bar{x}) \\\\\n", - " a_1 \\sum_{i=0}^{n} x_{i}(x_{i} -\\bar{x}) &= \\sum_{i=0}^{n} y_i(x_i - \\bar{x}) \n", - "\\end{align*}\n", - "$$\n", - "\n", - "Finally, we get that:\n", - "\n", - "$$\n", - "\\begin{align}\n", - " a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})}\n", - "\\end{align}\n", - "$$\n", - "\n", - "Then our coefficients are:\n", - "\n", - "$$\n", - "\\begin{align}\n", - " a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})} \\quad , \\quad a_0 = \\bar{y} - a_1\\bar{x}\n", - "\\end{align}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Let's fit!\n", - "\n", - "Let's now fit a straight line through the temperature-anomaly data, to see the trend over time. We'll use least-squares linear regression to find the slope and intercept of a line \n", - "\n", - "$$y = a_1x+a_0$$\n", - "\n", - "that fits our data.\n", - "\n", - "In our case, the `x`-data corresponds to `year`, and the `y`-data is `temp_anomaly`. To calculate our coefficients with the formula above, we need the mean values of our data. Sine we'll need to compute the mean for both `x` and `y`, it could be useful to write a custom Python _function_ that computes the mean for any array, and we can then reuse it.\n", - "\n", - "It is good coding practice to *avoid repeating* ourselves: we want to write code that is reusable, not only because it leads to less typing but also because it reduces errors. If you find yourself doing the same calculation multiple times, it's better to encapsulate it into a *function*. \n", - "\n", - "Remember the _key concept_ from [02_Getting-started](https://github.uconn.edu/rcc02007/CompMech01-Getting-started/blob/master/notebooks/02_Getting-started.ipynb): A function is a compact collection of code that executes some action on its arguments. \n", - "\n", - "Once *defined*, you can *call* a function as many times as you want. When we *call* a function, we execute all the code inside the function. The result of the execution depends on the *definition* of the function and on the values that are *passed* into it as *arguments*. Functions might or might not *return* values in their last operation. \n", - "\n", - "The syntax for defining custom Python functions is:\n", - "\n", - "```python\n", - "def function_name(arg_1, arg_2, ...):\n", - " '''\n", - " docstring: description of the function\n", - " '''\n", - " \n", - "```\n", - "\n", - "The **docstring** of a function is a message from the programmer documenting what he or she built. Docstrings should be descriptive and concise. They are important because they explain (or remind) the intended use of the function to the users. You can later access the docstring of a function using the function `help()` and passing the name of the function. If you are in a notebook, you can also prepend a question mark `'?'` before the name of the function and run the cell to display the information of a function. \n", - "\n", - "Try it!" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "?print" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the `help` function instead:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on built-in function print in module builtins:\n", - "\n", - "print(...)\n", - " print(value, ..., sep=' ', end='\\n', file=sys.stdout, flush=False)\n", - " \n", - " Prints the values to a stream, or to sys.stdout by default.\n", - " Optional keyword arguments:\n", - " file: a file-like object (stream); defaults to the current sys.stdout.\n", - " sep: string inserted between values, default a space.\n", - " end: string appended after the last value, default a newline.\n", - " flush: whether to forcibly flush the stream.\n", - "\n" - ] - } - ], - "source": [ - "help(print)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define a custom function that calculates the mean value of any array. Study the code below carefully. " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "def mean_value(array):\n", - " \"\"\" Calculate the mean value of an array \n", - " \n", - " Arguments\n", - " ---------\n", - " array: Numpy array \n", - " \n", - " Returns\n", - " ------- \n", - " mean: mean value of the array\n", - " \"\"\"\n", - " sum_elem = 0\n", - " for element in array:\n", - " sum_elem += element # this is the same as sum_elem = sum_elem + element\n", - " \n", - " mean = sum_elem / len(array)\n", - " \n", - " return mean\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you execute the cell above, the function`mean_value()` becomes available to use on any argument of the correct type. This function works on arrays of any length. We can try it now with our data." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1948.0\n" - ] - } - ], - "source": [ - "year_mean = mean_value(year)\n", - "print(year_mean)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0526277372263\n" - ] - } - ], - "source": [ - "temp_anomaly_mean = mean_value(temp_anomaly)\n", - "print(temp_anomaly_mean)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Neat! You learned how to write a Python function, and we wrote one for computing the mean value of an array of numbers. We didn't have to, though, because NumPy has a built-in function to do just what we needed: [`numpy.mean()`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.mean.html).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Exercise \n", - "\n", - "Calculate the mean of the `year` and `temp_anomaly` arrays using the NumPy built-in function, and compare the results with the ones obtained using our custom `mean_value` function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have mean values, we can compute our coefficients by following equations (12). We first calculate $a_1$ and then use that value to calculate $a_0$.\n", - "\n", - "Our coefficients are:\n", - "\n", - "$$\n", - " a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})} \\quad , \\quad a_0 = \\bar{y} - a_1\\bar{x}\n", - "$$ \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We already calculated the mean values of the data arrays, but the formula requires two sums over new derived arrays. Guess what, NumPy has a built-in function for that: [`numpy.sum()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html). Study the code below." