diff --git a/f22/meena_lec_notes/cs220_survey_data.csv b/f22/meena_lec_notes/cs220_survey_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..abfd53ba7dfcd37e19c7d2b5ef5b4951d59cfa99
--- /dev/null
+++ b/f22/meena_lec_notes/cs220_survey_data.csv
@@ -0,0 +1,993 @@
+Lecture,Age,Major,Zip Code,Latitude,Longitude,Pizza topping,Pet preference,Runner,Sleep habit,Procrastinator
+LEC001,22,Engineering: Biomedical,53703,43.073051,-89.40123,none (just cheese),neither,No,no preference,Maybe
+LEC006,,Undecided,53706,43.073051,-89.40123,none (just cheese),neither,No,no preference,Maybe
+LEC004,18,Engineering: Industrial,53715,43.073051,-89.40123,none (just cheese),neither,No,no preference,Maybe
+LEC005,,Undecided,53706,43.073051,-89.40123,none (just cheese),neither,No,no preference,Maybe
+LEC002,,Undecided,53706,43.073051,-89.40123,none (just cheese),neither,No,no preference,Maybe
+LEC004,18,Engineering: Other|Engineering: Computer,53706,43.073051,-89.40123,none (just cheese),neither,No,no preference,Maybe
+LEC003,,Undecided,53706,43.073051,-89.40123,none (just cheese),neither,No,no preference,Maybe
+LEC003,18,Data Science,53715,43.073051,-89.40123,pineapple,cat,Yes,no preference,Maybe
+LEC006,18,Data Science,53706,35.4,119.11,none (just cheese),dog,No,night owl,Yes
+LEC006,18,Mathematics/AMEP,53706,44,-93,pepperoni,dog,No,night owl,Yes
+LEC002,21,Engineering: Other,53703,24.713552,46.675297,none (just cheese),cat,Yes,night owl,Maybe
+LEC003,19,Data Science,53705,24.6806,46.57936,pineapple,cat,No,early bird,No
+LEC004,24,Economics,53703,43,-89,pineapple,cat,Yes,early bird,Yes
+LEC003,18,Data Science,53706,36.102371,-115.174553,none (just cheese),dog,No,night owl,Yes
+LEC006,22,Psychology,53703,31.78,119.95,mushroom,cat,No,night owl,Yes
+LEC005,20,Data Science,53705,37.8,112.5,pepperoni,cat,Yes,night owl,Yes
+LEC004,24,Science: Biology/Life,53703,46.872131,-113.994019,pepperoni,dog,Yes,early bird,Yes
+LEC004,17,Engineering: Mechanical,53706,46.6242,8.0414,pineapple,dog,No,night owl,Yes
+LEC004,19,Engineering: Mechanical,53726,43.073051,-89.40123,none (just cheese),dog,Yes,early bird,No
+LEC002,19,Engineering: Mechanical,57303,41.878113,-87.629799,pineapple,dog,No,night owl,Yes
+LEC001,,Mathematics/AMEP,53706,31.230391,121.473701,basil/spinach,dog,No,no preference,Maybe
+LEC002,19,Mathematics/AMEP,53558,40.712776,-74.005974,sausage,dog,Yes,night owl,Yes
+LEC001,20,Economics (Mathematical Emphasis),53703,48.86,2.3522,pepperoni,dog,No,early bird,Yes
+LEC001,19,Engineering: Mechanical,53703,24.7,46.7,mushroom,dog,Yes,early bird,Maybe
+LEC005,18,Computer Science,53703,37.338207,-121.88633,green pepper,dog,Yes,night owl,Yes
+LEC003,19,Engineering: Mechanical,53558,43.073051,-89.40123,pepperoni,dog,No,night owl,Yes
+LEC005,20,Engineering: Mechanical,53715,38.9072,-77.0369,Other,cat,No,night owl,Yes
+LEC003,20,Data Science,53703,43.073051,-89.40123,pepperoni,dog,No,night owl,Yes
+LEC002,21,Science: Other|Political Science,53703,31.768318,35.213711,pepperoni,dog,No,no preference,Maybe
+LEC003,19,Mathematics/AMEP,53715,19.075983,72.877655,basil/spinach,cat,No,night owl,Maybe
+LEC001,23,Computer Science,53711,43.073929,-89.385239,sausage,dog,No,night owl,Yes
+LEC006,21,Business: Other,53715,25.761681,-80.191788,pepperoni,dog,No,night owl,Yes
+LEC003,19,Business: Other|Real Estate,53715,117,33,pepperoni,dog,Yes,night owl,No
+LEC004,19,Computer Science,53726,47.037872,-122.900696,tater tots,dog,No,night owl,Yes
+LEC004,24,Economics,53703,23.12911,113.264381,pepperoni,cat,Yes,early bird,Maybe
+LEC005,19,Data Science,53703,64.49796,165.40998,sausage,dog,No,night owl,Yes
+LEC003,19,Data Science,53705,25,47,mushroom,cat,No,early bird,Maybe
+LEC005,20,Engineering: Other|Engineering Physics: Scientific Computing,53715,43.073051,-89.4,none (just cheese),dog,No,night owl,Yes
+LEC005,20,Computer Science,53703,48.856613,2.352222,pepperoni,dog,No,night owl,Yes
+LEC002,19,Business: Finance,53726,43.04156,87.91006,pepperoni,dog,No,night owl,Yes
+LEC002,21,Data Science,53713,29.868336,121.543991,mushroom,dog,No,night owl,No
+LEC004,19,Computer Science,53715,40.712776,-74.005974,pepperoni,dog,No,night owl,Maybe
+LEC003,18,Computer Science,53706,5.93876,80.48433,Other,dog,No,night owl,Maybe
+LEC005,19,Engineering: Mechanical,53704,38.7,-77,pepperoni,cat,Yes,no preference,No
+LEC004,18,Engineering: Mechanical,53726,41.878113,-87.629799,pepperoni,dog,No,night owl,Maybe
+LEC005,19,Engineering: Other,53703,36.169941,-115.139832,pepperoni,dog,No,night owl,Maybe
+LEC005,19,Engineering: Mechanical,53703,43.078104,-89.431698,pepperoni,dog,Yes,night owl,Yes
+LEC006,18,Engineering: Biomedical,53051,33.6846,117.8265,pepperoni,dog,Yes,night owl,Yes
+LEC001,22,Engineering: Mechanical,53719,43.073051,-89.40123,none (just cheese),cat,Yes,night owl,Yes
+LEC001,18,Computer Science,53706,26.2992,87.2625,mushroom,dog,Yes,night owl,No
+LEC001,24,Business: Information Systems,53703,43.073051,-89.40123,macaroni/pasta,cat,No,night owl,No
+LEC006,19,Engineering: Mechanical,53703,43.04049,-87.91732,Other,dog,No,night owl,Yes
+LEC001,,Computer Science,53715,34.052235,-118.243683,green pepper,dog,No,night owl,Yes
+LEC002,20,Statistics,53703,40.7128,74.006,Other,dog,No,night owl,Maybe
+LEC005,23,Computer Science,53703,37.5,126.97,pepperoni,dog,No,night owl,No
+LEC002,21,Statistics,53703,52.370216,4.895168,pepperoni,dog,Yes,early bird,Maybe
+LEC002,18,Undecided,53706,38.56247,-121.70411,pepperoni,dog,Yes,night owl,Yes
+LEC006,18,Statistics,53706,40.712776,40.712776,pepperoni,dog,No,night owl,Yes
+LEC003,21,Economics,53715,43.073051,-89.40123,none (just cheese),dog,No,night owl,Yes
+LEC003,19,Engineering: Mechanical,53715,45,-93,sausage,dog,No,night owl,No
+LEC005,21,Business: Finance,53717,40.6461,-111.498,sausage,dog,No,night owl,Yes
+LEC001,26,Engineering: Mechanical,53703,41.902782,12.496365,pepperoni,dog,No,night owl,Yes
+LEC001,25,Economics,53703,40.712776,-74.005974,pepperoni,dog,No,night owl,Yes
+LEC003,18,Mathematics/AMEP,53706,31.230391,121.473701,mushroom,dog,Yes,early bird,No
+LEC001,19,Computer Science,53706,48.855709,2.29889,pepperoni,cat,Yes,night owl,Yes
+LEC005,17,Science: Biology/Life,53706,-18.766947,46.869106,basil/spinach,dog,Yes,early bird,Maybe
+LEC003,19,Business: Information Systems,53711,38.893452,-77.014709,pepperoni,dog,No,early bird,Yes
+LEC001,21,Computer Science,53715,16.306652,80.436539,Other,dog,No,night owl,Yes
+LEC006,19,Data Science,53703,35.689487,139.691711,sausage,neither,Yes,no preference,Maybe
+LEC004,18,Engineering: Industrial,53706,17.385044,78.486671,mushroom,dog,No,early bird,Yes
+LEC004,19,Computer Science,53715,37.774929,-122.419418,pepperoni,dog,No,night owl,Maybe
+LEC004,19,Data Science,53703,26.2644,20.3052,pepperoni,dog,No,night owl,Yes
+LEC005,18,Data Science,53706,40.712776,-74.005974,pepperoni,dog,Yes,no preference,Yes
+LEC002,18,Data Science,53706,36,117,Other,dog,No,early bird,Maybe
+LEC005,19,Data Science,50703,42.360081,-71.058884,sausage,cat,No,night owl,No
+LEC006,19,Computer Science,53711,36.569666,112.218744,pineapple,neither,Yes,early bird,Maybe
+LEC005,18,Computer Science,53706,37.54443,-121.95269,pepperoni,dog,No,night owl,Maybe
+LEC003,20,Mathematics/AMEP,53715,32.0853,34.781769,mushroom,dog,No,no preference,Yes
+LEC003,19,Data Science,53715,42.701847,-84.48217,tater tots,dog,No,night owl,Yes
+LEC003,18,Mathematics/AMEP,53706,40.179188,44.499104,Other,dog,Yes,no preference,Yes
+LEC002,,Computer Science,53711,2.81375,101.504272,sausage,dog,Yes,no preference,Maybe
+LEC001,18,Engineering: Industrial,53715,30.733315,76.779419,green pepper,cat,No,no preference,Yes
+LEC003,21,Data Science,53590,7.9519,98.3381,Other,dog,Yes,early bird,Yes
+LEC004,19,Data Science,53715,35.69,139.69,mushroom,dog,No,no preference,Maybe
+LEC002,19,Data Science,53704,26.473308,50.048218,Other,cat,Yes,night owl,Yes
+LEC002,22,Economics,53703,34.052235,-118.243683,pineapple,dog,No,night owl,Yes
+LEC006,18,Data Science,53706,19.075983,72.877655,mushroom,dog,Yes,night owl,Yes
+LEC003,,Business: Actuarial,53705,39.6336,118.16,basil/spinach,dog,Yes,early bird,Yes
+LEC003,18,Data Science,53706,52.370216,4.895168,mushroom,cat,Yes,no preference,No
+LEC003,18,Engineering: Mechanical,53706,52.368944,4.891663,pepperoni,cat,No,night owl,No
+LEC002,18,Science: Physics,53703,32,118,sausage,neither,No,night owl,No
+LEC005,18,Data Science,53706,17.384716,78.409424,mushroom,dog,Yes,night owl,Maybe
+LEC003,19,Data Science,53715,3.1569,101.7123,mushroom,cat,No,early bird,No
+LEC005,18,Computer Science,53706,43.769562,11.255814,Other,neither,No,night owl,Yes
+LEC006,18,Business: Actuarial,53706,48.856613,2.352222,mushroom,cat,No,no preference,Yes
+LEC004,20,Business: Actuarial,53711,40.7128,74.006,pepperoni,dog,Yes,early bird,No
+LEC005,20,Science: Biology/Life,53703,44.67082,-93.24432,mushroom,dog,No,no preference,Maybe
+LEC004,18,Mathematics/AMEP,53706,46.786671,-92.100487,pepperoni,cat,No,early bird,Yes
+LEC005,20,Economics,53703,48.856613,2.352222,pepperoni,neither,No,night owl,Maybe
+LEC006,18,Business: Finance,53706,40.409264,49.867092,Other,neither,No,early bird,No
+LEC004,21,Computer Science,53715,27.993828,120.699364,green pepper,dog,Yes,no preference,No
+LEC002,,Computer Science,53706,43.073051,-89.40123,Other,neither,Yes,no preference,Maybe
+LEC002,20,Engineering: Mechanical,53706,35.6762,139.6503,sausage,cat,Yes,night owl,Yes
+LEC001,20,Economics (Mathematical Emphasis),53703,43.073929,-89.385239,macaroni/pasta,cat,No,night owl,No
+LEC002,21,Business: Information Systems,53713,43.03638,-89.40292,pineapple,neither,Yes,night owl,Yes
+LEC004,18,Data Science,53706,45.31625,-92.59181,pepperoni,dog,No,night owl,Yes
+LEC001,21,Business: Finance,53711,43.073929,-89.385239,pepperoni,dog,No,no preference,Maybe
+LEC005,19,Engineering: Mechanical,53715,35.689487,139.691711,pepperoni,dog,No,night owl,Yes
+LEC003,18,Computer Science,53706,51.500153,-0.1262362,pepperoni,dog,No,night owl,Yes
+LEC002,22,Science: Biology/Life,53711,43.073051,-89.40123,mushroom,cat,No,no preference,No
+LEC004,18,Data Science,53706,42.360081,-71.058884,green pepper,dog,No,night owl,Yes
+LEC005,19,Engineering: Mechanical,53703,32.8328,117.2713,sausage,neither,Yes,night owl,Yes
+LEC003,20,Engineering: Mechanical,53715,44.834,-87.376,none (just cheese),dog,Yes,night owl,No
+LEC006,21,Economics,53703,41.902782,12.496365,none (just cheese),dog,No,no preference,Yes
+LEC003,25,Data Science,53703,34.693737,135.502167,pineapple,dog,No,early bird,Maybe
+LEC003,17,Computer Science,53703,19.075983,72.877655,Other,neither,Yes,no preference,No
+LEC002,19,Psychology,53715,30.5928,114.3052,sausage,cat,No,night owl,Yes
+LEC001,19,Computer Science,53703,51.507351,-0.127758,sausage,cat,Yes,no preference,Yes
+LEC006,17,Engineering: Industrial,53706,55.953251,-3.188267,Other,dog,No,night owl,Yes
+LEC005,,Computer Science,53703,43.073051,-89.40123,pineapple,dog,Yes,night owl,No
+LEC002,21,Engineering: Mechanical,53705,37.566536,126.977966,mushroom,cat,Yes,no preference,Maybe
+LEC002,18,Undecided,53715,48.775845,9.182932,Other,dog,No,night owl,Yes
+LEC004,19,Data Science,53703,43,-89,sausage,cat,No,early bird,Maybe
+LEC001,21,Science: Biology/Life,53703,36,117,macaroni/pasta,dog,No,night owl,Maybe
+LEC002,19,Business: Information Systems,53703,42.360081,-71.058884,pepperoni,dog,No,no preference,Yes
+LEC005,19,Computer Science,53706,-8.340539,115.091949,pineapple,dog,Yes,night owl,Maybe
+LEC003,20,Business: Information Systems,53726,43.073051,-89.40123,sausage,dog,Yes,night owl,No
+LEC003,,Science: Other,53715,39.904202,116.407394,mushroom,cat,No,night owl,Maybe
+LEC004,20,Engineering: Biomedical,53715,43.0707,12.6196,tater tots,dog,No,night owl,Maybe
+LEC004,19,Engineering: Biomedical,53715,41.878113,-87.629799,mushroom,dog,Yes,night owl,Yes
+LEC002,21,Business: Other|Accounting,53703,41.8781,87.6298,pepperoni,cat,No,night owl,No
+LEC002,17,Undecided,53706,33.742185,-84.386124,Other,dog,No,no preference,Yes
+LEC006,18,Data Science,53558,40.73061,-73.935242,pepperoni,dog,Yes,night owl,No
+LEC003,25,Data Science,53705,43.073051,-89.385239,sausage,cat,No,night owl,Maybe
+LEC002,18,Data Science,53706,37.34163,-122.05411,sausage,dog,No,night owl,Yes
+LEC006,18,Science: Biology/Life,53706,19.21833,72.978088,green pepper,neither,No,no preference,Maybe
+LEC002,,Business: Other|business analytics,53703,31.230391,121.473701,none (just cheese),cat,Yes,night owl,Maybe
+LEC003,,Data Science,53706,35.719312,139.784546,none (just cheese),neither,Yes,night owl,Yes
+LEC002,19,Engineering: Mechanical,53726,47.141041,9.52145,mushroom,dog,No,night owl,Yes
+LEC002,,Computer Science,53715,41.8781,87.6298,pepperoni,dog,No,no preference,Maybe
+LEC002,26,Science: Other|animal sciences,53705,25.204849,55.270782,pepperoni,dog,No,no preference,Maybe
+LEC003,21,Mathematics,53704,61.218056,-149.900284,green pepper,cat,Yes,early bird,Maybe
+LEC003,22,Engineering: Other,53703,49.28273,-123.120735,macaroni/pasta,cat,No,early bird,Maybe
+LEC001,18,Engineering: Other,53706,41.902782,12.496365,pepperoni,dog,No,night owl,Yes
+LEC003,20,Engineering: Mechanical,53726,39.81059,-74.71795,basil/spinach,dog,No,early bird,Yes
+LEC003,21,Health Promotion and Health Equity,53711,37.2982,113.0263,pepperoni,dog,No,early bird,No
+LEC003,20,Engineering: Mechanical,53703,38.722252,-9.139337,mushroom,dog,No,night owl,Yes
+LEC003,19,Engineering: Mechanical,53714,43,-89.4,none (just cheese),dog,No,night owl,Yes
+LEC002,19,Engineering: Industrial,53703,41.878,-87.63,pepperoni,dog,Yes,night owl,Yes
+LEC003,18,Computer Science,53706,43.073051,-89.40123,mushroom,neither,No,night owl,Yes
+LEC001,18,Engineering: Industrial,53706,19.655041,-101.169891,pepperoni,dog,Yes,no preference,Maybe
+LEC005,20,Engineering: Mechanical,53703,26.147,-81.795,pepperoni,dog,Yes,early bird,Yes
+LEC006,18,Business: Other,53706,51.507,-0.128,sausage,dog,No,no preference,No
+LEC005,19,Business: Other,53706,43,-89,pepperoni,dog,Yes,no preference,Yes
+LEC004,19,Engineering: Mechanical,53705,34.869709,-111.760902,pepperoni,cat,No,no preference,Maybe
+LEC005,21,Business: Finance,53703,3.15443,101.715103,pepperoni,cat,No,night owl,Yes
+LEC005,18,Engineering: Mechanical,53706,44.655991,-93.242752,none (just cheese),dog,Yes,night owl,Yes
+LEC003,18,Art,53706,36.25,138.25,macaroni/pasta,dog,No,night owl,Yes
+LEC005,19,Data Science,53715,41.94288,-87.68667,pepperoni,dog,Yes,night owl,Yes
+LEC005,18,Data Science,53703,44.2795,73.9799,pepperoni,dog,Yes,night owl,No
+LEC002,19,Mathematics/AMEP,53715,37.80718,23.734864,pineapple,cat,No,night owl,Yes
+LEC004,18,Computer Science,53706,35.689487,139.691711,pepperoni,cat,No,night owl,Yes
+LEC006,18,Engineering: Mechanical,53706,43.0826,-97.16051,pepperoni,dog,No,no preference,Yes
+LEC006,18,Engineering: Other,53715,37.441883,-122.143021,mushroom,dog,Yes,night owl,Maybe
+LEC006,18,Engineering: Mechanical,53706,44.883,-87.86291,pepperoni,dog,No,early bird,Yes