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "a_1 = numpy.sum(temp_anomaly*(year - year_mean)) / numpy.sum(year*(year - year_mean)) " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0103702839435\n" - ] - } - ], - "source": [ - "print(a_1)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "a_0 = temp_anomaly_mean - a_1*year_mean" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-20.1486853847\n" - ] - } - ], - "source": [ - "print(a_0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Exercise\n", - "\n", - "Write a function that computes the coefficients, call the function to compute them and compare the result with the values we obtained before. As a hint, we give you the structure that you should follow:\n", - "\n", - "```python\n", - "def coefficients(x, y, x_mean, y_mean):\n", - " \"\"\"\n", - " Write docstrings here\n", - " \"\"\"\n", - "\n", - " a_1 = \n", - " a_0 = \n", - " \n", - " return a_1, a_0\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now have the coefficients of a linear function that best fits our data. With them, we can compute the predicted values of temperature anomaly, according to our fit. Check again the equations above: the values we are going to compute are $f(x_i)$. \n", - "\n", - "Let's call `reg` the array obtined from evaluating $f(x_i)$ for all years." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "reg = a_0 + a_1 * year" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the values of our linear regression, we can plot it on top of the original data to see how they look together. Study the code below. " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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FzMzOq9qmTMmhudnXqXF1U1P3CubepKbaSE21sWtXEy+9dJjf/GYHWVnd3+9E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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 5))\n", - "\n", - "plt.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1, alpha=0.5) \n", - "plt.plot(year, reg, 'k--', linewidth=2, label='Linear regression')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Land temperature anomaly [°C]')\n", - "plt.legend(loc='best', fontsize=15)\n", - "plt.grid();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Apply regression using NumPy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Above, we coded linear regression from scratch. But, guess what: we didn't have to because NumPy has built-in functions that do what we need!\n", - "\n", - "Yes! Python and NumPy are here to help! With [`polyfit()`](https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.polyfit.html), we get the slope and $y$-intercept of the line that best fits the data. With [`poly1d()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.poly1d.html), we can build the linear function from its slope and $y$-intercept.\n", - "\n", - "Check it out:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "# First fit with NumPy, then name the coefficients obtained a_1n, a_0n:\n", - "a_1n, a_0n = numpy.polyfit(year, temp_anomaly, 1)\n", - "\n", - "f_linear = numpy.poly1d((a_1n, a_0n)) " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0103702839435\n" - ] - } - ], - "source": [ - "print(a_1n)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-20.1486853847\n" - ] - } - ], - "source": [ - "print(a_0n)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "0.01037 x - 20.15\n" - ] - } - ], - "source": [ - "print(f_linear)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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FzMzOq9qmTMmhudnXqXF1U1P3CubepKbaSE21sWtXEy+9dJjf/GYHWVnd3+9E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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 5))\n", - "\n", - "plt.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1, alpha=0.5) \n", - "plt.plot(year, f_linear(year), 'k--', linewidth=2, label='Linear regression')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Land temperature anomaly [°C]')\n", - "plt.legend(loc='best', fontsize=15)\n", - "plt.grid();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## \"Split regression\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you look at the plot above, you might notice that around 1970 the temperature starts increasing faster that the previous trend. So maybe one single straight line does not give us a good-enough fit.\n", - "\n", - "What if we break the data in two (before and after 1970) and do a linear regression in each segment? \n", - "\n", - "To do that, we first need to find the position in our `year` array where the year 1970 is located. Thankfully, NumPy has a function called [`numpy.where()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) that can help us. We pass a condition and `numpy.where()` tells us where in the array the condition is `True`. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([90]),)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numpy.where(year==1970)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To split the data, we use the powerful instrument of _slicing_ with the colon notation. Remember that a colon between two indices indicates a range of values from a `start` to an `end`. The rule is that `[start:end]` includes the element at index `start` but excludes the one at index `end`. For example, to grab the first 3 years in our `year` array, we do:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1880., 1881., 1882.])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "year[0:3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we know how to split our data in two sets, to get two regression lines. We need two slices of the arrays `year` and `temp_anomaly`, which we'll save in new variable names below. After that, we complete two linear fits using the helpful NumPy functions we learned above." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [], - "source": [ - "year_1 , temp_anomaly_1 = year[0:90], temp_anomaly[0:90]\n", - "year_2 , temp_anomaly_2 = year[90:], temp_anomaly[90:]\n", - "\n", - "m1, b1 = numpy.polyfit(year_1, temp_anomaly_1, 1)\n", - "m2, b2 = numpy.polyfit(year_2, temp_anomaly_2, 1)\n", - "\n", - "f_linear_1 = numpy.poly1d((m1, b1))\n", - "f_linear_2 = numpy.poly1d((m2, b2))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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7HGMLmq7qS+B7wHHArkbXBwN+YGMnxCSEEEL0eMGgxmRqvySqpsYbnjcIUFSU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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 5))\n", - "\n", - "plt.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1, alpha=0.5) \n", - "plt.plot(year_1, f_linear_1(year_1), 'g--', linewidth=2, label='1880-1969')\n", - "plt.plot(year_2, f_linear_2(year_2), 'r--', linewidth=2, label='1970-2016')\n", - "\n", - "plt.xlabel('Year')\n", - "plt.ylabel('Land temperature anomaly [°C]')\n", - "plt.legend(loc='best', fontsize=15)\n", - "plt.grid();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have two different curves for two different parts of our data set. A little problem with this and is that the end point of our first regression doesn't match the starting point of the second regression. We did this for the purpose of learning, but it is not rigorously correct. We'll fix in in the next course module when we learn more about different types of regression. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## We learned:\n", - "\n", - "* Making our plots more beautiful\n", - "* Defining and calling custom Python functions\n", - "* Applying linear regression to data\n", - "* NumPy built-ins for linear regression\n", - "* The Earth is warming up!!!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "1. [_Essential skills for reproducible research computing_](https://barbagroup.github.io/essential_skills_RRC/) (2017). Lorena A. Barba, Natalia C. Clementi, Gilbert Forsyth. \n", - "2. _Numerical Methods in Engineering with Python 3_ (2013). Jaan Kiusalaas. Cambridge University Press.\n", - "3. _Effective Computation in Physics: Field Guide to Research with Python_ (2015). Anthony Scopatz & Kathryn D. Huff. O'Reilly Media, Inc.\n" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - }, - "widgets": { - "state": {}, - "version": "1.1.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/04_Stats_and_Montecarlo.ipynb b/notebooks/04_Stats_and_Montecarlo.ipynb new file mode 100644 index 0000000..7e22206 --- /dev/null +++ b/notebooks/04_Stats_and_Montecarlo.ipynb @@ -0,0 +1,958 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Created under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2020 R.C. Cooper" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Statistics and Monte-Carlo Models\n", + "\n", + "Monte Carlo models use random numbers to either understand statistics or generate a solution [1]. \n", + "The main element in a Monte Carlo model is the use of random numbers. Monte Carlo methods are very useful if you can easily execute a function lots of time or even in parallel. \n", + "\n", + "We can generate random numbers in many ways, but most programming languages have 'pseudo'-random number generators. \n", + "\n", + "In Python, we use the numpy library as such\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "x=np.random.rand(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "#Import rcParams to set font styles\n", + "from matplotlib import rcParams\n", + "\n", + "#Set font style and size \n", + "rcParams['font.family'] = 'sans'\n", + "rcParams['font.size'] = 16\n", + "rcParams['lines.linewidth'] = 3" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([3., 1., 0., 0., 1., 0., 0., 2., 1., 2.]),\n", + " array([0.34837708, 0.41148652, 0.47459596, 0.53770539, 0.60081483,\n", + " 0.66392427, 0.72703371, 0.79014314, 0.85325258, 0.91636202,\n", + " 0.97947146]),\n", + " )" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(x,bins=10, color='b', histtype='bar', edgecolor='w')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `numpy.random.rand(10)` function generates 10 random numbers between 0 and 1. The pyplot function `hist` then displays a histogram of these randomly generated numbers. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "\n", + "Try generating more random numbers and plotting histograms of the results i.e. increase `10` to larger values. \n", + "\n", + "What should the histogram of `x` look like if Python is generating truly random numbers?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Examples of Monte Carlo models:\n", + "\n", + "Monte Carlo models have a wide array of applications. We are going to use Monte Carlo models in later modules to explore how uncertainty in measurements can be incorporated into computational models. The three main applications for Monte Carlo models are used in three main classes: optimization, numerical integration, and generating population distributions [1]. \n", + "\n", + "Here is a brief list of Monte Carlo model use cases in real-world applications:\n", + "\n", + "- [Eigenvlaues in supercritical systems](https://link.springer.com/chapter/10.1007%2FBFb0049064)\n", + "- [average time between failures for reliability](http://www.egr.msu.edu/~mitraj/research/pubs/proc/singh-mitra_em_stdby_ias95.pdf)\n", + "- disordered materials (physics)\n", + "- [Calculation of the energy output of a wind farm](http://www.mdpi.com/1996-1073/9/4/286/pdf)\n", + "- [US Coast Guard rescue missions](https://en.wikipedia.org/wiki/Search_and_Rescue_Optimal_Planning_System)\n", + "- [Radiation shielding](http://www.sciencedirect.com/science/article/pii/S0920379612000580)\n", + "- [Predict number of asteroids that hit body of water](https://cneos.jpl.nasa.gov/sentry/intro.html)\n", + "- [Financial modeling](https://en.wikipedia.org/wiki/Monte_Carlo_methods_in_finance)\n", + "\n", + "We will explore Monte Carlo modeling through the use of three examples:\n", + "\n", + "1. Calculate the value of $\\pi$\n", + "\n", + "2. Calculate the integral of a function\n", + "\n", + "3. Propagate uncertainty in manufacturing into uncertainty in failure load" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "\n", + "\n", + "# Example 1: Calculate $\\pi$ with random numbers. \n", + "\n", + "Assuming we can actually generate random numbers (a topic of philosophical and heated debates) we can populate a unit square with random points and determine the ratio of points inside and outside of a circle.