+LEC004,19,Engineering: Mechanical,53706,40.73598,-74.37531,none (just cheese),dog,Yes,early bird,No
+LEC001,20,Business: Actuarial,53703,42.28,-83.74,mushroom,dog,No,night owl,Yes
+LEC003,17,Engineering: Mechanical,53706,37.98381,23.727539,pineapple,dog,Yes,night owl,No
+LEC004,18,Computer Science,53706,40.27385,-74.75972,sausage,dog,Yes,night owl,Yes
+LEC002,19,Economics,53703,90.1994,38.627,none (just cheese),dog,No,early bird,Yes
+LEC002,21,"Mathematics, Data Science",53703,30.572815,104.066803,sausage,dog,No,night owl,Maybe
+LEC002,,Computer Science,53717,36,139,mushroom,dog,Yes,early bird,Yes
+LEC006,19,Science: Biology/Life,53715,45.289143,-87.021847,none (just cheese),cat,No,night owl,Maybe
+LEC002,21,Mathematics/AMEP,53703,20.878332,-156.682495,pepperoni,cat,No,night owl,Yes
+LEC003,22,Mathematics/AMEP,53715,44.481586,-88.005981,pepperoni,neither,No,night owl,Yes
+LEC006,18,Data Science,53706,43.073051,-89.40123,pepperoni,dog,No,night owl,Yes
+LEC005,18,Computer Science,53706,30.733315,76.779419,none (just cheese),dog,No,night owl,Yes
+LEC005,20,Mathematics/AMEP,53703,38.837702,-238.449497,pepperoni,dog,No,night owl,Yes
+LEC005,,Computer Science,53593,50.116322,-122.957359,sausage,dog,No,night owl,Yes
+LEC005,18,Computer Science,53715,43.059023,-89.296875,pepperoni,cat,No,night owl,Maybe
+LEC005,19,Engineering: Industrial,53703,22.2255,-159.4835,pepperoni,cat,Yes,night owl,Yes
+LEC005,18,Engineering: Biomedical,53593,43.073051,-89.40123,green pepper,cat,No,night owl,Maybe
+LEC005,20,Engineering: Mechanical,53715,41.283211,-70.099228,sausage,dog,No,no preference,Maybe
+LEC005,18,Data Science,53715,25.26741,55.292679,basil/spinach,cat,Yes,early bird,Yes
+LEC005,19,Business: Other,53726,43.038902,-87.906471,pepperoni,dog,No,night owl,Yes
+LEC002,,Undecided,53703,30.5723,104.0665,sausage,dog,No,night owl,Yes
+LEC006,18,Engineering: Mechanical,53706,30.2672,97.7431,pepperoni,dog,No,night owl,No
+LEC006,20,Data Science,53703,36.731651,-119.785858,Other,dog,Yes,night owl,Yes
+LEC005,18,Computer Science,53706,43.038902,-87.906471,pepperoni,dog,No,night owl,Yes
+LEC004,,Business: Finance,53703,33.8688,151.2093,green pepper,dog,Yes,night owl,Yes
+LEC005,18,Science: Other|Science: Genetics and Genomics,53715,43.073051,-89.40123,mushroom,dog,No,no preference,Yes
+LEC003,19,Engineering: Mechanical,53715,44.90767,-93.183594,basil/spinach,dog,No,night owl,Maybe
+LEC006,18,Business: Finance,53706,-33.448891,-70.669266,macaroni/pasta,dog,No,night owl,Yes
+LEC006,17,Business: Finance,53706,43.296482,5.36978,pineapple,dog,No,night owl,Yes
+LEC006,21,Mathematics/AMEP,53703,30.572815,104.066803,green pepper,dog,No,no preference,Maybe
+LEC005,20,Engineering: Mechanical,53703,41.99884,-87.68828,Other,dog,No,no preference,No
+LEC001,19,Business: Information Systems,53703,39.481655,-106.038353,macaroni/pasta,dog,Yes,night owl,Yes
+LEC004,19,Engineering: Mechanical,53703,41.883228,-87.632401,pepperoni,dog,No,no preference,Maybe
+LEC004,18,Engineering: Industrial,53706,41.878113,41.878113,pepperoni,dog,No,night owl,No
+LEC004,19,Engineering: Mechanical,53703,28.228209,112.938812,none (just cheese),neither,Yes,early bird,Yes
+LEC003,18,Data Science,89451,34.42083,-119.698189,green pepper,dog,No,early bird,No
+LEC003,19,Computer Science,53703,41.3874,2.1686,pepperoni,cat,No,early bird,No
+LEC005,20,Science: Biology/Life,53703,32.05196,118.77803,sausage,neither,No,night owl,Yes
+LEC004,19,Engineering: Mechanical,53706,50.075539,14.4378,none (just cheese),neither,No,night owl,Yes
+LEC003,20,Statistics (actuarial route),53715,43.134315,-88.220062,sausage,dog,No,early bird,No
+LEC004,19,Computer Science,53706,17.385044,78.486671,pepperoni,neither,Yes,night owl,Yes
+LEC002,18,Engineering: Mechanical,53706,53707,-88.415382,Other,dog,No,night owl,Yes
+LEC004,19,Computer Science,53706,45.440845,12.315515,sausage,dog,No,night owl,Yes
+LEC004,18,Computer Science,53706,55.953251,-3.188267,Other,dog,No,night owl,Maybe
+LEC004,18,Engineering: Mechanical,53706,33.8902,-118.39848,sausage,dog,Yes,night owl,Yes
+LEC001,20,Business: Other|Business: Accounting,53703,31.230391,121.473701,pepperoni,cat,Yes,no preference,No
+LEC004,18,Data Science,53706,39.512611,116.677063,pepperoni,dog,No,night owl,Maybe
+LEC003,18,Undecided,53706,41.256538,95.934502,Other,dog,No,no preference,Yes
+LEC003,18,Data Science,53706,19.075983,72.877655,pepperoni,dog,No,night owl,No
+LEC003,22,Economics,53703,40.753685,-73.999161,green pepper,dog,No,night owl,Maybe
+LEC003,18,Data Science,53706,51.507351,-0.127758,pepperoni,cat,No,night owl,Yes
+LEC003,,Engineering: Mechanical,53706,42.44817,-71.224716,pepperoni,cat,Yes,night owl,Maybe
+LEC003,17,Engineering: Other|Computer Engineering,53706,42.36,-71.059,basil/spinach,neither,No,early bird,Maybe
+LEC003,21,Business: Actuarial,53706,32.715736,-117.161087,green pepper,dog,Yes,night owl,No
+LEC003,,Engineering: Other|Computer engineering,53706,35.689487,139.691711,Other,cat,No,night owl,Yes
+LEC003,18,Mathematics/AMEP,53715,41.385063,2.173404,pepperoni,cat,Yes,no preference,Maybe
+LEC003,20,Computer Science,53705,30.274084,120.155067,mushroom,cat,No,night owl,Yes
+LEC005,,Computer Science,53705,51.507351,-0.127758,basil/spinach,dog,No,night owl,Yes
+LEC003,18,Computer Science,53706,45.45676,15.29662,sausage,dog,Yes,early bird,Yes
+LEC003,18,Engineering: Industrial,53706,18.92421,-99.221565,green pepper,dog,Yes,night owl,Yes
+LEC004,18,Engineering: Other|Material Science Engineering,53703,38.941631,-119.977219,pepperoni,dog,Yes,night owl,Yes
+LEC002,21,Economics,53705,25.03841,121.5637,pepperoni,cat,No,night owl,Maybe
+LEC005,,Civil engineering - hydropower engineering,53705,34,113,pineapple,neither,No,night owl,Maybe
+LEC005,18,Computer Science,53706,40.7,-74.005,pepperoni,cat,No,early bird,No
+LEC001,19,Engineering: Mechanical,53706,35.142441,-223.154297,green pepper,neither,Yes,night owl,Yes
+LEC006,18,Data Science,53706,43.05891,-88.007462,pepperoni,dog,Yes,night owl,Yes
+LEC006,,Engineering: Mechanical,53706,37.566536,126.977966,pepperoni,dog,Yes,night owl,No
+LEC005,18,Data Science,53706,36.393154,25.46151,none (just cheese),dog,No,night owl,No
+LEC001,,Engineering: Mechanical,53715,19.8968,155.5828,pepperoni,dog,No,night owl,No
+LEC002,19,Engineering: Biomedical,53706,48.494904,-113.979034,macaroni/pasta,cat,No,night owl,Yes
+LEC005,18,Engineering: Mechanical,53706,41.88998,12.49426,pineapple,dog,Yes,night owl,Yes
+LEC003,17,Data Science,53706,-7.257472,112.75209,pineapple,dog,Yes,early bird,Yes
+LEC005,19,Economics,53703,40.592331,-111.820152,none (just cheese),dog,Yes,night owl,Maybe
+LEC005,19,Data Science,53704,38.722252,-9.139337,pepperoni,dog,No,night owl,Yes
+LEC003,,Computer Science,53703,64.963051,-19.020836,pineapple,dog,No,no preference,Maybe
+LEC002,20,Economics,53703,43.769562,11.255814,mushroom,dog,No,night owl,Yes
+LEC004,20,Business: Actuarial,53715,44.834209,-87.376266,sausage,dog,No,no preference,Yes
+LEC005,21,Economics,53703,37.751824,-122.420105,green pepper,cat,No,night owl,Yes
+LEC004,22,Economics,53703,56.490669,4.202646,mushroom,dog,No,no preference,Yes
+LEC004,18,Engineering: Mechanical,53706,44.9058,-93.28535,pepperoni,cat,Yes,night owl,Maybe
+LEC004,19,Data Science,53703,41.878113,-87.629799,sausage,dog,No,night owl,Yes
+LEC001,21,Computer Science,53703,43.21518,-87.94241,pepperoni,dog,No,no preference,Maybe
+LEC004,24,Science: Chemistry,53703,32.715736,-117.161087,mushroom,dog,Yes,night owl,Maybe
+LEC005,19,Engineering: Mechanical,53715,39.412327,-77.425461,pepperoni,cat,Yes,early bird,Yes
+LEC004,20,Statistics,53703,43.07391,-89.39356,pepperoni,dog,No,early bird,Maybe
+LEC005,21,Business: Finance,53703,38.178127,-92.781052,mushroom,dog,No,night owl,Yes
+LEC004,18,Engineering: Mechanical,53706,35.689487,139.691711,pepperoni,dog,No,no preference,Yes
+LEC005,18,Data Science,60521,41.9,87.6,pepperoni,dog,Yes,night owl,Yes
+LEC005,23,Business: Information Systems,53558,43.073051,-89.40123,pepperoni,dog,Yes,early bird,No
+LEC004,18,Engineering: Mechanical,53706,43.739507,7.426706,pepperoni,dog,No,night owl,Yes
+LEC005,21,Data Science,53703,25,121,pepperoni,dog,No,night owl,Yes
+LEC005,20,Business: Information Systems,53703,43.073051,-89.40123,pepperoni,dog,Yes,night owl,Yes
+LEC004,,Engineering: Biomedical,53715,41.385063,2.173404,pepperoni,dog,Yes,no preference,No
+LEC004,18,Communication arts,53715,22.543097,114.057861,mushroom,cat,Yes,early bird,Yes
+LEC001,22,Engineering: Mechanical,53703,47.497913,19.040236,pepperoni,dog,No,no preference,No
+LEC005,19,Computer Science,54706,34.05,-118.24,sausage,cat,Yes,night owl,Yes
+LEC005,18,Engineering: Biomedical,53706,46.818188,8.227512,pineapple,dog,Yes,no preference,Yes
+LEC004,19,Engineering: Mechanical,53715,42.36,-71.058884,pepperoni,dog,Yes,no preference,Yes
+LEC005,21,Data Science,53703,36.4,117,pineapple,dog,Yes,night owl,Yes
+LEC005,19,Engineering: Mechanical,53704,35.6762,139.6503,sausage,dog,No,night owl,Maybe
+LEC004,20,Economics,53703,44.885,-93.147,pepperoni,dog,No,early bird,Yes
+LEC004,20,Health Promotion and Health Equity,53704,48.8566,2.349014,pepperoni,dog,No,night owl,Yes
+LEC004,19,Engineering: Mechanical,53715,43.073051,-89.40123,sausage,dog,Yes,no preference,Yes
+LEC001,20,Business andministration,53703,37.389091,-5.984459,pineapple,dog,Yes,night owl,Maybe
+LEC003,23,Mathematics/AMEP,53715,24.88,102.8,pineapple,dog,Yes,early bird,Yes
+LEC002,20,Engineering: Industrial,53703,44.389,12.9908,sausage,dog,No,early bird,Maybe
+LEC005,20,Education,53703,41.878113,-87.629799,basil/spinach,cat,Yes,early bird,No
+LEC003,19,Science: Biology/Life,53703,41.38,2.17,pepperoni,dog,Yes,no preference,Maybe
+LEC006,18,Pre-business,53706,41.8781,87.6298,pepperoni,dog,Yes,night owl,Yes
+LEC004,20,Business: Finance,53706,41.10475,-80.64916,basil/spinach,dog,Yes,night owl,Yes
+LEC004,20,Statistics,53703,42.360081,-71.058884,pepperoni,dog,No,night owl,Yes
+LEC003,18,Engineering: Mechanical,53706,24.5554,81.7842,pepperoni,dog,No,early bird,Maybe
+LEC004,19,Data Science,53703,38.72,75.07,none (just cheese),dog,Yes,early bird,Yes
+LEC006,20,Engineering: Mechanical,53705,30.572815,104.066803,mushroom,cat,Yes,no preference,Maybe
+LEC003,20,Mathematics/AMEP,53726,43.07199,-89.42629,mushroom,dog,No,night owl,Yes
+LEC004,20,Engineering: Mechanical,53705,48,7.85,pepperoni,dog,Yes,night owl,No
+LEC001,20,Computer Science,53703,40.7128,74.006,pepperoni,dog,Yes,night owl,Maybe
+LEC003,18,Business: Actuarial,53719,14.599512,120.984222,pineapple,cat,Yes,no preference,Maybe
+LEC003,17,Computer Science,53715,37.38522,-122.114128,Other,dog,No,night owl,No
+LEC003,18,Computer Science,53706,37.386051,-122.083855,sausage,dog,Yes,no preference,Maybe
+LEC004,23,Business: Finance,53703,31.230391,121.473701,mushroom,neither,No,night owl,No
+LEC004,21,Engineering: Industrial,53703,37.94048,-78.63664,Other,dog,Yes,night owl,Yes
+LEC002,21,Mathematics/AMEP,53715,42.360081,-71.058884,mushroom,neither,Yes,early bird,Yes
+LEC002,18,Engineering: Industrial,53715,40.712776,-74.005974,pineapple,dog,Yes,night owl,Yes
+LEC001,22,Engineering: Mechanical,53726,36.97447,122.02899,pepperoni,dog,No,no preference,Yes
+LEC005,,Mathematics/AMEP,53715,36.651199,117.120094,mushroom,neither,No,night owl,Yes
+LEC005,18,Mathematics/AMEP,53706,46.482525,30.723309,basil/spinach,dog,No,early bird,Yes
+LEC006,20,Engineering: Industrial,53703,42.102901,-88.368896,pepperoni,dog,No,night owl,Maybe
+LEC006,18,Computer Science,53706,-31.959153,-244.161255,green pepper,dog,No,night owl,Yes
+LEC002,24,Computer Science,53715,30.704852,104.003904,mushroom,neither,Yes,no preference,Maybe
+LEC005,19,Engineering: Mechanical,53705,40.712776,-74.005974,pepperoni,dog,No,early bird,No
+LEC004,22,Science: Biology/Life,53705,39.758161,39.758161,pepperoni,cat,No,early bird,Yes
+LEC005,20,Statistics,53703,43.073051,-89.40123,sausage,dog,Yes,night owl,Yes
+LEC001,19,Data Science,53703,41,87,sausage,dog,No,no preference,No
+LEC004,20,Engineering: Mechanical,53726,58.2996,14.4444,sausage,cat,No,night owl,Maybe
+LEC005,18,Engineering: Mechanical,53562,1.3521,103.8198,green pepper,cat,No,early bird,Maybe
+LEC002,19,Engineering: Mechanical,53703,44.46534,-72.684303,green pepper,cat,Yes,night owl,Yes
+LEC002,20,Engineering: Industrial,53726,43.038902,-87.906471,pepperoni,dog,No,night owl,Yes
+LEC006,18,Business: Actuarial,53706,45.464203,9.189982,pepperoni,cat,Yes,night owl,Yes
+LEC006,18,Computer Science,53715,30.58198,114.268066,sausage,cat,Yes,early bird,Maybe
+LEC004,19,Business: Finance,53706,41.878113,-87.629799,pepperoni,dog,No,early bird,No
+LEC005,18,Business: Finance,53706,40.416775,-3.70379,pepperoni,dog,Yes,early bird,No
+LEC001,20,Science: Other|Environmental Science,53715,41.878113,-87.629799,green pepper,cat,No,early bird,No
+LEC002,22,Computer Science,53715,42,-71,mushroom,cat,No,night owl,Maybe
+LEC001,24,Economics,53703,40,-90,pineapple,dog,No,night owl,Yes
+LEC006,19,Business: Information Systems,53715,40.712776,-74.005974,basil/spinach,dog,No,night owl,Yes
+LEC002,19,Data Science,53703,33.4942,89.4959,sausage,dog,No,night owl,Maybe
+LEC003,20,Engineering: Mechanical,53715,43.02833,-87.971467,pepperoni,neither,Yes,night owl,Maybe
+LEC001,,Data Science,53706,40.416775,-3.70379,none (just cheese),dog,Yes,no preference,Yes
+LEC003,19,Engineering: Mechanical,53715,43.07,-89.4,pepperoni,dog,No,no preference,Maybe
+LEC006,18,Data Science,53706,46.683334,7.85,mushroom,dog,Yes,no preference,No
+LEC003,19,Engineering: Biomedical,53703,31.046051,34.851612,Other,dog,No,night owl,Maybe
+LEC003,18,Data Science,53705,31.23,121.47,mushroom,dog,Yes,night owl,Maybe
+LEC005,19,Engineering: Mechanical,53703,42.00741,-87.69384,mushroom,dog,No,night owl,Yes
+LEC001,37,Data Science,53718,43.073051,-89.40123,green pepper,dog,No,no preference,Maybe
+LEC003,20,History,53703,31.62,74.8765,Other,cat,Yes,early bird,No
+LEC002,20,Economics,53703,38.627003,-90.199402,mushroom,dog,Yes,night owl,Yes
+LEC005,20,Engineering: Mechanical,53703,40,-74,none (just cheese),dog,Yes,early bird,No
+LEC005,18,Data Science,53706,23.7275,37.9838,pepperoni,dog,Yes,early bird,Yes
+LEC004,20,Mathematics/AMEP,53703,34.746613,113.625328,sausage,neither,Yes,early bird,Maybe
+LEC001,21,Data Science,53703,30.572351,121.776761,pepperoni,cat,No,night owl,Maybe
+LEC005,,Data Science,53715,35.72,-78.89,pepperoni,dog,No,night owl,Yes
+LEC005,20,Information science,53590,44.92556,-89.51539,pepperoni,dog,No,night owl,Yes
+LEC002,22,Mathematics/AMEP,53704,40.76078,-111.891045,pineapple,dog,Yes,night owl,No
+LEC001,22,consumer behavior and marketplace studies,53715,43.653225,-79.383186,mushroom,cat,Yes,night owl,No
+LEC004,22,Computer Science,53703,10.315699,123.885437,sausage,dog,Yes,early bird,No
+LEC002,20,Conservation Biology,53703,40.16573,-105.101189,pineapple,dog,No,night owl,Yes
+LEC005,20,Computer Science,53726,39.4817,106.0384,Other,neither,Yes,early bird,Yes