\n", + "\n", + "![Unit circle and unit square](../images/MonteCarloPi.gif)\n", + "\n", + "![1/4 Unit circle and 1/4 unit square](../images/MonteCarloPi_rand.gif)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "The ratio of the area of the circle to the square is:\n", + "\n", + "$\\frac{\\pi r^{2}}{4r^{2}}=\\frac{\\pi}{4}$\n", + "\n", + "So if we know the fraction of random points that are within the unit circle, then we can calculate $\\pi$\n", + "\n", + "(number of points in circle)/(total number of points)=$\\pi/4$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.61262684, 0.8262925 , 0.59955407, 0.43229944, 0.66224665,\n", + " 0.61154899, 0.34189469, 0.94126304, 0.41459117, 0.6400169 ])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.rand(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "def montecarlopi(N):\n", + " '''Create random x-y-coordinates to and use ratio of circle-to-square to \n", + " calculate the value of pi\n", + " i.e. Acircle/Asquare = pi/4\n", + " Arguments\n", + " ---------\n", + " N: number of random points to produce between x=0-1 and y=0-1\n", + " \n", + " Returns\n", + " -------\n", + " our_pi: the best prediction of pi using N points\n", + " '''\n", + " \n", + "\n", + " x=np.random.rand(N,1);\n", + " y=np.random.rand(N,1);\n", + " R=np.sqrt(x**2+y**2); # compute radius\n", + " num_in_circle=sum(R<1);\n", + " total_num_pts =len(R);\n", + " our_pi = 4*num_in_circle/total_num_pts;\n", + " return our_pi" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean value for pi = 3.188800\n", + "standard deviation is 0.041097\n", + "actual pi is 3.141593\n" + ] + } + ], + "source": [ + "test_pi=np.zeros(10)\n", + "for i in range(0,10):\n", + " test_pi[i]=montecarlopi(1000);\n", + "\n", + "print('mean value for pi = %f'%np.mean(test_pi))\n", + "print('standard deviation is %f'%np.std(test_pi))\n", + "print('actual pi is %f'%np.pi)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercises\n", + "\n", + "1. Why is there a standard deviation for the value of $\\pi$ calculated with a Monte Carlo method? Does it depend upon how many times you run the function i.e. the size of `test_pi`? or the number of random points `N`? Alter the script above to discover correlations\n", + "\n", + "2. How well does our function `montecarlopi` converge to the true value of $\\pi$ (you can use `np.pi` as a true value)? Plot the convergence as we did in [03-Numerical_error](https://github.uconn.edu/rcc02007/CompMech01-Getting-started/blob/master/notebooks/03-Numerical_error.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example 2: Calculate the integral of a function\n", + "\n", + "One way to calculate the integral of a function, $f(x)$, is to approximate it as a Riemann sum as such\n", + "\n", + "$I=\\int_{a}^{b}f(x) dx \\approx \\Delta x \\left(f(a)+f(a+\\Delta x)+f(a+2\\Delta x)+...+f(b)\\right))$.\n", + "\n", + "Another way to integrate this function is with a [Monte Carlo approach](https://en.wikipedia.org/wiki/Monte_Carlo_integration) [2, 3]. We can approximate the integral as such\n", + "\n", + "$I=\\int_{a}^{b}f(x) dx \\approx \\frac{1}{n}\\sum_{i=1}^n f(x_i)$. \n", + "\n", + "where $x_i$ are uniformly random values of $x$ over the interval $[a,b]$. We were actually doing this in two dimensions in Example #1 when we compared the area of the circle to the area of a square. \n", + "\n", + "Visually, this approximation can be represented by the following figure.\n", + "\n", + "![Integration approximation](../images/integrals.png)\n", + "\n", + "The figure above shows the exact integral on the left as compared to two approximations, the Riemann sum (top-right) and Monte Carlo method (bottom-right). \n", + "\n", + "The main benefit in using a Monte Carlo integration over a Riemann sum (or another non-random method e.g. trapezoidal) is that the integration converges as \n", + "\n", + "$error \\approx \\frac{1}{\\sqrt{n}}$\n", + "\n", + "independent of the number of dimensions in the formula. Non-random methods have convergence rates that depend upon the step size along each axis, so with two dimensions reducing the step size to 1/3 increases n by 4, in three dimensions its 8, in 4D its 16, and so on. We'll do an example with a 1D function to introduce the concept. \n", + "\n", + "You can even integrate functions that are not easy to evaluate, such as the [area of light shown by the bat signal](http://leios.github.io/Batman_Montecarlo)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise (Discussion)\n", + "\n", + "Why does the Monte Carlo method work? Is there a benefit to random numbers as opposed to using an ordered set divided into equally-spaced intervals?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we will calculate the integral $I=\\int_{1}^{3}f(x)dx$ where\n", + "\n", + "$I=\\int_{1}^{3}\\frac{x}{1+x^2}dx \\approx \\frac{1}{n}\\sum_{i=1}^n \\frac{x_i}{1+x_i^2}$\n", + "\n", + "Our first objective is to take random numbers between 0 and 1 and turn them into random number between 1 and 3. This process is called _scaling_ or _transforming_ our variable $x_{i}$" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ri=np.random.rand(100)\n", + "\n", + "xi=1+(3-1)*ri # scaling equation for our uniform distribution 0..1 to 1..3\n", + "\n", + "plt.hist(xi,label='x_i: our scaled var')\n", + "plt.hist(ri,label='r_i: our original var')\n", + "plt.legend();\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "\n", + "Compare the scaled histogram to the original histogram. What is similar? What is different?\n", + "\n", + "Make a scaling equation to get uniformly random numbers between 10 and 20. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performing the integration\n", + "\n", + "If you have been doing a lot of calculus lately, you may know that \n", + "\n", + "$\\int\\frac{x}{1+x^2}dx = \\frac{\\log({x^2+1})}{2}$. \n", + "\n", + "If not, Python's [Sympy: Symbolic algebra](https://docs.sympy.org/1.5.1/modules/integrals/integrals.html) can help you jog your memory [4]. It will also give us an exact solution to judge our convergence. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\log{\\left(x^{2} + 1 \\right)}}{2}$" + ], + "text/plain": [ + "log(x**2 + 1)/2" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sympy\n", + "\n", + "x=sympy.Symbol('x')\n", + "\n", + "sympy.integrate(x/(1+x**2),x)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle 0.80471895621705$" + ], + "text/plain": [ + "0.804718956217050" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sympy.integrate(x/(1+x**2),(x,1,3)).evalf()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So the exact integral that we are approximating is\n", + "\n", + "$I=\\frac{\\log{10}}{2}-\\frac{\\log{2}}{2}=0.80471895621705$\n", + "\n", + "_Note: We won't be using sympy for general numerical methods. It is helpful to do some algebra for us, but in general we want to use arrays so that our solutions can be scaled up and solved quicker by the computer._" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7824846191561594\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'f(x)')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f= lambda x:x/(1+x**2)\n", + "\n", + "n=10\n", + "ri=np.random.rand(n)\n", + "\n", + "xi=1+(3-1)*ri # scaling equation for our uniform distribution 0..1 to 1..3\n", + "# we scaled our variable by a factor of 2 so we scale the bins accorcdingly\n", + "bin_scale = 3-1 \n", + "\n", + "my_I=sum(f(xi))/n*bin_scale\n", + "print(my_I)\n", + "\n", + "plt.bar(xi,f(xi),width=bin_scale/n,color=[0.1,0,1,0.5])\n", + "plt.plot(np.linspace(0,4),f(np.linspace(0,4)),color='k')\n", + "plt.title('Graphical illustration of Monte Carlo'+\\\n", + " ' integration\\n I={:.4f}'.format(my_I))\n", + "plt.xlabel('x')\n", + "plt.ylabel('f(x)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def montecarlo_integrate(f,a,b,N):\n", + " '''Estimate the integral of a function f(x) from a->b with N \n", + " randomly chosen points\n", + " Arguments\n", + " ---------\n", + " f: a function of the form f=lambda x: ...\n", + " a: the lower bound of the integration\n", + " b: the upper bound of the integration\n", + " N: number of random points to produce between xi=a-b\n", + " \n", + " Returns\n", + " -------\n", + " our_I: the best prediction of the integral using N points \n", + " '''\n", + " ri=np.random.rand(n)\n", + "\n", + " xi=a+(b-a)*ri # scaling equation for our uniform distribution 0..1 to 1..3\n", + " # we scaled our variable by a factor of 2 so we scale the bins accorcdingly\n", + " bin_scale = b-a\n", + "\n", + " our_I=sum(f(xi))/n*bin_scale\n", + " \n", + " return our_I\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above function, we have defined a general Monte Carlo integration function. Now, we can create a for loop to try the function multiple times and get an average and standard deviation. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean value is I = 0.81410075695566\n", + "standard deviation is 0.028138512733038436\n", + "actual I = 0.80471895621705\n", + "error in mean = -0.009381800738609947\n" + ] + } + ], + "source": [ + "test_I=np.zeros(10)\n", + "for i in range(0,10):\n", + " test_I[i]=montecarlo_integrate(f,1,3,500);\n", + "\n", + "print('mean value is I = {}'.format(np.mean(test_I)))\n", + "print('standard deviation is {}'.format(np.std(test_I)))\n", + "print('actual I = 0.80471895621705')\n", + "print('error in mean = {}'.format(0.80471895621705-np.mean(test_I)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Example 3: Determine uncertainty in failure load based on geometry uncertainty\n", + "\n", + "In this example, we know that a steel bar will break under 940 MPa tensile stress. The bar is 1 mm by 2 mm with a tolerance of 10 %. What is the range of tensile loads that can be safely applied to the beam?\n", + "\n", + "$\\sigma_{UTS}=\\frac{F_{fail}}{wh}$\n", + "\n", + "$F_{fail}=\\sigma_{UTS}wh$" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "N=10000;\n", + "r=np.random.rand(N,1);\n", + "wmean=1; # in mm\n", + "wmin=wmean-wmean*0.1;\n", + "wmax=wmean+wmean*0.1;\n", + "hmean=2; # in mm\n", + "hmin=hmean-hmean*0.1;\n", + "hmax=hmean+hmean*0.1;\n", + "\n", + "wrand=wmin+(wmax-wmin)*r;\n", + "hrand=hmin+(hmax-hmin)*r;\n", + "\n", + "uts=940; # in N/mm^2=MPa\n", + "\n", + "Ffail=uts*wrand*hrand*1e-3; # force in kN\n", + "plt.hist(Ffail,bins=20,)\n", + "plt.xlabel('failure load (kN)')\n", + "plt.ylabel('relative counts')\n", + "plt.title('Failure load is {:.2f}+/- {:.2f} kN'.format(np.mean(Ffail),np.std(Ffail)));" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Normally, the tolerance is not a maximum/minimum specification, but instead a normal distribution that describes the standard deviation, or the 68% confidence interval.\n", + "\n", + "So instead, we should generate normally distributed dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "N=10000;\n", + "wmean=1; # in mm\n", + "wstd=wmean*0.1; # standard deviation in mm\n", + "hmean=2; # in mm\n", + "hstd=hmean*0.1; # standard deviation in mm\n", + "\n", + "\n", + "wrand=np.random.normal(wmean,wstd,size=N);\n", + "hrand=np.random.normal(hmean,hstd,size=N);\n", + "uts=940; # in N/mm^2=MPa\n", + "\n", + "Ffail=uts*wrand*hrand*1e-3; # force in kN\n", + "plt.hist(Ffail,bins=20)\n", + "#plt.xlabel('failure load (kN)')\n", + "#plt.ylabel('relative counts')\n", + "plt.title('Failure load is {:.2f}+/- {:.2f} kN'.format(np.mean(Ffail),np.std(Ffail)));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this propagation of uncertainty, the final value of failure load seems to be independent of wheher the distribution is uniformly random or normally distributed. In both cases, the failure load is $\\approx 1.9 \\pm 0.25$ kN.\n", + "\n", + "The difference is much more apparent if you look at the number of occurrences that failure will occur whether the dimensions are uniformly random or normally distributed. \n", + "\n", + "For the uniformly random case, there are approximately 500 parts out of 10,000 that will fail at 1.9 kN. \n", + "\n", + "For the normally distributed case, there are approximately 1500 parts out of 10,000 that will fail at 1.9 kN. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Where does a normal distribution come from?\n", + "\n", + "\"Everybody believes in the exponential law of errors: the experimenters, because they think it can be proved by mathematics; and the mathematicians, because they believe it has been established by observation\" [5].\n", + "\n", + "In the previous example, we drew dimensions from uniformly random distributions and normally distributed random distributions. Why do we use the \"normal\" distribution to describe data with a mean and standard deviation? There are exact statistical methods to derive the normal distribution, but let's take a look at a Monte Carlo approach. \n", + "\n", + "Let's say there are 10 different independent factors that could change the dimensions of the steel bar in question e.g. which tool was used, how old the blade is, the humidity, the temperature, and the list goes on. \n", + "\n", + "Let's consider one dimension. \n", + "Each of these factors could change the dimensions of the part, let's use a uniform scale of -1/2-1/2.