+LEC005,19,Mathematics/AMEP,53715,48.85,2.35,sausage,cat,No,night owl,Maybe
+LEC005,19,Data Science,53706,30.572815,104.066803,mushroom,neither,No,early bird,Yes
+LEC004,24,Business: Information Systems,53703,37.566536,126.977966,tater tots,dog,No,early bird,No
+LEC004,19,Economics,53703,52.877491,-118.08239,pepperoni,dog,No,night owl,Yes
+LEC004,21,Computer Science,53703,28.538336,-81.379234,pepperoni,dog,No,night owl,Yes
+LEC006,18,Data Science,53706,41.4,-81.9,sausage,dog,Yes,night owl,Maybe
+LEC002,21,Science: Biology/Life,53703,43.038902,-87.906471,none (just cheese),neither,No,no preference,Yes
+LEC004,21,Data Science,53703,3.86,-54.2,macaroni/pasta,dog,No,early bird,No
+LEC004,19,Engineering: Mechanical,53715,39.952583,-75.165222,macaroni/pasta,dog,Yes,no preference,Yes
+LEC004,20,Science: Other,53715,21.3099,157.8581,pineapple,dog,No,early bird,Yes
+LEC005,21,Data Science,48823,11.451419,19.81,mushroom,neither,No,night owl,Maybe
+LEC001,20,Computer Science,53715,41,-87,Other,dog,No,night owl,Yes
+LEC005,21,Data Science,53705,42.3601,71.0589,pepperoni,dog,Yes,no preference,Yes
+LEC005,19,Computer Science,53706,48.856613,2.352222,pepperoni,dog,Yes,night owl,Maybe
+LEC001,17,Statistics,53715,43.0722,89.4008,pineapple,dog,No,early bird,Maybe
+LEC001,20,Economics,53715,27.99942,120.66682,pepperoni,dog,Yes,early bird,No
+LEC001,19,Mathematics/AMEP,53711,45.85038,-84.616989,pineapple,cat,No,night owl,Yes
+LEC004,20,Computer Science,53711,40.842358,111.749992,pineapple,cat,No,night owl,Maybe
+LEC003,18,Engineering: Mechanical,53706,39.738449,-104.984848,pepperoni,dog,No,early bird,Yes
+LEC003,21,Statistics,53705,41.878113,-87.629799,macaroni/pasta,dog,No,night owl,Yes
+LEC006,19,Engineering: Industrial,60540,41.878113,-87.629799,none (just cheese),dog,No,night owl,No
+LEC004,19,Engineering: Mechanical,53703,40.6263,14.3758,mushroom,dog,No,early bird,No
+LEC004,22,Engineering: Other|Chemical Engineering,53703,48.13913,11.58022,macaroni/pasta,dog,Yes,night owl,Yes
+LEC004,21,Economics (Mathematical Emphasis),53703,52.520008,13.404954,pepperoni,dog,No,night owl,No
+LEC004,25,Science: Other|Biophysics PhD,53705,30.21161,-97.80999,pineapple,dog,No,night owl,Yes
+LEC003,19,Computer Science,53716,25.49443,-103.59581,pepperoni,cat,No,no preference,Yes
+LEC003,19,Data Science,53706,64.963051,-19.020836,pineapple,dog,No,no preference,No
+LEC006,19,Computer Science,53706,41.878113,-87.629799,pepperoni,cat,No,night owl,Maybe
+LEC001,23,Economics,53703,43.07348,-89.38089,pepperoni,dog,No,night owl,Yes
+LEC001,29,Business: Other|Technology Strategy/ Product Management,53705,37.386051,-122.083855,Other,cat,No,no preference,Maybe
+LEC002,,Engineering: Mechanical,53706,14.34836,100.576271,pepperoni,neither,No,no preference,Maybe
+LEC004,20,Undecided,53715,37.566536,126.977966,none (just cheese),neither,No,night owl,Yes
+LEC006,19,Engineering: Mechanical,53703,27.993828,120.699364,sausage,neither,No,no preference,Yes
+LEC002,,Computer Science,53705,25.032969,121.565414,pineapple,dog,No,night owl,Yes
+LEC005,20,Mathematics/AMEP,53703,32.060253,118.796875,pineapple,cat,Yes,night owl,Maybe
+LEC003,,Business: Other,53706,50.07553,14.4378,pepperoni,dog,Yes,night owl,Maybe
+LEC006,21,Data Science,57303,32.715736,-117.161087,macaroni/pasta,cat,Yes,no preference,Yes
+LEC006,18,Engineering: Mechanical,53706,45.5579,94.1632,sausage,dog,No,night owl,Yes
+LEC001,18,Engineering: Biomedical,53715,43.073051,-89.40123,sausage,dog,No,early bird,Yes
+LEC005,19,Engineering: Mechanical,53706,38.571739,-109.550797,pepperoni,cat,No,night owl,Yes
+LEC003,18,Engineering: Mechanical,53706,41.902782,12.496365,pepperoni,dog,Yes,night owl,No
+LEC002,21,Data Science,53711,120,30,sausage,dog,Yes,night owl,Maybe
+LEC004,18,Engineering: Biomedical,53706,40.014984,-105.270546,green pepper,dog,No,night owl,Yes
+LEC004,20,Engineering: Mechanical,53715,53.2779,6.1058,sausage,dog,Yes,no preference,Yes
+LEC003,17,Science: Physics,53706,50.088153,14.399437,Other,cat,No,night owl,Yes
+LEC002,19,Engineering: Industrial,53705,35.084385,-106.650421,pineapple,cat,No,night owl,Yes
+LEC003,20,Engineering: Mechanical,53703,44.501343,-88.06221,pepperoni,dog,No,night owl,Yes
+LEC003,18,Engineering: Mechanical,53703,45.659302,-92.466164,macaroni/pasta,dog,No,no preference,Maybe
+LEC003,19,Data Science,53703,16.896721,42.5536,none (just cheese),neither,No,early bird,Maybe
+LEC001,18,Data Science,53703,23.885942,45.079163,mushroom,neither,No,early bird,Maybe
+LEC006,19,Engineering: Mechanical,53703,55.953251,-3.188267,mushroom,cat,Yes,night owl,Yes
+LEC001,30,Business: Other,53705,43.07175,-89.46498,pineapple,cat,No,early bird,No
+LEC006,18,Political Science,53706,39.640263,-106.374191,green pepper,dog,No,early bird,No
+LEC005,23,Business: Information Systems,53705,27.99,120.69,green pepper,dog,No,night owl,No
+LEC003,18,Graphic Design,53706,40.713051,-74.007233,Other,dog,Yes,early bird,Yes
+LEC002,21,Economics,53715,37.369171,-122.112473,mushroom,dog,No,night owl,No
+LEC005,18,Computer Science,53706,21.3099,157.8581,pepperoni,cat,No,night owl,Yes
+LEC002,19,Business: Other|Marketing,53706,59.913868,10.752245,macaroni/pasta,dog,No,night owl,Maybe
+LEC003,20,Cartography and GIS,53726,43.0722,89.4008,sausage,cat,No,early bird,Maybe
+LEC005,21,Economics,53705,25.032969,120.960518,sausage,dog,Yes,night owl,Maybe
+LEC005,19,Engineering: Industrial,53703,42.03992,87.67732,sausage,dog,Yes,night owl,Yes
+LEC003,,Computer Science,53706,35.443081,139.362488,sausage,dog,Yes,night owl,Yes
+LEC002,22,Sociology,53703,53.483959,-2.244644,pepperoni,dog,No,night owl,Yes
+LEC002,18,Undecided,53706,43.073051,-89.40123,pineapple,dog,Yes,night owl,Yes
+LEC004,19,Engineering: Biomedical,53706,-37.81,144.96,sausage,dog,Yes,night owl,Yes
+LEC005,21,Mathematics/AMEP,53703,22.542883,114.062996,pepperoni,cat,No,no preference,Maybe
+LEC002,20,Statistics,53715,23,113,pineapple,dog,No,night owl,Maybe
+LEC001,20,Business: Other|Consumer Behavior and Marketplace Studies,53703,40.76078,-111.891045,green pepper,dog,Yes,early bird,Maybe
+LEC001,21,Data Science,53705,40.712776,-74.005974,pepperoni,cat,No,night owl,Maybe
+LEC002,19,Engineering: Mechanical,53703,26.345631,-81.779083,pepperoni,dog,Yes,night owl,Yes
+LEC004,19,Engineering: Mechanical,53715,40.62632,14.37574,pepperoni,dog,No,no preference,Maybe
+LEC003,18,Engineering: Other,53706,40.73061,-73.9808,mushroom,dog,No,night owl,No
+LEC006,18,Atmospheric Sciences,53706,39.74,-104.99,sausage,dog,Yes,night owl,Maybe
+LEC002,20,Data Science,53703,43.073051,-89.40123,macaroni/pasta,dog,Yes,early bird,Yes
+LEC006,18,Engineering: Mechanical,53706,32.7157,117.1611,pineapple,dog,Yes,night owl,Yes
+LEC004,18,Computer Science,53706,51.507351,-0.127758,green pepper,dog,No,night owl,Yes
+LEC004,19,Education,53715,32.715736,-117.161087,pepperoni,dog,No,night owl,Yes
+LEC004,26,Languages,53703,50.11,8.68,sausage,dog,No,no preference,Yes
+LEC005,21,Economics (Mathematical Emphasis),53715,55.676098,12.568337,pepperoni,cat,No,night owl,Maybe
+LEC004,53,Mathematics/AMEP,53555,47.6,-122.3,mushroom,dog,No,night owl,Yes
+LEC004,17,Computer Science,53706,43.073051,-89.40123,Other,dog,No,night owl,Yes
+LEC006,18,Engineering Mechanics (Aerospace Engineering),53706,43.038902,-87.906471,pepperoni,cat,No,night owl,No
+LEC002,20,Engineering: Mechanical,53715,23.7157,117.1611,none (just cheese),cat,Yes,night owl,Maybe
+LEC002,22,Science: Other|Psychology,53703,37.82034,-122.47872,mushroom,dog,No,early bird,No
+LEC002,22,Computer Science,53705,34.052235,-118.243683,basil/spinach,dog,No,night owl,Yes
+LEC004,26,Science: Biology/Life,53715,33.962425,-83.378622,pineapple,neither,Yes,no preference,Yes
+LEC002,18,Economics,53715,41.878113,-87.629799,basil/spinach,cat,No,night owl,Maybe
+LEC004,24,Engineering: Other|Civil and Environmental Engineering,53703,47.5,19.04,pepperoni,dog,Yes,early bird,Maybe
+LEC004,19,Engineering: Biomedical,53711,40.712776,74.005974,pineapple,dog,No,early bird,No
+LEC001,19,Engineering: Mechanical,53715,43,-90,sausage,dog,No,no preference,Maybe
+LEC006,18,Data Science,94707,37.566536,126.977966,pineapple,dog,Yes,night owl,Yes
+LEC006,20,Undecided,53719,62.2001,58.9638,Other,cat,Yes,night owl,Maybe
+LEC002,18,Engineering: Mechanical,53706,44.977753,-93.265015,none (just cheese),cat,Yes,night owl,Yes
+LEC001,20,Business: Information Systems,53711,34.385204,132.455292,pepperoni,dog,No,early bird,Yes
+LEC005,19,Engineering: Biomedical,53703,41.8781,87.6298,macaroni/pasta,dog,No,night owl,No
+LEC002,19,Engineering: Biomedical,53703,37.98381,23.727539,macaroni/pasta,dog,No,night owl,Maybe
+LEC005,18,Data Science,53706,40,74,pepperoni,dog,No,no preference,Yes
+LEC002,19,Engineering: Mechanical,53711,41.95881,-85.32536,Other,dog,No,no preference,No
+LEC005,18,Data Science,53706,32.715736,-117.161087,sausage,dog,No,night owl,Maybe
+LEC002,18,Undecided,53706,43.060791,-88.119217,Other,neither,No,early bird,Yes
+LEC004,21,Science: Other,53715,27.963989,-82.799957,pineapple,dog,No,night owl,Yes
+LEC006,18,Data Science,53706,1.352083,103.819839,sausage,dog,No,night owl,Yes
+LEC005,19,Data Science,53703,-33.92487,18.424055,none (just cheese),dog,No,night owl,Yes
+LEC001,22,International Studies,53703,48.13913,11.58022,none (just cheese),cat,No,night owl,Yes
+LEC001,19,Engineering: Other,53715,38.331581,-75.086159,macaroni/pasta,dog,No,no preference,Yes
+LEC002,19,Business: Information Systems,53715,44.5,-88,pepperoni,dog,No,night owl,Yes
+LEC002,19,Data Science,53705,21.59143,-158.01743,Other,dog,Yes,night owl,Yes
+LEC002,,Business: Finance,53593,45.813042,9.080931,Other,dog,No,early bird,Yes
+LEC003,21,Business: Information Systems,53703,43.612255,-110.705429,sausage,dog,Yes,no preference,No
+LEC001,21,Data Science,53703,41.00824,28.978359,pepperoni,cat,Yes,early bird,No
+LEC002,18,Engineering: Biomedical,53706,17.385044,78.486671,green pepper,dog,No,night owl,Yes
+LEC006,21,Political Science,53703,45.512,-122.658,sausage,dog,No,night owl,Yes
+LEC003,18,Engineering: Mechanical,53706,41.902782,12.496365,pepperoni,dog,No,early bird,Maybe
+LEC005,19,Engineering: Mechanical,53703,-36.848461,174.763336,none (just cheese),dog,Yes,no preference,No
+LEC002,,Data Science,53713,30.316496,78.032188,mushroom,cat,Yes,night owl,Yes
+LEC002,,Business: Information Systems,53703,35.689487,139.691711,sausage,dog,Yes,night owl,Maybe
+LEC005,18,Data Science,53706,52.520008,13.404954,pineapple,dog,Yes,early bird,No
+LEC005,19,Computer Science,53706,41.3784,2.1686,sausage,cat,No,no preference,Yes
+LEC003,20,Engineering: Mechanical,53715,41.878113,-87.629799,Other,cat,No,night owl,Yes
+LEC004,20,Computer Science,53703,43.073051,-89.40123,none (just cheese),cat,Yes,night owl,Yes
+LEC006,23,Data Science,53703,17.05423,-96.713226,basil/spinach,dog,No,night owl,Maybe
+LEC001,19,Engineering: Mechanical,53706,43.77195,-88.43383,pepperoni,dog,No,early bird,Maybe
+LEC001,20,Economics,53726,42.92,-87.96,pepperoni,dog,Yes,early bird,No
+LEC001,19,Engineering: Mechanical,53715,29.424122,-98.493629,mushroom,dog,Yes,early bird,Maybe
+LEC004,18,Computer Science,53706,30.267153,-97.743057,pepperoni,dog,No,night owl,Yes
+LEC005,,Computer Science,53715,44.9778,93.265,sausage,cat,Yes,night owl,Yes
+LEC003,19,Science: Other,53715,41.9028,12.4964,pepperoni,dog,No,night owl,Yes
+LEC004,19,Data Science,53715,61.2176,149.8997,pineapple,cat,Yes,night owl,Maybe
+LEC001,20,Agricultural and Applied Economics,53703,-22.932924,-47.073845,pineapple,cat,Yes,early bird,Maybe
+LEC003,18,Computer Science,53706,52.370216,4.895168,basil/spinach,cat,No,night owl,Maybe
+LEC003,19,Engineering: Industrial,53703,5.838715,3.603516,pepperoni,dog,Yes,early bird,No
+LEC005,19,Engineering: Mechanical,53715,48.502281,-113.988533,sausage,dog,No,night owl,Yes
+LEC004,41,Languages,53705,29.654839,91.140549,pepperoni,cat,No,night owl,Yes
+LEC002,21,Business: Other|MHR,53703,44,125,Other,neither,No,night owl,Maybe
+LEC005,24,Business: Other,53703,43.073051,-89.40123,pineapple,dog,No,night owl,Yes
+LEC002,18,Undecided,53706,46.786671,-92.100487,none (just cheese),cat,No,no preference,Yes
+LEC004,18,Engineering: Biomedical,53705,35.689487,139.691711,basil/spinach,dog,No,night owl,Yes
+LEC001,25,Medicine,53703,48.38203,-123.537827,basil/spinach,dog,Yes,early bird,No
+LEC004,19,Science: Biology/Life,53705,46.009991,-91.482094,pineapple,dog,No,early bird,No
+LEC005,21,Science: Other|Personal Finance,53703,28.228209,112.938812,pepperoni,cat,Yes,night owl,Yes
+LEC004,18,Data Science,53706,35.689487,139.691711,pepperoni,dog,No,night owl,Maybe
+LEC006,21,Mathematics/AMEP,53703,41.878113,-87.629799,pineapple,cat,Yes,night owl,Maybe
+LEC005,18,Environmental science,53706,31.224361,121.46917,mushroom,dog,No,night owl,Yes
+LEC005,18,Engineering: Industrial,53706,40.712776,-74.005974,pepperoni,dog,Yes,night owl,Yes
+LEC001,20,Business: Other|Real Estate,53703,51.5,0.128,mushroom,dog,Yes,no preference,Maybe
+LEC001,19,Computer Science,53706,40,-74,pepperoni,cat,No,night owl,Yes
+LEC003,19,Engineering: Mechanical,53715,44,-94,pineapple,dog,No,early bird,No
+LEC001,19,Data Science,53715,40.712776,-74.005974,pepperoni,dog,No,early bird,No
+LEC005,18,Engineering: Industrial,53703,41.385063,2.173404,pepperoni,dog,Yes,no preference,Yes
+LEC002,20,Engineering: Industrial,53715,22.3,91.8,sausage,cat,Yes,early bird,Maybe
+LEC001,24,Engineering: Industrial,53705,13.100485,77.594009,none (just cheese),dog,Yes,no preference,Maybe
+LEC004,19,Statistics,53706,36.778259,-119.417931,pineapple,cat,No,night owl,Yes
+LEC005,21,Economics,53703,40.016869,-105.279617,pepperoni,cat,Yes,night owl,Yes
+LEC003,19,Economics (Mathematical Emphasis),53705,31.230391,121.473701,sausage,neither,Yes,no preference,Maybe
+LEC003,19,Business: Finance,53706,22.270979,113.576675,pepperoni,dog,Yes,night owl,Yes
+LEC003,21,Computer Science,53705,43.073051,-89.40123,green pepper,cat,No,no preference,Maybe
+LEC001,28,Science: Biology/Life,53703,7.190708,125.455338,sausage,dog,No,night owl,Yes
+LEC004,18,Statistics,53703,60.472023,8.468946,none (just cheese),dog,No,early bird,No
+LEC002,19,Computer Science,53715,41.73993,-88.09423,mushroom,cat,Yes,no preference,Yes
+LEC002,21,Economics,53703,26.074301,119.296539,mushroom,cat,No,no preference,Maybe
+LEC002,20,Engineering: Industrial,53715,2.188477,41.379179,sausage,dog,No,night owl,Yes
+LEC003,21,Science: Other|Environmental Science,53703,20.8,-156.3,basil/spinach,cat,No,early bird,Maybe
+LEC006,18,Engineering: Mechanical,53706,25.204849,55.270782,pepperoni,dog,No,night owl,Yes
+LEC002,18,Data Science,53706,42.360081,-71.058884,sausage,dog,Yes,night owl,Yes
+LEC004,23,Engineering: Mechanical,53703,38.82097,-104.78163,sausage,dog,No,night owl,No
+LEC001,19,Engineering: Industrial,53715,47.606209,-122.332069,pepperoni,cat,No,night owl,No
+LEC006,19,Sociology,53703,43.05977,-87.88491,basil/spinach,dog,No,night owl,Maybe
+LEC005,19,Engineering: Mechanical,53711,38.8951,-77.0364,pepperoni,dog,Yes,night owl,No