\n", + "If the effect is 0, the dimension is exactly as specified. If the effect is -1/2, the dimension is much smaller. Conversely, if the effect is 1/2 the dimension is much larger. Now, we use a Monte Carlo model to generate 10 effects on 10,000 parts as shown in the next block." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "factors = np.random.rand(10000,10)-1/2 # each row represents a part and each column is an effect (-1/2-1/2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we have created 10,000 parts with 10 uniformly random effects between -1/2-1/2. \n", + "\n", + "We sum the effects and look at the final part distribution. The x-axis is labeled \"A.U.\" for arbitrary units, we are just assuming an effect of -1/2-1/2 for each of the 10 factors. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'number of parts')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dims = np.sum(factors,axis=1)\n", + "\n", + "plt.hist(dims,30)\n", + "plt.xlabel('effect A.U.')\n", + "plt.ylabel('number of parts')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, depending upon which random numbers were generated, you should see what looks like a normal distribution. \n", + "\n", + "Normal distributions come from the assumption that we have a large (or infinite) number of uncontrollable factors that can change our desired result. In our case, ideally each factor would have an effect of 0, because then it is exactly as specified, but the reality is that we can't control most factors. As engineers, we always have to consider the uncertainty in our models and measurements. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## We learned:\n", + "\n", + "* How to generate \"random\" numbers in Python$^+$\n", + "* The definition of a Monte Carlo model\n", + "* How to calculate $\\pi$ with Monte Carlo\n", + "* How to take the integral of a function with Monte Carlo\n", + "* How to propagate uncertainty in a model with Monte Carlo\n", + "* **Bonus**: use Sympy to do calculus and algebra for us! *no need for Wolfram, sorry Stephen*\n", + "* How to generate a normal distribution using uniformly random numbers\n", + "\n", + "$^+$ Remember, the computer only generates pseudo-random numbers. For further information **and** truly random numbers check [www.random.org](https://www.random.org/randomness/) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# References\n", + "\n", + "1. [Why the Monte Carlo method is so important today\n", + "Dirk P. Kroese, Tim Brereton *et al.* Wiley Interdisciplinary Reviews: Computational Statistics, 6, 6, 11 2014](https://onlinelibrary.wiley.com/doi/full/10.1002/wics.1314)\n", + "\n", + "2. [Wikipedia: Monte Carlo integration](https://en.wikipedia.org/wiki/Monte_Carlo_integration)\n", + "\n", + "3. [Weinzierl, S. (2000). \"Introduction to Monte Carlo methods\"](https://arxiv.org/abs/hep-ph/0006269)\n", + "\n", + "4. Meurer A, _et al._ (2017) SymPy: symbolic computing in Python. PeerJ Computer Science 3:e103 https://doi.org/10.7717/peerj-cs.103\n", + "\n", + "5. Whittaker, E. T. and Robinson, G. \"Normal Frequency Distribution.\" Ch. 8 in The Calculus of Observations: A Treatise on Numerical Mathematics, 4th ed. New York: Dover, p. 179, 1967." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Problems\n", + "\n", + "1. Calculate the area of a unit circle using the Monte Carlo integration method that we devloped in Example 2. Calculate the area of a quarter of the circle, then multiply the final result by 4. \n", + "\n", + " _Hint: You have to create a function that describes the perimeter of the circle._\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. 100 steel rods are going to be used to support a 1000 kg structure. The\n", + "rods will buckle when the load in any rod exceeds the [critical buckling\n", + "load](https://en.wikipedia.org/wiki/Euler%27s_critical_load)\n", + "\n", + " $P_{cr}=\\frac{\\pi^3 Er^4}{16L^2}$\n", + "\n", + " where E=200e9 Pa, r=0.01 m +/-0.001 m, and L is the \n", + " length of the rods supporting the structure. Create a Monte\n", + " Carlo model `montecarlo_buckle` that predicts \n", + " the mean and standard deviation of the buckling load for 100\n", + " samples with normally distributed dimensions r and L. \n", + "\n", + " ```python\n", + " mean_buckle_load,std_buckle_load=\\\n", + " montecarlo_buckle(E,r_mean,r_std,L,N=100)\n", + " ```\n", + "\n", + " a. What is the mean_buckle_load and std_buckle_load for L=5 m?\n", + "\n", + " b. What length, L, should the beams be so that only 2.5% will \n", + " reach the critical buckling load?" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def montecarlo_buckle(E,r_mean,r_std,L,N=100):\n", + " '''Generate N rods of length L with radii of r=r_mean+/-r_std\n", + " then calculate the mean and std of the buckling loads in for the\n", + " rod population holding a 1000-kg structure\n", + " Arguments\n", + " ---------\n", + " E: Young's modulus [note: keep units consistent]\n", + " r_mean: mean radius of the N rods holding the structure\n", + " r_std: standard deviation of the N rods holding the structure\n", + " L: length of the rods (or the height of the structure)\n", + " N: number of rods holding the structure, default is N=100 rods\n", + " Returns\n", + " -------\n", + " mean_buckle_load: mean buckling load of N rods under 1000*9.81/N-Newton load\n", + " std_buckle_load: std dev buckling load of N rods under 1000*9.81/N-Newton load\n", + " '''\n", + " \n", + " # your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Generate your own normal distribution using uniformly random numbers between -1/2 and 1/2. \n", + "\n", + " a. What is the effect of changing the number of factors?\n", + " \n", + " b. What is the effect of changing the number of samples?\n", + " \n", + " *Hint: for a-b try plotting histograms of the results.*\n", + " \n", + " c. How would you change the mean in your generated distribution?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/octave-workspace b/notebooks/octave-workspace deleted file mode 100644 index 88f4685a09da55006912cd8c5bac8ba901de30af..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 54 zcmeZIE=ep))iu=hVPIxpU`Wg>29gX6|2cs~abbLMNl|8A`oI7G|1$ywSb^A7&rr_* E0EcQ0Q~&?~ diff --git a/project/.ipynb_checkpoints/02_Analyze-data_project-checkpoint.ipynb b/project/.ipynb_checkpoints/02_Analyze-data_project-checkpoint.ipynb new file mode 100644 index 0000000..ce6abc8 --- /dev/null +++ b/project/.ipynb_checkpoints/02_Analyze-data_project-checkpoint.ipynb @@ -0,0 +1,140 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Computational Mechanics Project #02 - Create specifications for a spitballing robot\n", + "\n", + "On the first day of class, we threw $2\"\\times~2\"$ dampened paper (spitballs) at a target on the whiteboard. Now, we are going to analyze the accuracy of the class with some cool Python tools and design a robot that has the same accuracy and precision as the class. \n", + "\n", + "The goal of this project is to determine the precision of necessary components for a robot that can reproduce the class throwing distibution. We have generated pseudo random numbers using `numpy.random`, but the class target practice is an example of truly random distributions. If we repeated the exercise, there is a vanishingly small probability that we would hit the same points on the target, and there are no deterministic models that could take into account all of the factors that affected each hit on the board. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we ask ourselves some questions:\n", + "\n", + "1. How do we quantify the class accuracy and precision?\n", + "\n", + "2. If we design a robot, what design components can we control?\n", + "\n", + "3. How can we relate the controlled components to the class accuracy, and specify the component precision?\n", + "\n", + "The first question, we have some experience from our work in [02_Seeing_Stats](../notebooks/02_Seeing_Stats.ipynb). We can define the mean, standard deviation, measure the first, second, and third quartiles, etc. \n", + "\n", + "The second question is a physical question. We cannot control the placement of the robot or the target those are chosen for us. We cannot control temperature, mechanical vibrations, etc. We *can* control the desired initial velocity. The initial velocity will have some speed and direction, and both will be subject to random noise. Once the speed and direction are set, the location on the target is determined by kinematic equations for an object in freefall, as such\n", + "\n", + "$x_{impact} = \\frac{v_x}{v_y}d + x(0)~~~~~~~~~~~~~~~~~~~~(1.a)$\n", + "\n", + "$z_{impact} = d\\left(\\frac{v_z(0)}{v_y}-\\frac{g}{2v_y^2}d\\right)+ z(0)~~~~~(1.b)$.\n", + "\n", + "Where the location of impact is at a $y$-distance of $d$ at a point on the target with coordinates $(x_{impact},~z_{impact})$, and the initial velocity is $\\bar{v}=v_x\\hat{i}+v_y\\hat{j}+v_z(0)\\hat{k}$, the object is released at an initial location $\\bar{r}(0)=x(0)\\hat{i}+0\\hat{j}+z(0)\\hat{k}$, and the only acceleration is due to gravity, $\\bar{a}=-g\\hat{k}$. Equation (1) becomes much easier to evaluate if we assume that $v_x=0$, resulting in an evalution of the accuracy of the height of the impact, $z_{impact}$, as such\n", + "\n", + "$x_{impact} = x(0)~~~~~~~~~~~~~~~~~~~~(2.a)$\n", + "\n", + "$z_{impact} = \\frac{d}{\\cos{\\theta}}\\left(\\sin{\\theta}-\\frac{g}{2v_0^2\\cos{\\theta}}d\\right)+ z(0)~~~~~(2.b)$.\n", + "\n", + "Where $\\theta$ is the angle of the initial velocity and $v_0$ is the initial speed. Equation (2) restricts the analysis to height accuracy. You can incorporate the 2D impact analysis if you finish the 1D analysis. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The third question, is how we can relate equation (2) to the measured points of impact? For this, we can use Monte Carlo methods *(There are other methods, but Monte Carlo is one of the most straight-forward)*. Our Monte Carlo approach is as such, if we have a desired initial speed, $v_0$, and desired angle, $\\theta$, we can propagate the uncertainty of our actual speeds and angles into the $z_{impact}$ locations. Then, we can choose distributions in speed and angles that match the distributions in $z_{impact}$ locations. Here are the steps:\n", + "\n", + "1. Generate random $\\theta_i$ and $v_{0~i}$ variables\n", + "\n", + "2. Plug into eqn 2 for random $z_{impact~i}$ locations\n", + "\n", + "3. Compare to our measured $z_{impact}$ location statistics\n", + "\n", + "4. Repeat 1-3 until the predicted uncertainty matches the desired uncertainty, we can use a number of comparison metrics:\n", + " \n", + " - standard deviation\n", + " \n", + " - first, second, and third quartiles\n", + " \n", + " - visually, with box plots and histograms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Project Deliverables\n", + "\n", + "1. Statistical analysis of class accuracy and precision (x- and z-locations)\n", + "\n", + "2. A Monte Carlo model to generate impact heights based upon uncertainty in $\\theta_0$ and $v_0$. \n", + "\n", + "3. The precision required to recreate the class accuracy and precision with a robot. \n", + "**You must show some validation of your work**\n", + "\n", + "4. [BONUS] Repeat 2-3 taking into account the variation in $x_{impact}$ due to misalignment. \n", + "\n", + "Given constants and constraints:\n", + "\n", + "- $d=$3 m, distance to target\n", + "\n", + "- $g=$9.81 m/s$^2$, acceleration due to gravity\n", + "\n", + "- $z(0)=$0.3 m, the initial height is 0.3 m above the bull's eye\n", + "\n", + "- 4 m/s$');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