+LEC005,19,Engineering: Mechanical,53703,41.881832,87.6298,pepperoni,dog,No,no preference,Yes
+LEC002,20,Engineering: Mechanical,53703,46.453825,7.436478,pineapple,dog,Yes,night owl,Yes
+LEC002,20,Economics,53703,30.49996,117.050003,Other,dog,No,early bird,Maybe
+LEC004,21,Science: Other|Psychology,53715,23.12911,113.264381,none (just cheese),cat,No,night owl,Maybe
+LEC002,18,Science: Biology/Life,53706,40.7831,73.9712,basil/spinach,dog,Yes,night owl,Yes
+LEC002,,Business: Information Systems,53706,18.52043,73.856743,green pepper,dog,No,night owl,Yes
+LEC002,,Computer Science,53706,29.424122,-98.493629,none (just cheese),dog,No,no preference,Yes
+LEC002,20,Engineering: Mechanical,53703,41.05995,-80.32312,basil/spinach,dog,Yes,night owl,Maybe
+LEC006,19,Statistics,53715,3.139003,101.686852,mushroom,cat,No,no preference,Maybe
+LEC005,18,Data Science,53706,52.370216,4.895168,basil/spinach,dog,No,night owl,Yes
+LEC006,19,Engineering: Industrial,53706,41.878113,-87.629799,pepperoni,dog,No,no preference,Maybe
+LEC006,18,Business: Information Systems,53706,25.032969,121.565414,mushroom,dog,Yes,night owl,Yes
+LEC001,17,Computer Science,53726,21.027763,105.83416,pepperoni,dog,No,early bird,Yes
+LEC001,20,Business: Information Systems,53711,45.046799,-87.298149,sausage,cat,No,night owl,Yes
+LEC005,25,Engineering: Other,53705,32.7157,-117.1611,mushroom,dog,No,no preference,Yes
+LEC004,18,Engineering: Industrial,53706,19.896767,-155.582779,pepperoni,dog,Yes,night owl,Maybe
+LEC005,18,Computer Science,53706,1.28217,103.865196,sausage,dog,No,night owl,Yes
+LEC003,18,Engineering: Mechanical,53706,44.977753,-93.265015,pepperoni,dog,No,night owl,Yes
+LEC004,20,Engineering: Mechanical,53715,23,90,green pepper,cat,No,no preference,Yes
+LEC005,20,Data Science,53703,45.259546,-84.938476,mushroom,dog,Yes,night owl,Yes
+LEC002,21,Science: Other,53703,41.878113,-87.629799,pineapple,dog,Yes,early bird,No
+LEC004,19,Information science,53703,40.712776,-74.005974,pineapple,cat,Yes,early bird,Maybe
+LEC001,19,Engineering: Mechanical,53715,64.126518,-21.817438,pepperoni,dog,No,night owl,Yes
+LEC003,,Business: Other,53706,42.360081,-71.058884,sausage,cat,Yes,night owl,No
+LEC002,31,Geoscience,53703,-41.126621,-73.059303,pepperoni,cat,No,night owl,Yes
+LEC003,18,Engineering: Biomedical,53706,45.17099,-87.16494,Other,dog,No,night owl,Maybe
+LEC002,18,Engineering: Mechanical,53706,37.774929,-122.419418,Other,dog,Yes,no preference,Yes
+LEC004,,Computer Science,53715,39.70698,-86.0862,mushroom,cat,No,night owl,Yes
+LEC005,20,Science: Biology/Life,53703,44.276402,-88.26989,macaroni/pasta,cat,No,no preference,Maybe
+LEC002,19,Science: Biology/Life,53703,51.492519,-0.25852,sausage,dog,Yes,no preference,Yes
+LEC002,19,Data Science,53703,37.6,14.0154,none (just cheese),dog,No,night owl,Yes
+LEC002,20,Engineering: Industrial,53715,46.685631,7.8562,Other,cat,No,night owl,Maybe
+LEC002,22,Economics,53706,41.385063,2.173404,pineapple,cat,No,night owl,Maybe
+LEC004,21,Engineering: Industrial,53703,41.878113,-87.629799,pepperoni,neither,Yes,early bird,No
+LEC004,19,Engineering: Mechanical,53703,51.507351,-0.127758,none (just cheese),neither,No,no preference,Maybe
+LEC006,18,Engineering: Mechanical,53706,41.077747,1.131593,sausage,dog,No,no preference,Maybe
+LEC006,18,Engineering: Mechanical,53706,43.526,5.445,basil/spinach,dog,Yes,no preference,Yes
+LEC003,22,Economics,53715,43.073051,-89.40123,pepperoni,dog,Yes,early bird,Yes
+LEC005,18,Engineering: Industrial,53706,43.085369,-88.912086,sausage,dog,No,night owl,Maybe
+LEC002,19,Statistics,53703,43.769562,11.255814,basil/spinach,dog,No,no preference,Yes
+LEC001,20,Computer Science,53715,20.880947,-156.681862,sausage,dog,No,night owl,Yes
+LEC003,19,Mathematics/AMEP,53703,64.963051,-19.020836,basil/spinach,dog,No,no preference,Yes
+LEC005,18,Undecided,53706,43.073929,-89.385239,sausage,dog,Yes,early bird,Yes
+LEC003,18,Business: Information Systems,53706,25.204849,55.270782,none (just cheese),dog,No,night owl,No
+LEC003,21,Economics,53703,39.904,116.407,pepperoni,cat,No,night owl,No
+LEC004,18,Engineering: Mechanical,53706,39.739235,-104.99025,pepperoni,cat,Yes,no preference,Maybe
+LEC004,21,Science: Biology/Life,53726,43,89,pepperoni,dog,Yes,night owl,Yes
+LEC003,19,Data Science,53715,43.073051,-89.40123,none (just cheese),dog,No,early bird,Maybe
+LEC002,19,Business: Other|accounting,53703,43.38,-87.9,sausage,neither,No,night owl,Yes
+LEC002,18,Science: Biology/Life,53706,40.122,25.4988,sausage,dog,No,early bird,No
+LEC005,20,Engineering: Mechanical,53715,39.904202,116.407394,sausage,dog,No,night owl,Yes
+LEC001,19,Engineering: Mechanical,53703,-37.813629,144.963058,sausage,dog,Yes,night owl,Yes
+LEC005,21,Economics,53715,46.81,-71.21,pepperoni,cat,No,night owl,Yes
+LEC004,19,Engineering: Mechanical,53715,52.370216,4.895168,mushroom,dog,Yes,night owl,Yes
+LEC001,21,Mathematics/AMEP,53703,34.29006,108.932941,basil/spinach,dog,No,early bird,Yes
+LEC005,21,Engineering: Mechanical,53726,43.804801,-91.226075,pepperoni,dog,Yes,night owl,Yes
+LEC002,18,Data Science,53703,32.715736,-117.161087,none (just cheese),cat,Yes,night owl,Maybe
+LEC004,18,Engineering: Mechanical,53706,20.92674,-156.69386,pepperoni,dog,No,night owl,Maybe
+LEC003,18,Data Science,53706,47.606209,-122.332069,pepperoni,dog,No,early bird,Yes
+LEC005,21,Computer Science,53703,43.07515,-89.3958,sausage,neither,Yes,night owl,Yes
+LEC001,19,Engineering: Mechanical,53562,43.096851,-89.511528,sausage,dog,No,night owl,No
+LEC003,19,Engineering: Mechanical,53715,20.924325,-156.690102,sausage,cat,Yes,night owl,No
+LEC005,20,Data Science,53703,25.0838,77.3212,pepperoni,dog,No,night owl,Maybe
+LEC003,21,Business: Actuarial,53715,43.073051,-89.40123,pineapple,cat,Yes,night owl,Yes
+LEC001,,Computer Science,53715,31.469279,119.765621,pepperoni,dog,No,night owl,Maybe
+LEC005,19,Engineering: Mechanical,53715,43.769562,11.255814,basil/spinach,neither,No,early bird,No
+LEC001,21,Science: Chemistry,53715,38.892059,-77.019913,pepperoni,neither,No,night owl,Yes
+LEC002,19,Business: Finance,53715,42.360081,-71.058884,mushroom,dog,Yes,night owl,Yes
+LEC001,18,Data Science,53703,24.713552,46.675297,none (just cheese),neither,No,night owl,Yes
+LEC003,19,Business: Actuarial,53715,60.391262,5.322054,pepperoni,dog,No,early bird,No
+LEC003,19,Data Science,53715,23.697809,120.960518,pepperoni,cat,No,night owl,Yes
+LEC003,18,Data Science,53706,40.712776,74.005974,pineapple,dog,Yes,early bird,No
+LEC004,19,Engineering: Mechanical,53703,45.126887,-94.528067,sausage,dog,No,night owl,Maybe
+LEC002,21,Science: Biology/Life,53715,48.208176,16.373819,Other,dog,Yes,night owl,No
+LEC006,18,Engineering: Mechanical,53706,44.0628,-121.30451,pepperoni,dog,No,night owl,Yes
+LEC003,21,Statistics,53703,31.230391,121.473701,pineapple,cat,Yes,night owl,Yes
+LEC005,21,Economics,53703,47.62772,-122.51368,macaroni/pasta,cat,No,no preference,No
+LEC003,19,Engineering: Mechanical,53715,65.68204,-18.090534,sausage,cat,No,no preference,No
+LEC004,21,Economics,53715,48.856613,2.352222,basil/spinach,dog,Yes,night owl,No
+LEC001,18,Engineering: Biomedical,53706,33.501324,-111.925278,pineapple,dog,Yes,early bird,No
+LEC005,18,Data Science,53706,14.77046,-91.183189,mushroom,cat,No,night owl,Maybe
+LEC002,18,Engineering: Industrial,53706,10.480594,-66.903603,mushroom,neither,No,night owl,Maybe
+LEC004,21,Engineering: Mechanical,53715,48.856613,2.352222,mushroom,cat,Yes,night owl,Yes
+LEC001,19,Science: Biology/Life,53706,20.788602,-156.003662,green pepper,dog,Yes,no preference,No
+LEC006,18,Data Science,53706,36.59239,-121.86875,pepperoni,cat,No,night owl,Maybe
+LEC002,,Engineering: Industrial,53705,47.6,-122.33,sausage,dog,No,early bird,No
+LEC001,18,Engineering: Mechanical,53703,23.885942,45.079163,Other,cat,No,night owl,Maybe
+LEC002,18,Engineering: Industrial,53532,47.606209,-122.332069,mushroom,dog,No,night owl,Maybe
+LEC002,17,Engineering: Biomedical,53706,39.5755,-106.100403,pepperoni,dog,Yes,night owl,Maybe
+LEC002,20,Data Science,53711,39.904202,116.407394,pepperoni,dog,No,night owl,Yes
+LEC001,19,Engineering: Industrial,53705,41.878113,-87.629799,tater tots,cat,No,night owl,No
+LEC004,19,Political Science,53703,55.679626,12.581921,pepperoni,dog,Yes,no preference,Maybe
+LEC005,18,Computer Science,53715,28.538336,-81.379234,pepperoni,dog,No,night owl,Maybe
+LEC004,29,Engineering: Mechanical,53704,50.064651,19.944981,sausage,dog,No,early bird,Maybe
+LEC005,18,Engineering: Other,53706,41.385063,2.173404,mushroom,cat,No,night owl,Yes
+LEC001,19,Engineering: Mechanical,53703,44.977753,-93.265015,Other,cat,Yes,early bird,No
+LEC001,32,Design Studies,53705,48.856613,2.352222,mushroom,dog,No,early bird,Yes
+LEC002,20,Engineering: Mechanical,53703,41.28347,-70.099449,pepperoni,dog,Yes,night owl,Yes
+LEC003,19,Engineering: Industrial,53715,41.73849,-71.30418,pepperoni,dog,No,night owl,Yes
+LEC001,18,Data Science,53706,43.073051,-89.40123,sausage,dog,No,early bird,Yes
+LEC001,19,Computer Science,53715,31.230391,121.473701,pineapple,cat,No,night owl,Yes
+LEC001,19,Data Science,53703,37.9838,23.7275,sausage,dog,Yes,no preference,Yes
+LEC005,20,Engineering: Biomedical,53703,47.497913,19.040236,Other,cat,Yes,night owl,No
+LEC004,18,Economics,53711,13.756331,100.501762,Other,dog,No,night owl,Maybe
+LEC002,18,Data Science,53706,3.864255,73.388672,pepperoni,dog,Yes,night owl,Maybe
+LEC006,18,Engineering: Mechanical,53706,32.715736,-117.161087,macaroni/pasta,dog,Yes,night owl,Yes
+LEC001,19,Business: Actuarial,53715,18.32431,64.941612,pepperoni,dog,No,no preference,Yes
+LEC001,22,Psychology,53711,43.055333,-89.425946,pineapple,dog,Yes,early bird,No
+LEC003,18,Computer Science,53706,40.744678,-73.758072,mushroom,cat,No,night owl,Maybe
+LEC006,18,Data Science,53715,38.9784,76.4922,mushroom,cat,No,early bird,Yes
+LEC004,20,Science: Other,53726,55.675758,12.56902,none (just cheese),cat,Yes,night owl,Yes
+LEC001,20,Science: Biology/Life,53715,40.713051,-74.007233,pineapple,cat,No,night owl,Maybe
+LEC004,18,Engineering: Industrial,53706,51.507351,-0.127758,pepperoni,dog,Yes,no preference,No
+LEC004,25,Computer Science,53703,38.736946,-9.142685,pepperoni,dog,No,night owl,Yes
+LEC002,18,Computer Science,53706,22.543097,114.057861,pepperoni,cat,No,no preference,Yes
+LEC004,25,Science: Chemistry,53703,37.566536,126.977966,Other,cat,Yes,night owl,Maybe
+LEC002,19,Engineering: Mechanical,53715,26.338,-81.775,pepperoni,dog,Yes,no preference,Maybe
+LEC005,19,Engineering: Mechanical,53715,33.448376,-112.074036,pepperoni,neither,Yes,early bird,No
+LEC005,19,Engineering: Mechanical,53703,43.073051,-89.40123,pepperoni,cat,No,no preference,Yes
+LEC001,19,Engineering: Mechanical,53705,26.647661,106.63015,mushroom,cat,No,night owl,No
+LEC003,18,Undecided,53706,43.2967,87.9876,pepperoni,dog,No,night owl,No
+LEC005,19,Science: Physics,53703,78.225,15.626,sausage,cat,No,early bird,No
+LEC002,,Science: Other|Environmetal Science,53703,52.973558,-9.425102,none (just cheese),dog,Yes,night owl,Maybe
+LEC006,19,Economics (Mathematical Emphasis),53715,37.774929,-122.419418,sausage,cat,Yes,night owl,Yes
+LEC002,20,Business: Finance,53703,40.7128,74.006,pineapple,dog,No,night owl,Yes
+LEC001,21,Science: Biology/Life,53703,44.794,-93.148,pepperoni,dog,No,night owl,No
+LEC002,19,Engineering: Mechanical,53706,36.17,-115.14,pepperoni,cat,No,night owl,Maybe
+LEC001,18,Engineering: Biomedical,53706,21.161907,-86.851524,none (just cheese),dog,No,early bird,Maybe
+LEC001,18,Computer Science,53715,48.856613,2.352222,pineapple,neither,Yes,no preference,No
+LEC004,19,Engineering: Mechanical,53715,48.137,11.576,green pepper,dog,No,early bird,No
+LEC001,20,Engineering: Biomedical,53703,43.07393,-89.38524,sausage,dog,No,night owl,Maybe
+LEC002,18,Science: Other,53706,35.6762,139.6503,Other,dog,No,no preference,Yes
+LEC004,19,Computer Science,53703,41.902782,12.496365,none (just cheese),neither,Yes,night owl,No
+LEC001,20,Science: Other|Atmospheric and Oceanic Sciences (AOS),53711,49.299171,19.94902,pepperoni,dog,No,night owl,Maybe
+LEC002,18,Data Science,53706,41.380898,2.12282,pepperoni,dog,No,night owl,Maybe
+LEC006,18,Data Science,53706,48.257919,4.03073,mushroom,cat,Yes,early bird,No
+LEC005,19,Engineering: Mechanical,53715,35.0844,106.6504,pineapple,dog,Yes,early bird,Yes
+LEC002,23,Economics,53703,121,5,pepperoni,neither,No,no preference,Maybe
+LEC004,18,Business: Actuarial,53706,21.306944,-157.858337,pineapple,dog,Yes,night owl,Maybe
+LEC005,18,Economics,53706,43,-87.9,pepperoni,dog,Yes,early bird,Maybe
+LEC005,23,Business: Other|Business Analytics,53703,31.230391,121.473701,pineapple,cat,Yes,night owl,Maybe
+LEC002,22,Psychology,53703,25.032969,121.565414,mushroom,dog,No,no preference,Yes
+LEC005,18,Computer Science,53706,43.0722,89.4008,sausage,cat,No,night owl,Yes
+LEC006,18,Data Science,53706,52.370216,4.895168,mushroom,dog,Yes,night owl,Maybe
+LEC004,20,Data Science,53703,35.726212,-83.491226,pepperoni,cat,No,early bird,Yes
+LEC001,18,Computer Science,53703,27,153,mushroom,cat,No,early bird,Yes
+LEC005,18,Data Science,53706,56.117017,-3.879547,pineapple,dog,Yes,night owl,Yes
+LEC001,20,Engineering: Biomedical,53715,45.983964,9.262161,sausage,dog,No,night owl,No
+LEC005,21,Psychology,53703,43.038902,-87.906471,macaroni/pasta,dog,Yes,night owl,Yes
+LEC002,18,Engineering: Mechanical,53706,41.38879,2.15084,sausage,dog,Yes,no preference,Maybe
+LEC003,18,Data Science,53706,47.48,-122.28,basil/spinach,dog,No,no preference,Maybe
+LEC004,21,Data Science,53703,34.746613,113.625328,green pepper,neither,Yes,no preference,No
+LEC005,21,Data Science,53703,38.240946,-85.757571,pepperoni,dog,No,no preference,Yes
+LEC005,19,Engineering: Mechanical,53703,43.07291,-89.39439,sausage,dog,No,night owl,Maybe
+LEC005,19,Engineering: Mechanical,53715,56.373482,-3.84306,none (just cheese),dog,No,early bird,Yes
+LEC005,19,Data Science,53703,41.381717,2.177925,pepperoni,dog,Yes,night owl,Yes
+LEC005,19,Engineering: Mechanical,53714,43.089199,87.8876,pepperoni,dog,No,night owl,Yes
+LEC005,19,Engineering: Other,53590,38.4,11.2,pepperoni,dog,Yes,early bird,No
+LEC005,19,Engineering: Mechanical,53715,25.761681,-80.191788,pepperoni,dog,Yes,night owl,No
+LEC005,19,Engineering: Mechanical,53703,44.5133,88.0133,mushroom,dog,Yes,night owl,Maybe
+LEC002,,Computer Science,53706,41.8781,87.6298,pepperoni,dog,No,night owl,Maybe
+LEC005,19,Business: Finance,53703,38.98378,-77.20871,none (just cheese),dog,Yes,night owl,Yes
+LEC005,18,Business: Finance,53703,22.9068,43.1729,pepperoni,dog,No,night owl,Yes
+LEC005,19,Engineering: Mechanical,53715,43.073051,-89.40123,pepperoni,dog,No,early bird,No
+LEC004,23,Economics,53703,43.083321,-89.372475,mushroom,dog,Yes,early bird,No
+LEC002,17,Business: Actuarial,53715,34.746613,113.625328,sausage,neither,Yes,night owl,Maybe
+LEC005,18,Engineering: Biomedical,53715,46.58276,7.08058,pepperoni,dog,No,early bird,No
+LEC001,20,Statistics,53715,39.904202,116.407394,mushroom,dog,Yes,early bird,No
+LEC002,18,Computer Science,53706,35.96691,-75.627823,sausage,dog,No,early bird,Yes
+LEC005,21,Mathematics/AMEP,53703,13.756331,100.501762,pepperoni,dog,No,night owl,Yes
+LEC005,20,Engineering: Biomedical,53715,28.538336,-81.379234,sausage,cat,No,night owl,Maybe
+LEC002,19,Engineering: Mechanical,53703,44.822783,-93.370743,sausage,dog,Yes,early bird,No
+LEC005,19,Engineering: Mechanical,53715,42.15,-87.96,pepperoni,dog,No,night owl,Yes
+LEC005,20,Journalism,53715,41.3874,2.1686,basil/spinach,dog,Yes,early bird,Maybe
+LEC001,19,Engineering: Mechanical,53703,42.864552,-88.333199,pepperoni,dog,No,early bird,Maybe
+LEC005,17,Data Science,53706,40.7128,74.006,macaroni/pasta,dog,No,night owl,Yes
+LEC005,19,Science: Other|Politcal Science,53703,41.878113,-87.629799,pepperoni,dog,Yes,night owl,No
+LEC002,20,Business: Finance,53703,40.7831,73.9712,sausage,dog,Yes,night owl,No
+LEC004,20,Data Science,53703,43,87.9,none (just cheese),dog,No,night owl,Yes
+LEC001,18,Data Science,53706,38.900497,-77.007507,pineapple,dog,No,night owl,Maybe
+LEC005,18,Engineering: Industrial,53706,45.440845,12.315515,sausage,dog,No,night owl,Maybe
+LEC002,19,Data Science,53715,25.73403,-80.24697,pepperoni,dog,Yes,night owl,Yes
+LEC005,18,Political Science,53706,42.360081,-71.058884,macaroni/pasta,dog,Yes,night owl,Yes
+LEC002,20,Economics,53703,41.878113,-87.629799,pepperoni,dog,Yes,no preference,Maybe
+LEC004,18,Engineering: Mechanical,55088,48.135124,11.581981,pepperoni,dog,Yes,no preference,No
+LEC002,23,Business: Information Systems,53703,37.566536,126.977966,sausage,dog,No,night owl,Maybe
+LEC005,17,Data Science,53703,49.2827,123.1207,sausage,dog,Yes,night owl,Yes
+LEC005,,Statistics,53726,40.712776,-74.005974,Other,dog,Yes,no preference,Yes
+LEC001,18,Science: Biology/Life,53706,48.856613,2.352222,pepperoni,cat,Yes,early bird,No
+LEC005,32,Communication Sciences and Disorder,53705,37.566536,126.977966,pineapple,dog,Yes,no preference,Yes
+LEC001,18,Data Science,53706,41.878113,-87.629799,macaroni/pasta,dog,No,night owl,Yes
+LEC002,17,Business: Information Systems,53706,-6.17511,106.865036,sausage,neither,No,no preference,Maybe
+LEC002,25,Science: Other|Geoscience,53711,46.947975,7.447447,mushroom,cat,No,no preference,Yes
+LEC002,20,Economics,53703,46.7867,92.1005,macaroni/pasta,neither,Yes,early bird,No
+LEC002,21,Business: Other|Marketing,53703,20.878332,-156.682495,basil/spinach,dog,No,night owl,Yes
+LEC001,19,Statistics,53703,52.370216,4.895168,sausage,dog,No,night owl,Maybe
+LEC005,20,Engineering: Biomedical,53711,35.689487,139.691711,basil/spinach,dog,No,night owl,Yes
+LEC005,22,Science: Other|Atmospheric and oceanic science,53703,26.1224,80.1373,pepperoni,dog,No,early bird,No
+LEC001,18,Engineering: Mechanical,53726,21.306944,-157.858337,sausage,dog,No,night owl,Yes
+LEC005,21,Business: Finance,53703,43.11339,-89.37726,sausage,dog,No,night owl,Yes
+LEC001,,Business: Other,53703,22.396427,114.109497,Other,dog,No,early bird,Maybe
+LEC004,19,Science: Biology/Life,53706,41.2,96,pepperoni,cat,No,early bird,No
+LEC004,18,Engineering: Industrial,53706,49.74609,7.4609,pepperoni,cat,No,early bird,Yes
+LEC004,20,Science: Other|Environmental Science,53715,43,-89,mushroom,dog,Yes,night owl,Maybe
+LEC001,18,Business: Finance,53706,39.7392,104.9903,pepperoni,dog,No,early bird,No
+LEC002,,Computer Science,53706,41.67566,-86.28645,pineapple,cat,No,no preference,Maybe
+LEC002,18,Business: Other,53706,33.88509,-118.409714,green pepper,dog,Yes,night owl,No
+LEC001,20,Engineering: Biomedical,53711,41.8781,87.6298,pepperoni,dog,No,night owl,Yes
+LEC002,20,Data Science,53715,10.97285,106.477707,mushroom,dog,No,no preference,Maybe
+LEC002,20,Computer Science,53703,36.16156,-75.752441,pepperoni,dog,Yes,no preference,Yes
+LEC002,20,Business: Other|Marketing,53703,35.689487,139.691711,pepperoni,dog,Yes,night owl,Yes
+LEC002,18,Engineering: Other|Engineering Mechanics,53706,35.689487,139.691711,mushroom,cat,No,night owl,Maybe
+LEC002,21,Economics (Mathematical Emphasis),53703,46.25872,-91.745583,sausage,dog,Yes,no preference,Yes
+LEC002,19,Mathematics,53703,39.904202,116.407394,tater tots,cat,No,night owl,Yes
+LEC002,18,Data Science,53703,40.706067,-74.030063,pepperoni,dog,No,night owl,Yes
+LEC002,19,Pre-Business,53703,39.60502,-106.51641,pepperoni,dog,Yes,early bird,No
+LEC002,20,Mathematics/AMEP,53703,35.106766,-106.629181,green pepper,cat,No,night owl,Yes
+LEC003,20,Science: Physics,53715,64.963051,-19.020836,mushroom,dog,No,night owl,Yes
+LEC002,20,Business: Finance,53703,31.298973,120.585289,pineapple,cat,Yes,night owl,No
+LEC002,18,Economics,53706,48.856613,2.352222,basil/spinach,dog,No,night owl,Maybe
+LEC001,21,Data Science,53703,40.712776,-74.005974,sausage,dog,No,night owl,Yes
+LEC002,19,Engineering: Industrial,53715,45.914,-89.255,sausage,dog,Yes,early bird,Yes
+LEC002,19,Computer Science,53703,20,110,pineapple,cat,No,night owl,Maybe
+LEC002,19,Engineering: Mechanical,53726,41.878113,-87.629799,basil/spinach,dog,No,early bird,Yes
+LEC005,19,Computer Science,53715,48.8566,2.3522,sausage,dog,No,night owl,Maybe
+LEC002,19,Industrial Engineering,53703,48.856613,2.352222,basil/spinach,dog,No,early bird,Yes
+LEC002,18,Data Science,53706,43.073051,-89.40123,pepperoni,dog,Yes,night owl,Yes
+LEC002,20,Statistics,53703,31.224361,121.46917,mushroom,dog,No,no preference,Maybe
+LEC002,18,Computer Science,53706,35.689487,139.691711,green pepper,dog,No,night owl,Yes
+LEC002,18,Computer Science,53706,25.03841,121.563698,pineapple,dog,No,night owl,Yes
+LEC002,19,Engineering: Mechanical,53715,43.06827,-89.40263,sausage,dog,No,night owl,No
+LEC002,18,Engineering: Mechanical,53703,43,89.4,pepperoni,cat,No,no preference,Maybe
+LEC002,,Mechanical Engineering,53703,41.8781,87.6298,Other,dog,Yes,night owl,Yes
+LEC002,26,Science: Other,57075,42.76093,-89.9589,Other,dog,Yes,early bird,No
+LEC002,21,Science: Other|Environmental science,53714,47.606209,-122.332069,pepperoni,dog,Yes,early bird,Yes
+LEC002,18,Data Science,53706,35.69,139.69,pineapple,cat,No,night owl,Yes
+LEC002,18,Computer Science,53706,42.807091,-86.01886,none (just cheese),cat,Yes,early bird,Yes
+LEC002,19,Engineering: Mechanical,53703,45.892099,8.997803,green pepper,dog,No,night owl,Yes
+LEC002,20,Computer Science,53715,40.755645,-74.034119,sausage,dog,Yes,night owl,Yes
+LEC001,18,Engineering: Mechanical,53066,43.073051,-89.40123,pepperoni,dog,No,night owl,Yes
+LEC002,18,Data Science,53706,21.306944,-157.858337,pineapple,dog,No,night owl,No
+LEC002,18,Engineering: Industrial,53706,32.0853,34.781769,pepperoni,dog,No,night owl,Maybe
+LEC002,19,Engineering: Mechanical,53703,46.786671,-92.100487,sausage,dog,No,early bird,No
+LEC002,19,Engineering: Mechanical,53715,42.590519,-88.435287,pepperoni,dog,No,early bird,No
+LEC002,23,Data Science,53703,37,127,pineapple,dog,No,night owl,Yes
+LEC002,20,Data Science,53703,43.06875,-89.39434,pepperoni,dog,Yes,no preference,Maybe
+LEC002,20,Engineering: Mechanical,53703,41.499321,-81.694359,pepperoni,dog,Yes,night owl,Maybe
+LEC002,21,Economics,53703,38.969021,-0.18516,sausage,dog,Yes,no preference,No
+LEC002,20,Economics,53703,50.85,4.35,pepperoni,dog,No,no preference,Yes
+LEC002,19,Data Science,53715,36.39619,10.61412,none (just cheese),cat,No,no preference,Yes
+LEC002,20,Engineering: Mechanical,53711,43.073051,-89.40123,green pepper,dog,Yes,night owl,No
+LEC002,30,Life Sciences Communication,53562,52.399448,0.25979,basil/spinach,cat,Yes,night owl,Yes
+LEC002,20,Business: Finance,53703,41.878,-87.629799,pepperoni,dog,No,no preference,Yes
+LEC002,18,Computer Science,53706,31.2304,121.4737,pepperoni,cat,No,night owl,Maybe
+LEC005,22,Economics,53711,48.135124,11.581981,pepperoni,cat,Yes,no preference,Yes
+LEC002,19,Engineering: Mechanical,53711,51.5,0.1276,pepperoni,dog,No,night owl,No
+LEC001,18,Computer Science,53703,31.298973,120.585289,pineapple,neither,No,night owl,No
+LEC001,19,Computer Science,53703,37,-97,macaroni/pasta,cat,No,no preference,Maybe
+LEC002,19,International Studies,53703,8.25115,34.588348,none (just cheese),dog,Yes,early bird,Maybe
+LEC001,19,Engineering: Mechanical,53703,43.038902,-87.906471,pineapple,cat,No,night owl,Yes
+LEC001,19,Science: Other|Atmospheric and Oceanic Sciences,53703,48.856613,2.352222,pepperoni,dog,Yes,night owl,Yes
+LEC004,20,Data Science,53703,41.878113,-87.629799,green pepper,dog,No,early bird,Yes
+LEC004,18,Undecided,53706,39.3823,87.2971,sausage,dog,Yes,early bird,No
+LEC004,21,Data Science,53703,31.230391,121.473701,mushroom,cat,No,night owl,Maybe
+LEC001,18,Data Science,53706,32.776474,-79.931053,none (just cheese),dog,No,early bird,Yes
+LEC006,18,Science: Physics,53706,43.073051,-89.40123,sausage,dog,No,night owl,Yes
+LEC001,19,Economics,53703,35.689487,139.691711,pineapple,dog,Yes,night owl,Yes
+LEC004,18,Data Science,53715,50.8,-1.085,Other,dog,No,night owl,Maybe
+LEC002,21,Languages,53703,37.389091,-5.984459,mushroom,cat,No,early bird,No
+LEC001,19,Rehabilitation Psychology,53706,36.204823,138.25293,pineapple,cat,No,no preference,Maybe
+LEC006,18,Data Science,53705,37.5741,122.3794,pepperoni,dog,Yes,night owl,Yes
+LEC004,18,Undecided,53706,26.452,-81.9481,pepperoni,dog,Yes,night owl,Yes
+LEC002,19,Business: Actuarial,53703,37.774929,-122.419418,pineapple,dog,No,early bird,No
+LEC005,18,Undecided,53706,55.676098,12.568337,pepperoni,dog,Yes,night owl,No
+LEC001,19,Engineering: Mechanical,53703,43.073051,-89.40123,pepperoni,dog,Yes,night owl,Yes
+LEC002,18,Statistics,53706,40.713051,-74.007233,none (just cheese),dog,No,night owl,Maybe
+LEC003,21,Languages,53511,39.952583,-75.165222,pepperoni,dog,No,night owl,Yes
+LEC002,18,Computer Science,53706,12.523579,-70.03355,pineapple,dog,No,night owl,Yes
+LEC004,,Engineering: Biomedical,53715,41.878113,-87.629799,pepperoni,dog,Yes,night owl,No
+LEC001,,Data Science,53701,40.37336,88.231483,pepperoni,dog,Yes,night owl,No
+LEC001,19,Data Science,53703,51.5072,0.1276,pepperoni,dog,Yes,no preference,No
+LEC002,18,Data Science,53706,47.987289,0.22367,none (just cheese),dog,Yes,night owl,Maybe
+LEC002,19,Business: Actuarial,53715,45.17963,-87.150009,sausage,dog,Yes,no preference,No
+LEC005,21,Science: Biology/Life,53703,21.23556,-86.73142,pepperoni,dog,Yes,night owl,Yes
+LEC004,18,Engineering: Industrial,53706,43.073051,-89.40123,sausage,dog,No,night owl,Yes
+LEC001,21,Science: Biology/Life,53715,41.878113,-87.629799,green pepper,cat,No,night owl,Yes
+LEC001,20,Engineering: Biomedical,53703,48.8566,2.3522,mushroom,cat,Yes,night owl,Maybe
+LEC005,19,Engineering: Mechanical,53703,49.28273,-123.120735,basil/spinach,dog,No,night owl,Yes
+LEC001,19,Data Science,53706,37.23082,-107.59529,basil/spinach,dog,No,no preference,Maybe
+LEC001,19,Business: Finance,53703,26.20047,127.728577,mushroom,dog,No,night owl,Maybe
+LEC006,18,Statistics,53706,32.060253,118.796875,pineapple,cat,Yes,early bird,Maybe
+LEC002,20,Business: Information Systems,53706,52.520008,13.404954,none (just cheese),dog,No,early bird,Yes
+LEC006,18,Undecided,53706,43.038902,-87.906471,sausage,dog,No,night owl,Yes
+LEC002,20,Accounting,53703,32.79649,-117.192123,mushroom,dog,No,no preference,Yes
+LEC006,19,Statistics,53715,21.315603,-157.858093,pepperoni,cat,No,night owl,No
+LEC004,20,Science: Biology/Life,53706,13.756331,100.501762,pineapple,neither,No,night owl,Yes
+LEC004,20,Business: Other,53715,42.818878,-89.494115,pepperoni,dog,No,night owl,Yes
+LEC001,19,Engineering: Mechanical,53703,44.9778,93.265,pepperoni,dog,Yes,night owl,Maybe
+LEC004,18,Engineering: Industrial,53706,41.3874,2.1686,none (just cheese),dog,No,night owl,Maybe
+LEC001,37,Engineering: Other|Civil- Intelligent Transportation System,53705,23.810331,90.412521,pineapple,neither,Yes,early bird,Yes
+LEC001,19,Science: Physics,53703,42.696842,-89.026932,sausage,cat,No,night owl,Yes
+LEC006,19,Data Science,53715,53.266479,-9.052602,macaroni/pasta,dog,No,no preference,Yes
+LEC001,19,Data Science,53703,45.19356,-87.118767,pepperoni,dog,Yes,early bird,Maybe
+LEC005,18,Engineering: Industrial,53715,21.306944,-157.858337,none (just cheese),dog,Yes,night owl,Maybe
+LEC004,19,Computer Science,53703,40.678177,-73.94416,Other,cat,No,night owl,Maybe
+LEC005,18,Science: Biology/Life,53706,44.513317,-88.013298,pepperoni,dog,Yes,night owl,No
+LEC001,19,Engineering: Mechanical,53703,40.712776,-74.005974,none (just cheese),dog,Yes,early bird,Maybe
+LEC002,22,Economics,53703,37.6,127,pineapple,neither,Yes,night owl,Maybe
+LEC004,20,Engineering: Industrial,53703,39.359772,-111.584167,pepperoni,dog,Yes,early bird,Maybe
+LEC001,19,Data Science,53706,31.298973,120.585289,mushroom,cat,No,night owl,Yes
+LEC001,20,Computer Science,53715,43.073051,-89.40123,none (just cheese),dog,No,night owl,Maybe
+LEC001,25,Data Science,53703,37.566536,126.977966,pineapple,dog,Yes,night owl,No
+LEC005,19,Data Science,53706,36.169941,-115.139832,pepperoni,dog,Yes,night owl,Yes
+LEC001,19,Engineering: Mechanical,53703,44.834209,87.376266,sausage,dog,Yes,no preference,Yes
+LEC005,20,Engineering: Mechanical,53703,43.17854,-89.163391,sausage,dog,Yes,night owl,Maybe
+LEC004,19,Engineering: Industrial,53703,41.93101,-87.64987,pepperoni,neither,No,early bird,No
+LEC003,19,Engineering: Industrial,53703,11.89,-85,pepperoni,dog,Yes,night owl,Maybe
+LEC003,19,Engineering: Mechanical,53715,33.873417,-115.900993,pepperoni,dog,No,early bird,No
+LEC001,22,Economics,53703,42.360081,-71.058884,pepperoni,dog,No,no preference,Maybe
+LEC001,18,Data Science,53706,34.04018,-118.48849,pepperoni,dog,Yes,night owl,Yes
+LEC002,42069,Data Science,53704,43,-89,none (just cheese),neither,No,no preference,No
+LEC004,20,Business: Finance,53715,38.71049,-75.07657,sausage,dog,No,early bird,No
+LEC004,21,Engineering: Mechanical,53715,43.073051,-89.40123,Other,dog,Yes,early bird,No
+LEC004,18,Engineering: Industrial,53706,44.261799,-88.407249,sausage,dog,Yes,night owl,No
+LEC004,26,Science: Other|Animal and Dairy Science,53705,53.270668,-9.05679,pepperoni,dog,No,early bird,Yes
+LEC005,20,Data Science,53715,43.355099,11.02956,sausage,dog,No,early bird,Maybe
+LEC003,19,Engineering: Mechanical,53715,45.40857,-91.73542,sausage,dog,Yes,no preference,No
+LEC004,22,Engineering: Mechanical,53726,55.864239,-4.251806,pepperoni,dog,Yes,night owl,Yes
+LEC001,18,Engineering: Mechanical,53706,50.808712,-0.1604,pepperoni,dog,Yes,night owl,Maybe
+LEC004,19,Engineering: Mechanical,53703,13.35433,103.77549,none (just cheese),dog,No,no preference,Maybe
+LEC005,24,Mathematics/AMEP,53705,40.7,-74,pineapple,cat,No,early bird,Maybe
+LEC001,19,Interior Architecture,53532,27.683536,-82.736092,mushroom,cat,Yes,no preference,Yes
+LEC001,19,Science: Chemistry,53715,40.7,-74,sausage,dog,No,night owl,Maybe
+LEC001,20,Engineering: Biomedical,53703,-33.86882,151.20929,pepperoni,dog,No,no preference,Maybe
+LEC001,20,Engineering: Industrial,53715,26.614149,-81.825768,pepperoni,dog,No,night owl,No
+LEC001,19,Engineering: Biomedical,53706,45.440845,12.315515,none (just cheese),dog,Yes,night owl,Yes