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Here is where things get really interesting. Matplotlib has the ability to create [event connections](https://matplotlib.org/devdocs/users/event_handling.html?highlight=mpl_connect), that is, connect the figure canvas to user-interface events on it, like mouse clicks. \n", + "\n", + "To use this ability, you write a function with the events you want to capture, and then connect this function to the Matplotlib \"event manager\" using [`mpl_connect()`](https://matplotlib.org/devdocs/api/backend_bases_api.html#matplotlib.backend_bases.FigureCanvasBase.mpl_connect). In this case, we connect the `'button_press_event'` to the function named `onclick()`, which captures the $(x,y)$ coordinates of the mouse click on the figure. Magic!" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def onclick(event):\n", + " '''Capture the x,y coordinates of a mouse click on the image'''\n", + " ix, iy = event.xdata, event.ydata\n", + " coords.append([ix, iy]) " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.figure()\n", + "plt.imshow(im)\n", + "\n", + "plt.plot(hits[:,0],hits[:,1],'x')\n", + "plt.plot(rings[:,0],rings[:,1],'s')" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 8, 16, 24])" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rings" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(hit_scale[:,0],hit_scale[:,1],'o')\n", + "plt.plot(np.mean(hit_scale[:,0]),np.mean(hit_scale[:,1]),'s')" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.plot(np.random.rand(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(3)\n", + "plt.imshow(im, interpolation='nearest')\n", + "\n", + "coords = []\n", + "connectId = fig.canvas.mpl_connect('button_press_event', onclick)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that in the previous code cell, we created an empty list named `coords`, and inside the `onclick()` function, we are appending to it the $(x,y)$ coordinates of each mouse click on the figure. After executing the cell above, you have a connection to the figure, via the user interface: try clicking with your mouse on the endpoints of the white lines of the metered panel (click on the edge of the panel to get approximately equal $x$ coordinates), then print the contents of the `coords` list below." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[229.51449092741933, 245.74974798387098],\n", + " [229.51449092741933, 324.3787802419355],\n", + " [325.44191028225805, 292.9271673387097],\n", + " [364.7564264112903, 300.7900705645161],\n", + " [380.4822328629032, 308.65297379032256],\n", + " [367.90158770161287, 329.09652217741933],\n", + " [339.59513608870964, 346.39490927419354],\n", + " [339.59513608870964, 371.5561995967742],\n", + " [364.7564264112903, 363.69329637096774],\n", + " [309.7161038306451, 393.57232862903226],\n", + " [308.14352318548384, 443.89490927419354],\n", + " [408.7886844758064, 421.8787802419355],\n", + " [473.26449092741933, 376.27394153225805],\n", + " [465.40158770161287, 280.34652217741933],\n", + " [496.8532006048387, 267.765877016129],\n", + " [452.8209425403225, 242.6045866935484],\n", + " [430.80481350806446, 220.5884576612903],\n", + " [627.3773941532259, 187.56426411290323],\n", + " [350.60320060483866, 176.5561995967742],\n", + " [364.7564264112903, 206.43523185483872]]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coords" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The $x$ coordinates are pretty close, but there is some variation due to our shaky hand (or bad eyesight), and perhaps because the metered panel is not perfectly vertical. We can cast the `coords` list to a NumPy array, then grab all the first elements of the coordinate pairs, then get the standard deviation as an indication of our error in the mouse-click captures." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hits = np.array(coords)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "plt.imshow(im, interpolation='nearest')\n", + "\n", + "coords = []\n", + "connectId = fig.canvas.mpl_connect('button_press_event', onclick)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[352.17578125, 283.00025202],\n", + " [468.54674899, 240.5405746 ],\n", + " [597.4983619 , 223.2421875 ],\n", + " [728.02255544, 207.51638105]])" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rings = np.array(coords)\n", + "rings" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.06351808135606307" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bull=rings[0,:]\n", + "distances=np.sqrt((rings[:,0]-bull[0])**2+(rings[:,1]-bull[1])**2)\n", + "ring_cm = np.array([0,8,16,24]) # ring distance from Bullseye cm\n", + "scale =np.mean(ring_cm[1:]/distances[1:]) # scale for distances to cm/px\n", + "scale" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 7.79120982, 2.36608055],\n", + " [ 7.79120982, -2.62828472],\n", + " [ 1.69808419, -0.63053862],\n", + " [ -0.79909844, -1.12997514],\n", + " [ -1.7979715 , -1.62941167],\n", + " [ -0.99887305, -2.92794664],\n", + " [ 0.79909844, -4.026707 ],\n", + " [ 0.79909844, -5.62490388],\n", + " [ -0.79909844, -5.12546736],\n", + " [ 2.69695724, -7.02332616],\n", + " [ 2.79684455, -10.21971993],\n", + " [ -3.59594299, -8.82129765],\n", + " [ -7.69132251, -5.9245658 ],\n", + " [ -7.19188599, 0.16855983],\n", + " [ -9.18963209, 0.96765827],\n", + " [ -6.39278754, 2.56585516],\n", + " [ -4.99436527, 3.96427743],\n", + " [-17.48027844, 6.06191084],\n", + " [ 0.09988731, 6.76112198],\n", + " [ -0.79909844, 4.86326318]])" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hit_scale=hits\n", + "hit_scale[:,0]=(bull[0]-hits[:,0])*scale\n", + "hit_scale[:,1]=(bull[1]-hits[:,1])*scale\n", + "hits" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(array([1., 1., 0., 2., 0., 1., 0., 1., 1., 1.]),\n", + " array([0.44381555, 1.12382265, 1.80382975, 2.48383685, 3.16384395,\n", + " 3.84385105, 4.52385815, 5.20386525, 5.88387235, 6.56387946,\n", + " 7.24388656]),\n", + " )" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "angle=np.arctan2(hit_scale[:,1],hit_scale[:,0])\n", + "radius=np.sqrt(hit_scale[:,1]**2+hit_scale[:,0]**2)\n", + "plt.figure()\n", + "#plt.hist(angle)\n", + "plt.hist(radius)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking ahead, what we'll do is repeat the process of capturing mouse clicks on the image, but clicking on the ball positions. Then, we will want to have the vertical positions converted to physical length (in meters), from the pixel numbers on the image.\n", + "\n", + "You can get the scaling from pixels to meters via the distance between two white lines on the metered panel, which we know is $0.25\\rm{m}$. \n", + "\n", + "Let's get the average vertical distance between two while lines, which we can calculate as:\n", + "\n", + "\\begin{equation}\n", + "\\overline{\\Delta y} = \\sum_{i=0}^N \\frac{y_{i+1}-y_i}{N-1}\n", + "\\end{equation}" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "123.56215213358072" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gap_lines = y_lines[1:] - y_lines[0:-1]\n", + "gap_lines.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}