+LEC001,19,Data Science,53726,43.0766,89.4125,none (just cheese),cat,No,night owl,No
+LEC001,20,Engineering: Biomedical,53711,33.684566,-117.826508,pineapple,dog,Yes,early bird,Maybe
+LEC001,21,Statistics,26617,22.396427,114.109497,pineapple,dog,Yes,night owl,Maybe
+LEC001,18,Data Science,53706,-33.86882,151.20929,pepperoni,dog,Yes,night owl,No
+LEC001,21,Economics,53703,1.53897,103.58007,pineapple,neither,Yes,night owl,Yes
+LEC001,18,Data Science,53558,41.877541,-88.066727,mushroom,dog,No,night owl,Maybe
+LEC001,17,Computer Science,53703,25.204849,55.270782,pepperoni,dog,Yes,night owl,Yes
+LEC001,19,Engineering: Mechanical,53715,19.7,-155,pineapple,dog,Yes,early bird,Yes
+LEC001,19,Data Science,53703,41.878113,-87.629799,none (just cheese),cat,Yes,night owl,Yes
+LEC001,18,Science: Biology/Life,53715,39.904202,116.407394,basil/spinach,dog,Yes,night owl,Maybe
+LEC001,20,Science: Physics,53711,43.038902,-87.906471,pepperoni,dog,No,no preference,Yes
+LEC001,18,Engineering: Mechanical,53706,41.902782,12.496366,pepperoni,neither,Yes,night owl,Yes
+LEC001,18,Data Science,53706,47.60323,-122.330276,Other,dog,No,night owl,Yes
+LEC001,19,Economics,53706,40.7,74,none (just cheese),dog,Yes,night owl,Yes
+LEC001,19,Business: Finance,53703,34.052235,-118.243683,mushroom,dog,Yes,early bird,Maybe
+LEC001,20,Science: Other|Atmospheric & Oceanic Sciences,53711,40.412776,-74.005974,pepperoni,neither,No,early bird,Yes
+LEC001,19,Computer Science,53706,37.774929,-122.419418,none (just cheese),cat,No,early bird,Yes
+LEC001,20,Engineering: Mechanical,53703,44.78441,-93.17308,pepperoni,dog,Yes,no preference,Yes
+LEC001,22,Engineering: Other,53726,39.48214,-106.048691,pineapple,cat,No,no preference,Maybe
+LEC001,21,Computer Science,53703,33.68,-117.82,basil/spinach,cat,No,early bird,No
+LEC001,17,Computer Science,53706,25.204849,55.270782,pepperoni,neither,Yes,no preference,Maybe
+LEC001,18,Engineering: Industrial,53706,41.917519,-87.694771,basil/spinach,dog,Yes,night owl,Yes
+LEC001,18,Engineering: Biomedical,53706,42.361145,-71.057083,macaroni/pasta,dog,No,night owl,Yes
+LEC001,,Engineering: Biomedical,53703,43.073929,-89.385239,basil/spinach,dog,No,early bird,No
+LEC001,18,Economics,53706,30.20241,120.226822,Other,neither,Yes,early bird,No
+LEC001,20,Engineering: Biomedical,53703,41.198496,0.773436,pepperoni,dog,No,night owl,Yes
+LEC001,19,Engineering: Mechanical,53703,39.739235,-104.99025,pepperoni,dog,Yes,no preference,Maybe
+LEC001,20,Science: Chemistry,53703,32.16761,120.012444,pepperoni,neither,No,night owl,Maybe
+LEC001,19,Data Science,53703,43.0722,89.4008,pineapple,dog,Yes,night owl,Yes
+LEC001,18,Science: Biology/Life,53715,41.878113,-87.629799,sausage,dog,Yes,early bird,No
+LEC004,,Business: Information Systems,53715,42.360081,-71.058884,Other,dog,No,no preference,Maybe
+LEC001,21,Engineering: Biomedical,53703,44.513317,-88.013298,pepperoni,dog,No,night owl,No
+LEC001,20,Data Science,53132,43.073051,-89.40123,Other,cat,No,night owl,Maybe
+LEC001,18,Business: Actuarial,53706,48.856613,2.352222,sausage,dog,No,no preference,Maybe
+LEC001,20,Political Science,53715,48.135124,11.581981,sausage,cat,Yes,night owl,Yes
+LEC001,19,Engineering: Industrial,53703,41,-74,sausage,dog,Yes,no preference,No
+LEC001,20,Psychology,53703,43.083321,-89.372475,Other,neither,No,night owl,Yes
+LEC001,18,Computer Science and Statistics,53706,36.162663,-86.781601,mushroom,dog,Yes,early bird,Maybe
+LEC001,19,Engineering: Mechanical,53703,25.88,-80.16,pepperoni,dog,No,night owl,Yes
+LEC001,18,Computer Science,53703,46.947975,7.447447,sausage,cat,Yes,night owl,No
+LEC001,19,Business: Information Systems,53703,41.17555,73.64731,pepperoni,dog,No,night owl,Maybe
+LEC001,20,Political Science,53703,45.018269,-93.473892,sausage,dog,No,night owl,Maybe
+LEC001,,Business analytics,53705,45.50169,-73.567253,pineapple,cat,No,no preference,No
+LEC001,21,Science: Biology/Life,53726,32.060253,118.796875,mushroom,cat,No,night owl,No
+LEC001,19,Engineering: Mechanical,53706,35.806,-78.68483,none (just cheese),dog,No,night owl,Yes
+LEC005,20,Data Science,53726,31.230391,121.473701,none (just cheese),dog,Yes,no preference,Maybe
+LEC005,18,Engineering: Mechanical,53706,41.878113,-87.629799,Other,cat,No,night owl,Maybe
+LEC004,18,Statistics,53706,27.35741,-82.615471,none (just cheese),dog,Yes,early bird,No
+LEC002,20,Business: Finance,53715,35.726212,-83.491226,pepperoni,dog,Yes,no preference,Yes
+LEC002,18,Undecided,53706,43.769562,11.255814,pepperoni,dog,No,night owl,Yes
+LEC004,19,Business: Actuarial,53703,43.040433,-87.897423,sausage,cat,No,night owl,No
+LEC004,19,Engineering: Mechanical,5,25.034281,-77.396278,sausage,dog,Yes,no preference,Yes
+LEC001,,Engineering: Mechanical,53706,34.052235,-118.243683,Other,dog,Yes,night owl,Yes
+LEC003,18,Engineering: Industrial,53706,20.798363,-156.331924,none (just cheese),dog,Yes,early bird,No
+LEC002,19,Engineering: Biomedical,53703,51.1784,115.5708,pineapple,dog,Yes,night owl,No
+LEC005,19,Statistics,53703,43.05367,-88.44062,pepperoni,dog,Yes,night owl,No
+LEC004,18,Engineering: Industrial,53706,36.110168,-97.058571,none (just cheese),dog,No,early bird,Maybe
+LEC004,21,Computer Science,53703,43.07016,-89.39386,mushroom,cat,Yes,early bird,No
+LEC005,19,Data Science,53726,43.073051,-89.40123,pepperoni,dog,No,early bird,Yes
+LEC004,18,Data Science,53706,41.878113,-87.629799,macaroni/pasta,dog,Yes,early bird,Maybe
+LEC001,20,Business: Finance,53726,43.073051,-89.40123,pepperoni,dog,No,night owl,Maybe
+LEC001,18,Data Science,53706,43.038902,-87.906471,pineapple,dog,No,night owl,Maybe
+LEC001,24,Engineering: Other,53718,46.77954,-90.78511,pineapple,dog,Yes,night owl,No
+LEC001,18,Statistics,53706,22.57,88.36,pineapple,dog,Yes,night owl,Maybe
+LEC004,20,Computer Science,53715,35.016956,-224.24911,pepperoni,dog,No,night owl,Yes
+LEC001,20,Science: Biology/Life,53715,47.606209,-122.332069,none (just cheese),dog,Yes,night owl,Maybe
+LEC004,18,Engineering: Industrial,53706,21.28482,-157.83245,pineapple,dog,No,night owl,Yes
+LEC001,20,Engineering: Biomedical,53715,40.63,14.6,none (just cheese),dog,No,early bird,Maybe
+LEC004,20,Legal Studies,53703,20.798363,-156.331924,green pepper,dog,No,early bird,No
+LEC002,18,Computer Science,53706,32.060253,118.796875,sausage,dog,Yes,early bird,Maybe
+LEC002,18,Journalism,53706,31,103,none (just cheese),cat,No,night owl,Yes
+LEC004,,Computer Science,53706,147,32.5,pineapple,cat,No,early bird,Maybe
+LEC004,18,Engineering: Biomedical,53701,43.038902,-87.906471,pepperoni,dog,No,night owl,No
+LEC004,18,Engineering: Mechanical,20815,39.640259,-106.370872,sausage,dog,No,night owl,No
+LEC004,19,Engineering: Mechanical,53715,41,12,pepperoni,dog,No,no preference,Maybe
+LEC004,20,Journalism: Strategic Comm./Advertising,53703,43.073051,-89.40123,Other,dog,Yes,night owl,Yes
+LEC004,,Engineering: Mechanical,53715,43,-87.9,pepperoni,cat,Yes,early bird,Maybe
+LEC004,19,Engineering: Biomedical,53706,32.715736,117.161087,pepperoni,dog,Yes,no preference,Yes
+LEC004,18,Data Science,53706,43.073051,-89.40123,pepperoni,dog,No,night owl,Yes
+LEC004,18,History,53706,42.19381,-73.362877,none (just cheese),cat,Yes,night owl,Yes
+LEC002,19,Engineering: Mechanical,53703,39.290386,-76.61219,mushroom,dog,No,no preference,No
+LEC002,19,Engineering: Mechanical,53726,40.416775,-3.70379,macaroni/pasta,dog,No,early bird,Maybe
+LEC005,19,Engineering: Mechanical,53726,46.870899,-89.313789,sausage,dog,Yes,night owl,Maybe
+LEC004,19,Science: Biology/Life,53151,41.878113,-87.629799,sausage,dog,No,night owl,Yes
+LEC005,18,Data Science,53711,35.1796,129.0756,pepperoni,cat,Yes,night owl,Yes
+LEC004,18,Data Science,53706,37.568291,126.99778,pepperoni,dog,No,no preference,Maybe
+LEC005,17,Statistics,53706,31.23,121.47,sausage,cat,No,night owl,Maybe
+LEC003,19,Undecided,53715,43.041069,-87.909416,mushroom,dog,No,no preference,Maybe
+LEC005,19,Economics,53703,47.606209,-122.332069,pineapple,neither,No,no preference,Maybe
+LEC005,21,Science: Biology/Life,53726,40.76078,-111.891045,mushroom,dog,No,no preference,Yes
+LEC003,19,Engineering: Mechanical,53706,43,-88.27,Other,dog,No,night owl,Yes
+LEC003,20,Business: Other|Accounting,53726,43,-89,pepperoni,dog,Yes,early bird,Yes
+LEC005,18,Engineering: Other,53706,64.147209,-21.9424,pepperoni,dog,No,night owl,Yes
+LEC003,18,Data Science,53562,42.66544,21.165319,pepperoni,dog,No,night owl,Yes
+LEC005,22,Data Science,53711,39.738449,-104.984848,none (just cheese),dog,No,night owl,Yes
+LEC003,18,Engineering: Mechanical,53706,33.748997,-84.387985,mushroom,dog,No,night owl,Yes
+LEC004,19,Engineering: Mechanical,53717,41.2224,86.413,Other,dog,Yes,early bird,Maybe
+LEC003,19,Business: Actuarial,53706,39.299236,-76.609383,pineapple,dog,Yes,night owl,No
+LEC001,,Engineering: Mechanical,53703,32.776665,-96.796989,sausage,dog,No,night owl,Maybe
+LEC004,19,Engineering: Biomedical,53703,41.878113,-87.629799,pepperoni,dog,Yes,no preference,Yes
+LEC004,26,Master of Public Affairs,53715,48.118145,-123.43074,basil/spinach,dog,Yes,early bird,Yes
+LEC004,19,Engineering: Mechanical,53703,-12.12168,-45.013481,basil/spinach,dog,No,night owl,Yes
+LEC004,18,Data Science,53706,31.230391,121.473701,sausage,cat,No,night owl,No
+LEC005,21,Engineering: Industrial,53715,1.352083,103.819839,none (just cheese),neither,No,night owl,Yes
+LEC004,19,Engineering: Mechanical,53703,40.712776,-74.005974,sausage,dog,No,early bird,No
+LEC004,19,Engineering: Mechanical,53715,37.98381,23.727539,basil/spinach,dog,Yes,early bird,No
+LEC005,20,Business: Actuarial,53703,45.003288,-90.329788,sausage,dog,No,early bird,Maybe
+LEC005,20,Engineering: Mechanical,53703,43.073051,-89.40123,pepperoni,dog,Yes,early bird,No
+LEC001,21,Economics,53703,41.902782,12.496365,basil/spinach,dog,No,no preference,No
+LEC004,18,Engineering: Biomedical,53706,45.4894,93.2476,mushroom,cat,No,night owl,No
+LEC005,19,Data Science,53703,43.2708,89.7221,sausage,dog,Yes,night owl,No
+LEC003,,Engineering: Mechanical,53706,45.87128,-89.711632,pepperoni,neither,Yes,no preference,Yes
+LEC004,19,Engineering: Mechanical,53715,42.360081,-71.058884,pepperoni,dog,Yes,night owl,Maybe
+LEC004,18,Engineering: Mechanical,53706,45.056389,-92.960793,pepperoni,dog,No,night owl,Yes
+LEC003,,Computer Science,53703,43.07,-89.4,pepperoni,dog,Yes,no preference,Maybe
+LEC001,20,Business: Finance,53703,22.20315,-159.495651,Other,dog,Yes,no preference,No
+LEC005,19,Engineering: Mechanical,53703,44.74931,-92.80088,pineapple,dog,No,early bird,No
+LEC004,21,Business: Actuarial,53726,38.874341,-77.032013,pepperoni,dog,No,no preference,Yes
+LEC005,19,Engineering: Mechanical,53703,18.34791,-64.71424,basil/spinach,dog,No,night owl,No
+LEC004,18,Engineering: Mechanical,53703,27.5041,82.7145,sausage,dog,No,night owl,Maybe
+LEC005,19,Engineering: Biomedical,53706,36.462,25.375465,basil/spinach,dog,No,night owl,No
+LEC004,27,Environment & Resources,53703,37.389091,-5.984459,mushroom,dog,No,night owl,Maybe
+LEC004,19,Business: Actuarial,53726,32,-117,pepperoni,neither,Yes,night owl,Yes
+LEC005,20,Science: Physics,53703,46.2833,-89.73,pepperoni,dog,No,early bird,Maybe
+LEC003,19,Engineering: Industrial,53703,40.712776,-74.005974,basil/spinach,dog,Yes,night owl,No
+LEC003,18,Data Science,53706,40.712776,-74.005974,Other,dog,Yes,early bird,No
+LEC005,,Data Science,53703,43.073051,-89.40123,pepperoni,dog,No,night owl,No
+LEC004,21,Business: Actuarial,53703,39.19067,-106.819199,macaroni/pasta,cat,No,no preference,Maybe
+LEC006,18,Engineering: Industrial,53706,37.743042,-122.415642,green pepper,dog,Yes,no preference,No
+LEC003,20,Economics,53703,22.54,114.05,pineapple,dog,No,night owl,Yes
+LEC006,18,Data Science,53706,59.93428,30.335098,pineapple,dog,Yes,night owl,Maybe
+LEC004,19,Engineering: Mechanical,53715,45.10994,-87.209793,pepperoni,dog,Yes,early bird,No
+LEC002,20,Science: Biology/Life,53703,51.507351,-0.127758,pepperoni,dog,Yes,no preference,Yes
+LEC004,18,Environmental Studies,53703,42.360081,-71.058884,pineapple,cat,No,no preference,Maybe
+LEC004,19,Engineering: Mechanical,53715,45,-87,sausage,cat,Yes,no preference,Maybe
+LEC004,19,Engineering: Mechanical,53703,48.137,11.575,pepperoni,dog,Yes,night owl,Maybe
+LEC004,20,Engineering: Industrial,53711,48.856613,2.352222,sausage,cat,No,no preference,No
+LEC004,18,Science: Other,53706,48.410648,-114.338188,none (just cheese),dog,No,no preference,Maybe
+LEC004,18,Mathematics/AMEP,53706,24.585445,73.712479,pineapple,dog,Yes,night owl,Maybe
+LEC003,18,Data Science,53706,36.974117,-122.030792,pepperoni,cat,Yes,night owl,Yes
+LEC004,19,Computer Science,53715,40.79254,-98.70807,pepperoni,dog,Yes,night owl,No
+LEC005,19,Engineering: Mechanical,53711,30.572815,104.066803,pineapple,dog,No,night owl,Yes
+LEC001,21,Science: Chemistry,53715,3.139003,101.686852,pepperoni,neither,No,no preference,Maybe
+LEC006,18,Data Science,53706,40.46,-90.67,sausage,dog,No,night owl,No
+LEC004,20,Science: Other|Environmental Science,53715,43.073051,-89.40123,sausage,dog,No,night owl,Yes
+LEC004,20,Engineering: Biomedical,53715,30.328227,-86.136975,pepperoni,dog,Yes,no preference,Maybe
+LEC004,21,Science: Biology/Life,53703,41.385063,2.173404,macaroni/pasta,dog,No,night owl,Yes
+LEC003,18,Mathematics/AMEP,53706,42.99571,-90,sausage,dog,Yes,night owl,Yes
+LEC004,19,Engineering: Mechanical,53703,41.385063,2.173404,sausage,dog,Yes,night owl,Yes
+LEC001,,Engineering: Industrial,53706,40.7128,74.006,pepperoni,dog,No,early bird,Yes
+LEC005,18,Psychology,53706,9.167414,77.876747,mushroom,cat,No,early bird,No
+LEC003,19,Engineering: Industrial,53715,24.713552,46.675297,basil/spinach,neither,Yes,early bird,Maybe
+LEC001,18,Undecided,53706,44.8341,87.377,basil/spinach,dog,No,no preference,Yes
+LEC003,19,Engineering: Mechanical,53705,46.589146,-112.039108,none (just cheese),cat,No,night owl,Yes
+LEC001,20,Economics,53703,39.631506,118.143239,pineapple,dog,No,night owl,Maybe
\ No newline at end of file
diff --git a/f22/meena_lec_notes/lec-06/.ipynb_checkpoints/lec_06_Creating_Functions-checkpoint.ipynb b/f22/meena_lec_notes/lec-06/.ipynb_checkpoints/lec_06_Creating_Functions-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..04530e8642e792381cea7c08fbd72bc8f1c98792
--- /dev/null
+++ b/f22/meena_lec_notes/lec-06/.ipynb_checkpoints/lec_06_Creating_Functions-checkpoint.ipynb
@@ -0,0 +1,1123 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating Functions\n",
+    "\n",
+    "## Readings\n",
+    "\n",
+    "- Parts of Chapter 3 of Think Python,\n",
+    "- Chapter 5.5 to 5.8 of Python for Everybody\n",
+    "- Creating Fruitful Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Review - pre-installed modules\n",
+    "\n",
+    "### Two ways of importing functions from a module\n",
+    "1. import \\<module\\>\n",
+    "    - requires you to use attribute operator: `.`\n",
+    "    - \\<module\\>.\\<function\\>\n",
+    "2. from \\<module\\> import \\<function\\>\n",
+    "    - function can be called just with its name\n",
+    "    \n",
+    "Let's learn about time module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add all your import statements to this cell\n",
+    "\n",
+    "# TODO: import time module using import style of import\n",
+    "import time\n",
+    "\n",
+    "# TODO: use from style of import to import log10 function from math module\n",
+    "from math import log10\n",
+    "\n",
+    "# Bad style to import everything from a module\n",
+    "# Not recommended to do\n",
+    "# from math import *\n",
+    "\n",
+    "# If you want to import everything, you need to \n",
+    "# follow import style of import\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "time module time function shows the current time in seconds since epoch.\n",
+    "\n",
+    "What is epoch? epoch is January 1, 1970. **FUN FACT:** epoch is considered beginning of time for computers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1663587453.357265\n",
+      "1663587459.713844\n",
+      "6.356579065322876\n"
+     ]
+    }
+   ],
+   "source": [
+    "start_time = time.time()\n",
+    "x = 2 ** 1000000000       # some large computation\n",
+    "end_time = time.time()\n",
+    "\n",
+    "# TODO: change the line below to compute difference\n",
+    "difference = (end_time - start_time)\n",
+    "\n",
+    "# TODO: add a separator of '\\n'\n",
+    "print(start_time, end_time, difference, sep = \"\\n\") \n",
+    "\n",
+    "# TODO: discuss - how can you use time() function to time your project code?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: call log10 function to determine log base 10 of 1000\n",
+    "print(log10(1000))\n",
+    "\n",
+    "# Recall that you cannot use math. when you use from style of import\n",
+    "# print(math.log10(1000)) #doesn't work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.141592653589793"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Can you access pi variable inside math module?\n",
+    "#pi # TODO: discuss why this didn't work\n",
+    "\n",
+    "# TODO: go back to the import cell and import math \n",
+    "# TODO: fix line 2, so that you are now able to access pi inside math module\n",
+    "math.pi"
+   ]
+  },
+  {
+   "attachments": {
+    "Modules.png": {
+     "image/png": 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peGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6VXNlckNvbW1lbnQ+U2NyZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+ClhbWm8AAAAcaURPVAAAAAIAAAAAAAAByAAAACgAAAHIAAAByAAAvif0LWz6AABAAElEQVR4AeydCbxNVf//vyokU4mEJKFBpoxFUoiMoVSeigwZQokGY0oUnociohBRHiFCpEGeVFKEiNCgTGUeHzJk/9dn/Z61/3vvs89wr3POPeeez3q93L322mt873PdfT77u77fLJZKwkQCJEACJEACJEACJEACJEACJEACJEACJEACJBAXAlkoyMWFMwchARIgARIgARIgARIgARIgARIgARIgARIgAU2Aghw/CCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQRwIU5OIIm0ORAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAU5fgZIgARIgARIgARIgARIgARIgARIgARIgARIII4EKMjFETaHIgESIAESIAESIAESIAESIAESIAESIAESIAEKcvwMkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEAcCVCQiyNsDkUCJEACJEACJEACJEACJEACJEACJEACJEACFOT4GSABEiABEiABEiABEiABEiABEiABEiABEiCBOBKgIBdH2ByKBEiABEiABEiABEiABEiABEiABEiABEiABCjI8TNAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAnEkQEEujrA5FAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlQkONngARIgARIgARIgARIgARIgARIgARIgARIgATiSICCXBxhcygSIAESIAESIAESIAESIAESIAESIAESIAESoCDHzwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJxJEABbk4wuZQJEACJEACJEACJEACJEACJEACJEACJEACJEBBjp8BEiABEiABEiABEiABEiABEiABEiABEiABEogjAQpycYTNoUiABEiABEiABEiABEiABEiABEiABEiABEiAghw/AyRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQRwIU5OIIm0ORAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAU5fgZIgARIgARIgARIgARIgARIgARIgARIgARIII4EKMjFETaHIgESIAESIAESIAESIAESIAESIAESIAESIAEKcvwMkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEAcCVCQiyNsDkUCJEACJEACJEACJEACJEACJEACJEACJEACFOT4GSABEiABEiABEiABEiABEiABEiABEiABEiCBOBKgIBdH2ByKBEiABEiABEiABEiABEiABEiABEiABEiABCjI8TNAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAnEkQEEujrA5FAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlQkONngARIgARIgARIgARIgARIgARIgARIgARIgATiSICCXBxhcygSIAESIAESIAESIAESIAESIAESIAESIAESoCDHzwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJxJEABbk4wuZQJEACJEACJEACJEACJEACJEACJEACJEACJEBBjp8BEiABEiABEiABEiABEiABEiABEiABEiABEogjAQpycYTNoUiABEiABEiABEiABEiABEiABEiABEiABEiAghw/AyRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQRwIU5OIIm0ORAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAU5fgZIgARIgARIgARIgARIgARIgARIgARIgARIII4EKMjFETaHIgESIAESIAESIAESIAESIAESIAESIAESIAEKcvwMkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEAcCVCQiyNsDkUCJEACJEACJEACJEACJEACJEACJEACJEACFOT4GSABEiABEiABEiABEiABEiABEiABEiABEiCBOBKgIBdH2ByKBEiABEiABEiABEiABEiABEiABEiABEiABCjI8TNAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAnEkQEEujrA5FAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlQkONngARIgARIgARIgARIgARIgARIIKEJ7NixQxYuXBgwx/PPP1/atm0rOCZa2rlzp7z++uvy66+/Svv27eX2228PmOLkyZPl1KlTAeV16tSRkiVLBpTHoyCSecdjHhyDBDI7AQpymf0Oc30kQAIkQAIkQAIkQAIkQAIkkOQE5syZI3fffbfvKo4ePSq5cuXyvZZRhSdOnJAiRYrIwYMH7Sm89dZb0rp1a/scGQiJZ8+edZXhBEJex44dA8qdBWg3e/ZsqV27tuTPn995Kd35SOed7gHYkARIwCZAQc5GwQwJkAAJkAAJkAAJkAAJkAAJkEAiEkg2QW7KlCnacs/J8pZbbpEvvvjCWZRuQQ5iHCwDp06dKosXL5b69eu7+k3vSaTzTm//bEcCJPD/CVCQ+/8smCMBEiABEiABEiABEiABEiABEkhAAskmyI0ZM0a6d+/uIhktQc6yLOnQoYO8+eabuv9oCnKRztu1MJ6QAAmkiwAFuXRhYyMSIAESIAESIAESIAESIAESIIF4EUg2Qe63336TG2+8UQ4dOmQjisaWVYhxnTt3ljfeeMPuN5qCXKTztgdnhgRIIN0EKMilGx0bkgAJkAAJkAAJkAAJkAAJkAAJxINAsglyYLJu3TqZMGGCHDlyRG8vve222wJQpdWHXLdu3WTs2LGufqIpyKHjSObtmgBPSIAE0kWAgly6sLERCZAACZAACZAACZAACZAACZBAvAiEE+R+//13LSRt2bJF9u/fLzfccINUqVJFypYtK1mzZvWd5i+//CKbNm0KuHbxxRdLjRo1Asq3bdsm69evDyjPmTOneMW2Tz75xDd6asWKFaVQoUJ2H2kR5Hr06CGjRo2y25rMgAEDpFq1auZUH7NlyyZ33HGHqyySk0jmDau/r776KqC78847T+rVq6f94p05c0azXbNmjXz//feSPXt2KVOmjFSoUEGuv/76gLYsIIFUJEBBLhXvOtdMAiRAAiRAAiRAAiRAAiRAAklEIJQgB5Fr9erVvquBENSuXTt55ZVXBCKVMz3zzDMyfPhwZ5HOX3PNNbJ58+aA8mHDhknv3r0DyhHhdO/eva7ySIW2SOs9+eSTMmLECNcYoU4gjv3999+hqvhei2Q+H374oTRs2NC3/Z49e2T+/PnSp0+fACamQZcuXfRacuTIYYp4JIGUJEBBLiVvOxdNAiRAAiRAAiRAAiRAAiRAAslDIJQgF8kqYEE2e/ZsueKKK+zqySLIQdwaOnSoPe9IMhklyN11110yb968sFOEReHSpUvD1mMFEsjMBCjIZea7y7WRAAmQAAmQAAmQAAmQAAmQQCYgcK6CHBAUL15cb6M0lnLJIMgNGjRIBg4cmOY7mFGCXFom+vHHH6drW21axmBdEkhkAhTkEvnucG4kQAIkQAIkQAIkQAIkQAIkQAISDUEOGMeMGSNdu3bVRJNBkIPPNfhgS2tKBkEO/uY++uijtC6N9Ukg0xCgIJdpbiUXQgIkQAIkQAIkQAIkQAIkQAKZk0AoQQ5BEuBj7dZbb5WDBw9Kv379ZOXKlb4gUBfBHOC/LBkEOYhxCKKwa9cu+cc//uG7piFDhgQEoUAgi+rVq/vWD1V4rj7kTN/169eXypUry6pVq4KKbkWKFJEdO3aYJjySQMoRoCCXcrecCyYBEiABEiABEiABEiABEiCB5CIQSpDbt2+fXHrppfaCTp48KTfeeKP8+OOPdpkz8+mnn0qdOnWSQpAz84ZwVbRoUXPqOi5evFgggEUjnasglyVLFpkwYYK0b99eT8eyLL0tdcmSJQHTgxXfX3/9FTQKbkADFpBAJiNAQS6T3VAuhwRIgARIgARIgARIgARIgAQyG4FQgtzRo0clV65criW/88478uCDD7rKzMm4ceOkc+fOFOQMEMfxXAU5+Lt77rnnHD2KvPjii9pq0VX4v5PffvtNihUr5neJZSSQ6QlQkMv0t5gLJAESIAESIAESIAESIAESIIHkJpBWQW7Lli1y7bXX+i66Z8+eMmLECApyPnTOVZDbs2ePFChQwNXzwoULpXHjxq4yc7Jp06ag98nU4ZEEMisBCnKZ9c5yXSRAAiRAAiRAAiRAAiRAAiSQSQikVZD7888/Bf7i/FLbtm3lzTffDCrIlSpVSiDoedOwYcOkd+/e3mLJnz+/7N2711UeibCFBpHWS5Ytq36C3IoVK+Tmm2928TEnFOQMCR5TkQAFuVS861wzCZAACZAACZAACZAACZAACSQRgbQKcqtXr5ZKlSr5rhBRVhFtNVhQBwR8wDZYiGXOhGAR2H7pTRktyC1YsCCoBZp3ruHOIxEIP/zwQ2nYsKFvVxTkfLGwkAR8CVCQ88XCQhIgARIgARIgARIgARIgARIggUQhkFZBDpFH+/fv7zv9l156SVu6DR48WAYMGOBbZ8OGDVK6dGn7GqK2IoorghB4U0YLcpMmTZJ27dp5p5Wucwpy6cLGRiSQLgIU5NKFjY1IgARIgARIgARIgARIgARIgATiRSCUIPf8889L37595YILLtDTWbp0qTRq1EhOnDjhO7158+ZJ06ZNZfLkyUGFrB49esjLL78sZ86ckU8++UQ6dOggu3bt8u0vHoLc8ePHdeAKRC31puuvv14WLVokV111lRw6dEjmzp0rr776qjzyyCPSpUsXb/WQ5xTkQuLhRRKIKgEKclHFyc5IIDUI7Ny5U15//XX59ddfdUjz22+/PTUWHsVV4gHw1KlTAT3WqVNHSpYsaZdHWs9ukAGZn3/+WfxC2WfLlk3goyUa6e+//5YWLVrImjVrpHnz5jJq1KhodMs+4kzgyJEj0qdPHzFfJh5//PE0OXLGFiNsy4HFwvz58wWfMSYSIAESIIHUIBBKkAOBK664QipWrKgFqWXLlgWFUrBgQdm+fbtkzZpVP7/UrVs3aF1E//zvf/8r+/btC1oHF+IhyGGcIkWKBBUFcR1iGp6ZTIKgCGExLYmCXFposS4JnCMB9VDMRAIkQAIRE1Bv56xLLrkEr+bsf2+99VbE7ROtonposd59911LOeKN69TOO+88m5+TpRI6XfOItJ6rUZxPMGfnGkxePdBFbSYDBw7UY6iHZ2v9+vVR65cdxZfAb7/95vqsfPDBB2magHIKbWXJkkX3oSwV0tSWlUmABEiABJKbwHvvvef6G2KeN9J6VNZ0Ngi1/dS6+OKLz7lfJcjZfZpMpM9wkdZDv0o8TNNclSBnphPxMZL5KGu8oPNQPuQCxvr666+D1ldBHQLqs4AEUoUA3lIzkQAJkEDEBJTFVsAf1FtuuSXi9olUEWJc69at9XoWL14c16lF8rCDCUVaL66T9wwWa0FOveW2IO7hgVtFNvOMztNkInCughzWan5n8XkYN25cMi2fcyUBEiABEjgHAtEQ5GrWrGmdPn3aNYv27dsHPNumVeSLlyCntuKmaa4U5Fy3mickkHAEKMgl3C3hhEggsQkofxQBDwLJKMidPXvWUs5v7bVQkEv/5y6WgtyBAwesokWL6vuktqJYsNBkSl4C0RDk1JZ5K1euXPozceGFF1rK6XbyAuHMSYAESIAEIiYQSpAL9gLTKazdcccdlp/11rZt2yy1NdV+JnS2ceabNWumXww6y0w+XoIcYHXq1CnsXM28KMhF/PFiRRLIEAIU5DIEOwclgeQlsHXr1gDT/mTbsgoxrmPHjq6HGQpy6f9MxlKQe+ihh+z79Nprr6V/kmyZEASiIchhIS+++KL9uahUqZKF32kmEiABEiCBzE1A+Q61/+83ghOOEOO+++47q2XLlpYK6hBQ58Ybb7TeeOONkH8rfvrpJwuCW86cOV3t8eKnWrVq1iuvvKLhjhgxwnXdzKNQoUIB8LNnz+5b980333TVjbSes5EK2mBdd911thsHMw8c4Vrm7rvv1i5ZlP87Z7OI8pHMRwW58F0b3ErgZao3rVq1yrc+5vvLL794q/OcBFKGAIM6qP8FmEiABNJGYN26dTJhwgSBg3Y47b/tttvS1kEG1+7WrZuMHTvWNQslyEn9+vVdZbE8icRhLsaPtF4s5xqub/WQK+ptbUA1zB2RydKblK84qVChgiixRZSVnCB4BJ34p5dmfNohspvy9xc06Mbvv/+uI8CZ2SgfcjoKnjmP9Hjy5Ent2Hr//v26ydtvvy0PPPBApM1ZjwRIgARIIJMSUD7hZMuWLbJjxw7JmzevFC9eXAoXLhzxatF+9erVOjAE2pUpU8aO3BpxJ3GsiIATP/74o+zevVuUKChXX321KJ94cZwBhyIBEjgXAhTkzoUe22YqAvhipxyGB6wJogKEGoRN//777wUiAf7wqTdm+o98w4YNdVSngIb/KwjXr3qTpCMOfvTRR/Ltt9+Keqsk6o2X1KtXT/9R9et35cqVeh5//vmn4J96k6XnULlyZalevbqgz1glhH33iw6KqFZ4EEDCl/KvvvoqYArqDaZelxFqlBNXHTUTXLEGPPRAgEHo9nDp8OHDui0emtSbNbnooosEkbDUW1ABB/TnlxBpyi9C54ABA0S9AXU1gfijtje4yswJHoAQwUtZDIra/qCjb2Fd+fLlk/Lly0vVqlVDPgCiLoQmb1LWZqKs9+ziSOvZDTyZaH1W1JtnzVtZOOnPaO7cuXWkyzvvvFPmzZsXE0EO0VTff/99vSLcN0QKY0pcAgcPHtS/3ypAiuBz4peiJcihb+VLTqZNm6aHwf+ZGzdujOn/fX7rYRkJkAAJkAAJkAAJkAAJpJtAytgCcqEkEIZAKL8UU6ZMsVSIdF9Ta5jJN23a1FJv4nxHCNXv0aNHdbvbb789oG+Y3D/++ON2n0oQtJ577jlLvfkKqKv+A7DL4G8LWwhjlYL56HCOGS7y0sSJE60CBQrYc3bOH/kuXboE9RW2efNmSwluvib6ph+Y6qMPZVHlwtCrV6+gY5q2ziPW6kzYFjdz5kyrTp06lhLrQvYFk31sncAWCL8UCUe0i7Sec4xoflb+85//WPAR6OTizOfIkcNSb599rysx0TmtNOWVkOjqM95bitM0WVa29u3bZ2FLED4b8MMTLCmhznVf0xpl1dnvrFmzXH2plxrOy8yTAAmQAAmQAAmQAAmQQEIToA+5hL49nFw8CYQSzpwCRLD8pZdean355ZcBUw7V7/bt27X/h2B9Kisr3d+aNWssZTXm+vIZrI0pV5ZLFoSZaKdIBKJQgtxdd90V0TrUNtiAqStrKStPnjwRtQeHkSNH2n0gOqdhE+nRKcipbbpWlSpV0tyH2u5gwQm9N0XCEW0irWf6j+ZnBb4B/XyxRMrvXAS5+++/32YN0S/UZxlCKfyxPPzww5ayTrSU9Z6lLC110A6INuealMWXBRYQxDEGhFa/f3PmzDnXoXzbQ+x3jgdfK8oK1XrhhRcs/J7gdwKiKebnFaExJ7Wt3EJADARCQHQ5OHgGM7+EtT777LMWfk8h/qNvMG3VqpWlrFH9mmgH2eXKlbPvV9asWa177rnH9e9f//qXbusnyMHBNuauLJEtZWFqKUtZC+I5ouuGSmrLvEsYxxyZSIAESIAESIAESIAESCBZCFCQS5Y7xXnGnEAo4SxSAeLyyy+3du3a5ZprqH7Vdlf7S6zfGMrPmYUvsBD7/K6HK4ND/GinSASiUIJcuDk7r3/88cf29I8dO2ZBmHFeD5c3gtzzzz+fpnamX6cgBxHDlKf1WKtWLXsdJhMJR9SNtB7qRvOz8umnn4a0QoyEQXoFudOnT7sCh0BcDpbw+1a3bt2Q96ZNmzZWepwao02fPn3siJ7h1qy2PQeb5jmVQ2R1jo1IxyVKlHCVmev4PwiinNpWbj3yyCO+dVC3QYMGlvLD5poX7jmcUpu+vEdYffbr18/VRm2Zt2644YagbUwfjRo10u28ghxExSuvvNK3PcZ79913XeN5T9TWfrstxDzls9BbheckQAIkQAIkQAIkQAIkkJAEKMgl5G3hpDKCQCjhzHypjOT4j3/8wzX99PaLLZHK/1xIqyxYlDzxxBPasiTY3BCFKZopEoEoWoIcvmybNHv2bPuLt3etsEKDRc/NN99swTrHXDeCHCx8TFlajuEEucsuu0xvX7333nv1NtpgfaMfbE92pkg4on6k9WDxFMqCLy2fFQg1iNwVbD2RlqdXkPv8889dY0MM9Uv4/YAAFcl8sP60JAg7TZo0iahvM74KZpCWISKu6xXkzHjBjhC4lC/JsHOHsOdMwT5r3nEQ5Q4JUdQitdwNJsg5+8b/ed6t4Ihup3x7Oqfpynuj3flZKbsa8IQESIAESIAESIAESIAEEoQABbkEuRGcRsYTCCWcYdteu3btrHHjxuntXPCR5Pwi6cwjVDj8KZkUql9nO29eObS3lixZEnScd955xwyhj2PGjPGt6xS1XA3SeRLsS3ukPuTMOiGQwNoGR1PmPRYpUsSepQp04FsPX+BhDWQSRAKEpofPPyPIrV271oIvtOnTp/v2gXGHDBmi66Ce+acCU5hu9TY/Mz9sz1u4cGGANY6K3hq0f+/2u0g4YvBI60Xzs4KtwWatfsfHHnvMUgFIrKVLl1qdO3d2iaDO+ukV5J555hnX+BB4/dKjjz7qqle7dm39O/Prr79asPaqUaOG63pafIx5PyvY8omtnNiyCh+FZp34vwFbObHtUkVm85vmOZf5CXIQX1WAFQvbNlWQGcvPDyXmCF+KEDhVxDm9xdX5eYKY6fzdMWtSAVK0GAkhFBZszu2oqAPLXiT8rkEQwz8na2yNNeXmuGDBAt3GayGH/sAWvuSOHz+uGfbs2dPmi+sQ24Mlrx+5vn37BqvKchIgARIgARIgARIgARJIKAIU5BLqdnAyGUkglHD29ddfu6a2devWkFu7Jk+ebNcP1a/5AgyrrrJly7oCHUAUwZZTU8d5RBAJb4KFlJ+1CrZ9/fHHH7r6qlWrLGz/i+QffED5JecXeuecIhXkMB8EdTAJ80aQBGdfJo+xjGCArYOm3HuESAIRxpngYwvb6ZwJPvu8bc15uKABZstq9+7dgwovEBVMf96jESTMfCLhiLqR1ovmZwU+x7zzN+dPPfWUWYJ9HD16tG/99Apy2E5pxsMRn1tv+uGHHyz0b+r5/U7Ayq1SpUp2HeQjTU5fh/ClpiKI2k0hvDk/s7CQjGXyCnItWrQICHoC329O61Bwgd87b8Ln1zDD0Rl0BOfXXHONFvic7cAxf/78djtY4HkTPhem37QEdcD/e34BcW666Sa7v4svvtg7nH3+xRdf2PUwvt/nwK7MDAmQAAmQAAmQAAmQAAkkEAEKcgl0MziVjCUQSjjzbjfETLHdy3wB9R6dW9dC9Yt22BoICxeTJkyYYOELLYQofFn19o1zCGqwkPP+K126tG99fGlFCmf55BwLVjJ+KRKBKNSWVScb0z+s05xjO/OwqEGCRZqz3JvHvBAIAEJNsHQughwsrryiGsbB9k6st3379iEDTnjbRsIR/UdaL5qflWA+weDDD1ZR3gQx1ns/cJ5eQc5E6zR9btu2zTukNWrUKHtMiLx+gTPQyPmZB8tIrdiqVatm9+/d2ol+4d/QzA9+12KZvIKc03LTOS58FZo5lSxZ0nnJzsOaz9TBEVZ2JkGIhZDtl5wWqhDnvCm9glywKKuwcnXOM1hQDwiKznomEI53fjwnARIgARIgARIgARIggUQjQEEu0e4I55NhBEIJZ36CHL4UO78IOvMQZ0wK1S/8I23YsMFUDTjCMsTZb3rzM2bM0H07xYlwfcVKkMPWPm8KZVm2adMmu3rjxo3D8oA4A+s+iG/edC6CnLcv+KqCSBHpPYq1IBfpPMLdd3xW4Bzfrx588fmlaAty3sACfiIatmI654jtwn7/EJ3UWW/jxo1+Swgoc/pgg/jnTc7PLITKYFFLve3Scx6pIOeMTBtMkIOg6uSB6LThErYnwwecaQffid4UbUEOvi/NeDgaK1/vuPi/2VkvlHWety3PSYAESIAESIAESIAESCAjCVCQy0j6HDuhCIQSzvwEOVjkOL8IOvOIbmhSqH47depkqvkenVvynP2nNW8EhUQV5LAlONianIIchJknn3wyqNWYs49SpUoFfImPhiD34YcfWujbOVYk+VgLctH8rASzyoMFll+KpiD3999/u7ai5smTx29IC/7iIuHurWMCEvh26ih0WoTBx9nmzZvtq/gcVahQwR7f+FSzK0Q5E6kg5xQpgwly2H7qZDJ+/Hjf2cLnHPyx+UVz9dtCGm1Bzil4Yr7GUtZvsnh5YNYEH55MJEACJEACJEACJEACJJAMBCjIJcNd4hzjQiCUcOYnyHm/JJsvhDgOGDDAnnOofkNtr0QHRYsWtb9oOvtHHpZgkf4bO3asnk+yC3IGKoIuwK+Xl4n3HH6onCmUIOcVzJztkIcF1KBBg4KKgXCqjy3E3jmYc2//wUQvpy8+jBtpvWh+VmABZebtPAYTeaIpyIEzAiWYcf22R4ILIuqaOhBk2rRpE9G/lStXonnY9P333wewx+cJ99l7TxDsJZbJ+39NsC2rzqAiwe5VOEEO/tzwQsEp8ObMmdMVzTbRBDmIcOazAGtFJhIgARIgARIgARIgARJIBgIU5JLhLnGOcSEQSjjzE+TgC818CfQep02bZs85rf3aDVXGGbnQOQa29JlgB8764fKZRZDDOk+fPq2jpmIbpZONMw/B0umMP5QgN2nSpJD4sJXT2bfJQwDA9jokRNc15d5jrAW5aH5WvD7czFogREGo8qZoCnLou3DhwjZHiHN+6e6777brwIItFgnWY/gMmfX7HRFMA1Z9sUzxEuQQPAMWiWad4ArrWkRydfrsSyRB7vDhw/Z8MW9uWY3lJ5F9kwAJkAAJkAAJkAAJRJMABblo0mRfSU0glHC2bNky19rWrVtn5c2b1/VF0HyJxdEZFTJUv35Cn3OgwYMHBx0DkUXD+a1au3at9cILL9hdYjxsD43kH5y/+yWvdZBZt9OyK1RQBz8fcpFsWcV8EM3T69wdFj9PP/10UE4moAXW8t///jeowIIItVtV9FwkiHjwrQVh6rXXXtNlrVq18h1j+PDh+jp+hBLkZs2aZddDJhKOaakXzc/Kgw8+6LtW3Gs4zXdGr8XW7VtuucW3fnqDOlSsWNHVH0QXb+rdu7erztKlS71VonI+Z84c1zjm8547d26rV69eMRfjsIh4CHIQFZ3BPHCfndzDCXLPPPOMzenyyy8Pyh5bTw1DHIMFdYh0yyqDOgRFzQskQAIkQAIkQAIkQAIJToCCXILfIE4vfgRCCWf44livXj0L4stjjz1m4cu480ulMw9n8M4Uqt9wghwsuoIJNxjz1ltvtZYsWaJFKohzEEcgHg4bNswVoTWccOecb7h8sPnEWpAz20FhPTVy5EgLAh0inILhc889F/R+QBx0Jqf1lfO+mbxzqx7KXn75Zd08WNAEpyCHe2H68R4hWMAvlxE6I+GIgSOtF83PCiLKeufvPL/kkkuspk2bWjVr1rSc/rucdZBPryAHn2zOvoxQ6ryPEJuddWAZ5RQKnXWRX7FihZ6zXxRR+Cb0+x2BVaNZH9ZSp04d/buF+3zs2DHvEAHnfv1ifIjkt912m/4cBzTyKYiHIIdgF06eb7/9tmsm4QS5Z5991tX+559/drU3J9EW5LzBdfC5ZCIBEiABEiABEiABEiCBZCBAQS4Z7hLnGBcCoYQz5xfVcHl8WXemUP2GE+TQT79+/VxfdP3GD7etzk9scM4xLflIBKJYWMgZQc65fswl1Nqx3RFRJZ2pbt26YXk6xzCCXDAhD/7W4NAeFoteMc/Zj8nD+gwpEo5pqYe60fqs4PNSpkyZNHEy63Me0yvI9e/f3zW210IVa0W68847XfUgFL766qsWxCUIX999950FYalZs2Z2vd27d/9fY/UT276bN29uZcuWzbrmmmuszz77zL6GjHM7NLbIRppC9esMfgBWwSzEnGPFQ5DzirBOoRlzcfqnM1tWIYibBO7Oe4+gGLCGheUd/PYhAiysTaMtyHktGGEpykQCJEACJEACJEACJEACyUCAglwy3CXOMS4EQglnzi+aofItW7YMmGuofiMR5LAlE9Z5ocYNdy2zCnLh1n3PPfcE3A9sbQzXznndCHLoy1me3nwsBbloflYgauXKleuc1pxeQW758uWucWHx6Zfg28wZ7TSSe+IU5GbOnOkaB374nOnee++1r0PchcgEAc25ldNZ3+RD9euNWvroo4+aZkGP8RDkEMzByQ+iG8RDbN1u0qSJ6xrqIUI0RGoTmAZHZ3vkwQw+Fk05LAOjLch5RehIg3YEhc0LJEACJEACJEACUSGA51JYriPwGHYY4ZkH+fr16+tdLlEZhJ2QQJIToCCX5DeQ048egVDCmflCGewIKy1Y9fg5dw/VbySCHFaIAAb44hnMqirYvCCINGrUyHc7XnrJBZtDvLasBlurtxz+sPy2J2LdEBO89YOdG0EOfgMR8TNYPZSDTbg6sRTksLZoflYQzTaYZWAoDuZaegU5/B5deumlNutQ2xB37dqlHfmbMUMd4RfN+TuHoA3O+qVKlQJCO61fv9513dTFfYYQ+Pjjj1sff/xxwO9XqH4bN27s6hNWeOFSPAQ5zKFFixauuZn1hjri98Kk1q1bh2wfC0GuSpUq9pj43YvmywezLh5JgARIgARIwI8AfBMzBSdgAuBlzZrVwjMV3IeYnS0dOnQI3pBXSCCFCFCQS6GbzaWGJhBKOMM2qCJFithf/MwXVDgvh/XI/Pnzg3aOa6a+84gv9Wn9Q44/Zk8++aTvXEzf+KMHp/iYM6xeop2yZ8/uux5Y0pj0ySef+NbBH2HvFlK0QRAMM3/v8ZdfftHd4i0bBKKePXtaEE689cw5IkNi+5xzO52Zl/OIyKjXXXed/WBg2uOIrY/Yovjuu++67hH8lsGKClscnfVz5sxp1a5dW2/Nw3wLFCjguo66V155pb53GzZs0NOIhCMqRlrPuTbko/VZ2bt3r2aOrbnONSMP0exf//qXNWHChIBruH7hhRd6pxXxOaKXmvHAM1Tatm2bFsecEUJNW1h6YZsyhDNvgqVbyZIl9TiYq/kM47M2aNAgl3WX6c/veMcdd2j/jab/YP3iOiwPy5UrZ68NfunCJa/12bfffuvb5IknnrD7RZASvwSxE5ZrZh3O6ML43cTWUnMNR/DD/zn4XOMzbK7BehKM4CfPJIjB8O+INqYejhgP24bx++O1xPvoo49Mc9cR5c4+/P4v279/v+slBR/uXQh5QgIkQAIkEEMCXbt21c+D1157reV8ORXDIZOqa7gbwYtZ/C1HIC6TnC/vxo0bZ4p5JIGUJZAFK1e/KEwkkPIElC8iUSKMLwdlVSNKdJFNmzbJH3/8IeqLpygfW6JEOt/6sS7Er636MirKOkiUI3s9nAo0ISryqyiBQZRgFOspZHj/SnDT9wIMlAAiypJLrrrqKs0gLZNToqj8+OOPorYySqFCheTqq68WJSiE7EKJEKJERFERX0X5HhMlVIgSG+02KFdb5wR9Y164rkQ++3o8M9H6rCiRSn799VdR4peevhLoRFkhinrYisly1IOc1KpVy+5bBcMQFfXWPvfLYK3Kb5moaLeiLKU0d/xOhEpog3uF+2jue58+fWTo0KGuZvj9V9tNRQlamgPusTNVq1ZNVIABm4dfv6a+ChYhyupPn6ptoaL8tZlLCXFUlqWiBDD9u6S2lthzAlNlrafZ4v8/tR3VvubMoJ4KxCEq0Ijgc4LfSxUcw1klKnm1NVjuu+8+3Zd6wSFK7BT1xSgqfbMTEiABEiABEghGAN8H1Isv+3L79u1l4sSJ9nmqZw4ePChqB4B+DlAvykUFNbOfGfDcjr/VKjiWqJehovz9SunSpVMdGdefwgQoyKXwzefS3QTCCXLKIsTdgGckQAIxJQABTllV6THUNmNRW0FjOh46h9ALcRuiOxIeKMeMGSMQ3JT1qS6DGLdw4ULp1auXLVDigrIMFWWNp+uE+mEEPwhaKhqpFm1D1ec1fwLKIk6UhZ++qHw8yqxZs/wrspQESIAESIAEokgAL8PxstU8KygLMHnppZeiOEJyd6Ws4GTatGl6ESqgk3Tp0sW1ILDq27evLqtUqZJ+Mep8se2qzBMSyOQEKMhl8hvM5UVOgIJc5KxYkwTiQcBpSQZBfOfOnaK2pcZ0aFhZwfLPpG+++UaU7zlz6jrCWrBYsWJ2mfI3KD169LDP/TKwMLvpppu0RSXepiufgn7VWBaGAN6+gz2sl/EQD4tVtVU/TCteJgESIAESIIHoEJgyZYq88sor2nofVvUFCxaMTscJ2Ass55U/OBk1alTY2SmXKaL87GpreljZ48Wjd+cOXn5ilxF2+yC9/fbb8sADD4TtmxVIIDMSoCCXGe8q15QuAhTk0oWNjUggpgRgcbZkyRI9BizVlM+WmI6Hh0Plsw7+VfU4s2fPDrqV/aefftJbXc2EVGATUZFYzanvcciQIYKHVTy8YysnU/oI4IuB8mGnG3fu3FmUH5r0dcRWJEACJEACJEACQQngBVi9evVE+RQWFSk9aD1zoXnz5vL+++/rU7ykxMtKv+S0olM+nbXbCVrJ+ZFiWWYnQEEus99hri9iAhTkIkbFiiQQNwKwQlNBELSfQPghgQUbtpTGMmE8iGZI8B2HLaYqOqp+mwufefA3qIJEaFENPiWR8BCJNk7rOn2BP6JOAD4CVWAXwRt7WMmBe6w/E1FfBDskARIgARIggQQngJeUKnCV9h+Lv7fhBDlYq6vo5/aqFi9eLPXr17fPnRm88GzZsqVdpII5aeHPLmCGBFKEAAW5FLnRXGZ4AhTkwjNiDRLICAIqArLARxhSt27dREXRjek0EJwBD5AIyhFpeuyxxyLayhFpf6wXnEC7du1k8uTJoqK3yueffy7Vq1cPXplXSIAESIAESCAGBOBy4tSpU7pnWMcbH7J4WeS0lu/Zs6cOAIFnly+//FJUpHTBVs4777xTWrRooX3UeqeHIAj9+/f3FmtftsWLF9dBphDEDeIXAiN4k3cOAwYMkLJly3qr6YAKw4YN0+V4saiizesXkSiARRzWpCLI6uvwo3vXXXfpvPkBFxzwp2tSq1atZMaMGfoUfnJV9Hbf+aECXE7kz5/fZoi206dPN13xSAKpQ0Bty2EiARJQBNSXfuxR8/2n/miQEQmQQAYSUA6T9e+miqZprVixIuYzUUKPpXyS+f5/4Px/QkURtVSU1JjPhwP8HwEVfddSXxr0fRkxYgSxkAAJkAAJkECGEMDziHkeGD16tD0HZUVml+O6iqZuKfHMVWbaqRdL1vz58+22JvPZZ5/51jftzFFZrVnvvPOOpaKbm6b66J3DBx984LpuTpSVmmsctQNAX1JBKyxl8e+6ZsZ0Hhs1amS6slSAC0tFq7fbKMHRvhYso7bC2vXz5ctnnTlzJlhVlpNApiVACzn1vwoTCYAAwm4PHjw4AAa2qOGNjdchaUBFFpAACcSMgHrYlLZt28qmTZv0G1v4YotHwttsbE/dvn27jqiKt+GFCxfWb7cRBRbbLfh/QzzuxP+Nge3D6ouKVK5cWcaOHRu/gTkSCZAACZAACTgI4PsBnk2QlCAn3bt31/nff/9drkqDj1hYuMFXrtPae+nSpVK7dm3dXyQ/brnlFh3p3VjLeeegBDlR4llAV84dCLgIlxywhKtRo4b8+OOPAfW9BegTfSOpF2ZSq1Ytu0okga5GjhzpsrDDMxfGZiKBVCJAQS6V7jbXSgIkQAIkQAIkQAIkQAIkQAIkcE4E0iLIQaDDS39sAVVWYKIs67WIZybQrFkzmTt3rjkVryAHtx3w4XbkyBH9D9tet27datdHxuk641wFObiFQII7H7jxQEK0++eff17nzY9rrrlG+9jFudrJIGb7K84XLVokDRo0QDZo8vqR69u3r8TrhWvQSfECCcSZAAW5OAPncCRAAiRAAiRAAiRAAiRAAiRAAslLIFJBDr7eYNkNv3HOVLVqVVm5cqUuyps3rw5UZK57BTmIYk4LOtSbOnWqwKfq33//rZvBB9ynn36qLevORZBTrjjMNOTpp5+Wf/7zn/o8XFCHhg0byocffmi3RYCHSpUq2ed+GVjE1axZ077UtGlTmTdvnn3ODAmkAgEKcqlwl7lGEiABEiABEiABEiABEiABEiCBqBCIVJCDcPbQQw8FjAlrMliVmfTXX39J9uzZ9Wkkghwqei3MOnXqJOPHj5eMEOSU310djdWsZ9u2bQEipLlmjj///LOOmm7OIVJ+88035pRHEkgJAhTkUuI2c5EkQAIkQAIkQAIkQAIkQAIkkLwERo0aJTt37gy6AFihGV9uQStF6UKkgty0adMEEVm9CeWtW7e2i//44w+5/PLL9XmkghwqX3311fb2Vfhfg9VZRghy8K+LNZjkFBhNmfd47NgxyZ07t10czgrPrsgMCWQiAhTkMtHN5FJIgARIgARIgARIgARIgARIIDMSKF26dMhgA2XKlJH169fHZennKsghGEKTJk3suarIqNpPHArSIsg9/PDD8tZbb+l+zNbXeAtyCG6BAFdm+2yePHnk8OHD9tpCZXLmzCnHjx/XVWAhCCGPiQRSiQAFuVS621wrCZAACZAACZAACZAACZAACSQhgcwkyC1cuNAOiIBbkV5BbsCAATpgBPqASAhBC1aEzkivaYmymh4fcpZlaUEOASuQ8ufPL3v37tX5cD8QGfbkyZO6Wo4cOWxxLlw7XieBzEKAglxmuZNcBwmQAAmQAAmQAAmQAAmQAAlkUgKwgNuwYUPQ1ZUtW1bWrVsX9Ho0L5yrhVy0BLn7779f3n33Xb00RD3dvHmzbN++Xa688kp7uTNnzpSWLVva5ybz3nvvCSK4mrR7925JjyCH9kWKFJFdu3bpri644AI5ffq06TboEVFjYdVnEresGhI8phIBCnKpdLe5VhIgARIgARIgARIgARIgARJIQgLYBhlqSyOsrZwCTyyXmAiC3IEDBwQi3P79+/VSW7RoIRDZvELX2LFj5dFHHw3AEU6QQ9AJBJ9Agn87p484b2eIqLp69Wq7GPcKW1dDJQZ1CEWH11KFAAW5VLnTXCcJJBgB+JnAg8OaNWukefPmAjP3BQsWCLYjzJ8/X5u+J9iUOR0SIAESIAESIAESIAES0NtD4TsNafTo0XYwCa//tmBBHaJhIdehQweZNGmSfTeGDx8uTz31lGALKazUzPzatGkjU6ZMseshgzpo/+abb9rlXgu5gQMHyqBBg+zrENBKlChhnzszjRo1kkWLFtlFW7dudW2btS84MsuXLxcEojCpadOmMm/ePHPKIwmkBgH1y8hEAiRAAnEnoP7IW+p/WStr1qyWcsBrrVixwsqSJYsuUw8IcZ8PByQBEiABEiABEiABEiCBSAicd955+pkVz7JKkLObKF9wdjmuKUHOvubMKL9urnpoZ9Jnn33muoZnZiWGmcuWslSzmjVr5qpTsmRJ68SJE3adG2+80b5+0UUXWV9//bV97ejRo5aK8GpfxzzxTwlydh1kXn31VVedjh07Wnv27LHUS3Vr5cqVltoua7322mu6Tf/+/V11ly1b5urL72TOnDmuNoMHD/arxjISyNQEaCGn/vdhIgESiC+BL774Qm6//XYdjQnm8C+99JKeAN7gTZ06VefHjRsnnTt3ju/EOBoJkAAJkAAJkAAJkAAJhCEQzy2rZiqFChUS9SJbtm3bZors45IlS6R27dr2Obaa4hnbJFjMYVupevktq1at0jtTzDVz9FrIwV8f/PY5E/rBHJT4p4tfeOEFUWKcKMFPqlevblfF+E8//bR97pdBuyFDhtiXlMgnlStXts+ZIYFUIEBBLhXuMtdIAglE4ODBg1K+fHntcPaKK66QLVu2CKIqIcEZ7LXXXivHjh0T+AH57rvv9BbWBJo+p0ICJEACJEACJEACJJDiBDJCkPNDjoimeIntDM6AesqkSB5++GH7Rbe3LeaP53Gn3zevIIc2zpfl3j5wbgQ5bI9FQAjjzy6S7adVq1YViHBIWIeyvtOCoS7gDxJIEQIU5FLkRnOZJJAoBJSJvMCfBpIyc5cuXbq4pgZrub59++oyvMnDH2q8zWMiARIgARIgARIgARIggUQggBfHJ0+e1FNx7urYuXOn4IWzSTNmzJD77rvPnNrHjz/+WOrXr2+f79ixQ0cqRcHSpUtd1m7FixfXQheCNZiEqKa33XabjBgxQgoWLGiKXUf4a+7Tp4/MnTtX4P8NKVu2bFKlShUdrAHi2V133aXL8ay9d+9eufTSS/W5+QEfz2rrqvYld+jQIVOsfdQ1btxYnnvuOS3s4YLzGb9AgQJaYLMbeDIISIE6xs8d/NlNmDDBU4unJJD5CVCQy/z3mCskgZgSwB9nOH0dNWpU2HGUrzipUKGC/uNbtGhR/XCABwNnwsMNHjLMG7a3335bHnjgAWcV5kmABEiABEiABEiABEggUxLwCnJfffWV3HzzzfLTTz8JrNgQAM0rnIUDoXzU6Sipyrec3oUSrr73OoQzBGrYvn27toS76qqrRPmmc1VTfuOkVq1adhms7zCeX5o5c6YtVCp/fLJx40a9S8avLstIIDMToCCXme8u10YCMSaA7af16tXTb9Twhz5cQjTV999/X1fr0aOHvPzyy75NnG/YrrvuOv1HmlZyvqhYSAIkQAIkQAIkQAIkkIkI+AlyTv9sibxUCHBr167VU+zUqZOMHz/ed7rOCLHYbjtr1izfeiwkgcxOgIJcZr/DXB8JxIgALNjuuOMOWbNmjRQrVkzCCXJwIAsTeZMWL17sMtU35TjOnj1bWrZsaRd99NFHWvizC5ghARIgARIgARIgARIggUxIIJkFuQULFgj8xyHlypVLsIU3T548rruEF/r47qCivdpBJipWrOiqwxMSSBUCFORS5U5znUlDQIVBl7Fjx8rmzZu15Rn8UJQqVUr/cWvVqpXkzJnTdy0IhADRa9OmTfof/ECcPn06oK4KKS7XXHONXd6rVy9tfo6CJk2ayEMPPaT/eCIyE/xbwBwdPioQCalEiRK6HfquW7eurFu3Tp8j2pLxQaEL1I+bbrpJ0LdJmDv8aCAhiAN8R8D/hl/CH2g4dz116pS+jLbTp0/3q8oyEiABEiABEiABEiABEsg0BJJZkMNNwHcERH1FGjNmjHTt2lXnzQ+4uhk0aJA+7dy5sw5KYa7xSAKpRoCCXKrdca43oQk88sgjMnHixKBzzJs3r448aoQxU/Hf//63Fr/++OMPUxT0+Nlnn8ntt99uX4fYZxy9duvWTR5//HHt/wERT50Jb7kmTZqkr9WpU0cQCj1UatSokUBcRIJDWDhuNc5g77zzTvnwww9DNdfWcxAEkfLly6cdwyIiFBMJkAAJkAAJkAAJkAAJZFYCyS7Ibdu2TcqVKyeHDx/WAS7gHy537tz6du3bt08bGuA7Aazk4F/aXMus95PrIoFQBCjIhaLDayQQRwLvvfdeQMhyv+GxRRSBEZBgAQerNmzpjDSFEuTatm0rcBy7ZcsW3+4GDBigt5P++OOPvtedhU5BzuvkFb7j4EMuVBo5cqTLwu7LL7+UGjVqhGrCayRAAiRAAiRAAiRAAiSQ1ASSXZADfOf3GrzwR6RWpHbt2snkyZN1lNbPP/9cksU3np48f5BADAhQkIsBVHZJAukh0KVLF5fj0+eff17uvvtuQRj0Tz75RF5//XXBtlSnIIdtpEOGDHENV7ZsWalUqZJ+24QIpojGhEilJoUS5EwdRE56+OGHBeHSp02bpv3DlSxZUiCKvfPOO7ranDlztHiHE1jPYb7OhG2xCIeOhO2vw4YNsy8vWrRIGjRoYJ/7Zbx+5Pr27RuwVr92LCMBEiABEiABEiABEiCBZCWQGQQ5sO/Tp48MHTpUEEV1+fLl2hUNorBaliUjRoyQnj17Just4rxJIGoEKMhFDSU7IoFzI4AIQ3ibZBK2jBYqVMicCky8R48eLRDuUA5/cfDTBtEMCebeEN6MI1XTENZsCI9uUjhBDoLep59+qkOao82RI0fkscceE/hxq1+/vulGnn76afnnP/+pz8MFdWjYsKFriyrmDtEwVIL4V7NmTbsK1jVv3jz7nBkSIAESIAESIAESIAESyGwE4KMZbmxMeuutt+S6664zp0lzPHv2rGD3Dfxbw68czvE9pHLlytpfdtIshBMlgRgSoCAXQ7jsmgTSQuC5555zWZlBGMMfYIQP90tOQQzXp0yZIm3atAmomlZBLtKtoc7xwwlyiJwEyz6T4FuiaNGi5tT3CL928G9nUtWqVeWbb74xpzySAAmQAAmQAAmQAAmQAAmQAAmQQNISoCCXtLeOE482gVGjRunoosH6hYDUvXv3YJfPuRxbSyFAIcKoSYheiiALzz77bIDDUwRWwFsmJIh333//vQ4dbtqaY1oEufLly8vatWtN05DHtAhyhQsXFmfAib/++kuyZ88esn9sz3U6eQ0n+oXsjBdJgARIgARIgARIgARIgARIgARIIIEIUJBLoJvBqWQsAWzrDBWsoEyZMjoSUCxnuWLFCr3ldO/eva5h4NMN1nK33nqrXX7JJZfYUUth1v7GG2/Y15yZtAhy999/vyBiayQpUkEO5unwZWe21ubJk0dHXYpkjJw5c8rx48d1VQh4EPKYSIAESIAESIAESIAESIAESIAESCDZCVCQS/Y7yPlHjUAiCHJYzC+//KK3niLaqTPBIeqbb75pb0uFyIUoq0gQx5xBE5zt0iLIOaMgOfvwy0cqyMFxK+Z65swZ3U3+/PnFKzj69Y+yCy+8UE6ePKkv58iRwxbngtVnOQmQAAmQAAmQAAmQAAmQAAmQAAkkAwEKcslwlzjHuBCABdyGDRuCjoVtoXCyGo8EEQshwZ955hkdzMGMCWELIcIRzME532bNmsncuXNNNdcxowU5TKZIkSKCIBVIF1xwgS0k6oIgPxBMIm/evPZVblm1UTBDAiRAAiRAAiRAAiRAAiRAAiSQ5AQoyCX5DeT0o0fg8OHDIbdEwlrLKRBFb+TgPR04cEBgtebcRmq2leL47rvv6sb58uWTLVu2yKWXXhrQWawEud69e9tWeZdffrnLR5x3Eoiounr1arsYrLF1NVRiUIdQdHiNBEiABEiABEiABEiABEiABEggmQlQkEvmu8e5ZyoCAwYMkHr16knNmjVd64K13JVXXik7duzQ5VdccYVs375dRo4cKb169bLrPvzww9qqzi74XyZWgtzAgQNl0KBB9nAQ0EqUKGGfOzONGjWSRYsW2UVbt24V+MULlZYvXy41atSwqzRt2lTmzZtnnzNDAiRAAiRAAiRAAiRAAiRAAiRAAslKgIJcst45zjvTETBWZNh+Cv9sVapU0ds7v/32Wy3SnTp1Sq8Z4tzvv/+urfnKlSsnP/30k83innvukfHjx7ss5SCEQRAzCZFZb7/9dnOqI7tCTENKiw+5MWPGuKLOduzYUQYPHqzHhjXciBEjdBCKLl26CMRGXDNp2bJlAcKjuWaO2ILbokULc6rb9+vXzz5nhgRIgARIgARIgARIgARIgARIgASSlQAFuWS9c5x3piNgBDmzMEQYxVZUWMM506OPPipjx47VRf/5z3+kdu3aAis6k7JmzSoIUAELtPXr18uvv/5qLuljtAQ5+NuDHztngn84jH/ixAld/MILL0j//v3l66+/lurVq9tVEYAComOohHZDhgyxq6xcuVIqV65snzNDAiRAAiRAAiRAAiRAAiSQOAT+/vtv/UJ9zZo1cskll8jBgwf195L58+frIG+JM1POAnl2XgAAQABJREFUhAQSgwAFucS4D5wFCejtmdimGSoVL15cVqxYIZdddpldbfbs2QIrtH379tlloTLREuQwRps2bWTq1KlBhzOC3NmzZ/Wc9+/fr+tGsv20atWqAhEOCZFZ9+zZI1myZNHn/EECJEACJEACJEACJEACqUbg+PHjctFFFyXssp977jl5/vnn9Qv6t956Sx544AFtONChQweZMGFCws6bEyOBjCJAQS6jyHNcEvAQOHTokEybNk3eeOMN+eGHH1xXEV31kUcekWeffdYlxplKu3fv1v7kli5dakczxbXzzz9fv51yinVeQQ7WdPAzh/TEE09o33T6JIIfZ86ckVdffVX7ksP8TYKlXOPGjQV/lMuXL6+LW7durdeHkwIFCmiBzdT3HhHMAnUg5CHxj7iXEM9JgARIgARIgARIgARSiQBcy0DUwgv6WbNmSdmyZRNq+V988YV2iwMrOQR/e+mll1wv78eNGyedO3dOqDlzMiSQ0QQoyGX0HeD4JOBD4OjRo/Lbb79pq7ciRYroP7zYChpJgvgGgQ3bXUuVKiW//PKLNhU3bb2CnCk/lyOEMwRqwPZaWO9hu6z37R38xtWqVcseBn7mbrzxRvvcmZk5c6bcd999uui8886TjRs3yrXXXuuswjwJkAAJkAAJkAAJkAAJpASBTZs2yfXXX2+vtX379jJx4kT7PKMz2JqKl/D4LoAAdFu2bJEcOXJoQwE8wx87dkwuvPBC+e6771zfSzJ63hyfBDKaAAW5jL4DHJ8EYkwgXJTVGA/v6h4C3Nq1a3VZp06ddAAKV4X/ncAibtKkSfoMgSrwFpCJBEiABEiABEiABEiABFKRwJ9//ikI7Hb69Gm9fGOBligsnDthXnvtNe1Ox8wNlnJ9+/bVp/CZDZc0dENj6PCY6gQoyKX6J4Drz/QEEkmQW7BggcB/HFKuXLlk586dkidPHtc9wBu2YsWKCawE8cd61apVUrFiRVcdnpAACZAACZAACZAACZBAKhGYMmWKvPLKK3qHydChQ6VgwYIxWT7c0AwcOFBGjRoVUf8IIlehQgXtaqZo0aLy888/uwI4nDx5UrDjx/iSfvvtt7VvuYg6ZyUSyOQEKMhl8hvM5ZFAIglyuBt169aVJUuW6BszZswY6dq1q+sm4QFg0KBBugx+JuBvgokESIAESIAESIAESIAESCC2BPBivF69erJ3717tPieS0Zo3by7vv/++rtqjRw95+eWXA5o5Leiuu+467Y6GVnIBmFiQggQoyKXgTeeSU4tAogly27Ztk3Llysnhw4e1jwn4h8udO7e+KfB/B793eDMHKzm8cTPXUuuucbUkQAIkQAIkQAIkQAIkED8CsGC74447ZM2aNfo5HP6swyXsZKlSpYpdbfHixVK/fn373GRmz54tLVu2NKfy0UcfaeHPLmCGBFKUAAW5FL3xXHbqEEg0QQ7k33vvPYFvOCREjEKkVqR27drJ5MmTBVFaP//8c6levbou5w8SIAESIAESIAESIAESSAQCCFjQv3//gKkgABsioJYoUUJKliyphSoEMvBLvXr10gEQcK1Jkyby0EMPaVcu8A338ccf6+Bot912mx4H/SE9+OCDcurUKZ3v2LGj3nWiT9QPvMxGmUk9e/bUQSDwjP3ll1/Kt99+K9hOeuedd0qLFi2kWrVqpqo+wiIOu1jWrVunz7GWu+66y1XnpptuEszbmVq1aiUzZszQRQjicODAAR28wVkHebiiyZ8/vz1/tJs+fbq3Gs9JIOUIUJBLuVvOBacagUQU5HAP+vTpI/B/gSiqy5cv13+gEYXVsiwZMWKE4EGCiQRIgARIgARIgARIgAQSicDSpUuldu3aYaeE3R4vvviiQHzybs/EjhD4WkPCy+nHH39c8By8a9cuV7/wuYxAZ/fee6+cf/752k8bKowePVq6d+9u1/3999/lqquuss+feuopmTt3rj2GfUFl8OJ7zpw5WghE+e7du6VOnTqyYcMGZ7WAfKNGjeSDDz6wy8+cOSMFChTQYiAKIfZ9+OGH9nVvBpZzEBuR8uXLJ3v27NFr8tbjOQmkEgEKcql0t7nWlCSAwAl33323FroAYMKECXrLaEbDOHv2rLRt21YQxh1v5HD+2WefSeXKlWXs2LEZPT2OTwIkQAIkQAIkQAIkQAIBBCIV5EzDW265RT755BOX5ZhTkMPz8FdffSWwvPNLQ4YM0VFK0yLI+fXjLIPlHnw6X3/99VKjRg3BC/xwySvILVu2TIuIph18x8GHXLA0cuRIl4UdLPcwNhMJpDIBCnKpfPe5dhIgARIgARIgARIgARIgARIggYgJeAU5uGGBNdyRI0f0P2wP3bp1q6u/xx57zBW11CnImYqwcHv44Yfl77//lmnTpumgCtj6+sMPP0j27NnTZCGHPtHf4MGD9YtvWLMNHz5cW9aZ8Zo1ayZvvvmmdheDMljNQRhEgmXe888/r/PmxzXXXCONGzc2p4LttcOGDbPPFy1aJA0aNLDPvRmvH7m+ffsKxEYmEkhlAhTkUvnuc+0kQAIkQAIkQAIkQAIkQAIkQAIRE/AKchCxvH6Pp06dqn0jQ1xDwpbVTz/91N7q6hXkypYtq69fdtlluj7EPYh42O5qgiSkxUIOQh52nsBvnDNVrVpVVq5cqYvy5s1rbzdFwdNPPy3//Oc/9TUIjOGCOjRs2NC1RRUBHipVqqTb+/2ARVzNmjXtS02bNpV58+bZ58yQQCoSoCCXinedayYBEiABEiABEiABEiABEiABEkgzgUgEOXTqtQjr1KmTjB8/Xo/nFeQi2b6ZFkEOgiACRXgTLNpg2WbSX3/9pa3vcJ5WQa5ixYo6Iqvpa9u2bQECoLmGI3zmYd0mQRz85ptvzCmPJJCSBCjIpeRt56JJgARIgARIgARIgARIgARIIHkIjBo1SkciDTZjWIM5Ax0Eq3eu5ZEKchjn6quvtrevwl8ahDckpyBXvnx5Wbt2rS4P9SMtghy2vCIqqzehvHXr1nbxH3/8IZdffrk+T6sgV7hwYUF7k5zinilzHo8dOya5c+e2iyKxwrMrM0MCmZQABblMemO5LBIgARIgARIgARIgARIgARLILARKly4dMvhAmTJlZP369TFfbloEOfiEe+utt/ScnFtEnYLc/fffL//+97/DzjsaghyipDZp0sQeC9tSIYwhpUWQQzC2bNmyaX93aJsnTx45fPgwsiFTzpw55fjx47oO/OJBxGMigVQmQEEule8+104CJEACJEACJEACJEACJEACSUAgGQW5AQMG6MAKwAtBDQLUBRdc4LKQ69atm7z66qth70A0BLmFCxe6AjOkV5CzLEsLcggWgZQ/f37Zu3dv2DUguuvJkyd1vRw5ctjiXNiGrEACmZQABblMemO5LBIgARIgARIgARIgARIgARLILARgAbdhw4agy0FghHXr1gW9Hq0LabGQg/Xbu+++q4dGlNLNmzfrvNNCLhkFOSyiSJEismvXLr0eiIynT5/W+WA/EKgCVoImccuqIcFjKhOgIJfKd59rJwESIAESIAESIAESIAESIIEkIIAtkaG2OML6yin4xGpJkQpyBw4cEIhw+/fv11Np0aKFvPfeezqfiIIcgj0g6AMS/Mo5/cPpQs8PRFRdvXq1XYr7g62rwRKDOgQjw/JUJkBBLpXvPtdOAukggPDteKBYs2aNNG/eXGCqvmDBAsE2gvnz52vz9XR0m1JNyDClbjcXSwIkQAIkQAIkkIkIRCrIdejQQSZNmmSvfPjw4fLUU0/p80QU5AYOHCiDBg2y5wsBrUSJEva5N9OoUSNZtGiRXbx161a56qqr7HNvZvny5YLAFiY1bdpU5s2bZ055JIHUJKD2fzORAAkkMYH//ve/cZ29+mNtqf8traxZs1rKca61YsUKK0uWLLpMPXjEdS7JOhgZJuud47xJgARIgARIgARSncBnn32mn3vxPIx/eK5T4pWNRVmWWc2aNXPVKVmypHXixAm7Ds5Ne7Vl1S4PlTnvvPPsNqNHj3ZVVb7g7GvoV0VTdV03Jyqog6se2pmk/Ni5rnXs2NHas2ePpV4kWytXrrTU9lvrtddeM9Wt/v37u+ovW7bMvuaXmTNnjqv+4MGD/aqxjARSioCk1Gq5WBLIZAS6du1qqQhH1rXXXmspnxkxXx3+0CqHsvqPqTJrt8dT4dPtP7Djxo2zy5kJJECGgUxYQgIkQAIkQAIkQALJQsAryBlhrVChQtaVV15pPxObchyXLFniWl4iCnI//PBDwNyVbzhLBV+wy1944QV7HcrizS7HGtV2V/uaX6Zfv36u+hD5mEgg1Qlwy6r634OJBJKRwKZNm+T666+3p96+fXuZOHGifR7tzMGDB6V8+fKyfft2ueKKK2TLli2C6EhIcOiqREE5duyYwH/Hd999p7ewRnsOyd4fGSb7HeT8SYAESIAESIAEUp2Ad8tqKB6IPqpeVss999zjqpaIW1YxwTZt2sjUqVNdc3WeKEFOlGWcLjp79qxcdtllto+8cFtQq1atKkqE023BRVnfidpl4+yeeRJIPQKprkhy/SSQrARgDo9to+p/Lf3PabEWizU99NBD9lhOc3Uz1osvvmhfV05eLfVH2lzi8X8EyJAfBRIgARIgARIgARJIbgJeC7nixYtbKpiB/RyMZ3MVgdR64IEHrD///NN3seqlul3/iSee8K3jLcyePbvdxrsjZceOHfY1jD9jxgxvc33+0UcfueqhnTOpSKnWyJEjrYsvvthVD5Zy2Ia7du1aZ3XL+WxboEAB1zXniQpsYTm33NLNjZMO86lMgBZyqafBcsWZiMCUKVPklVdekRtvvFGGDh0qBQsWjMnqlK84qVChguBNWNGiRQVOXtVWWddYJ0+e1OHPTSSpt99+W9SDiKtOKp+QYSrffa6dBEiABEiABEggsxDwWsh99dVXcvPNN8tPP/0ku3fv1rtELr300qReLp75EaQBO2NgBYdgDRdddFHAmpQrFqlVq5Zdjqir+F7iTTNnzpT77rtPFythTjZu3Kh313jr8ZwEUo0ABblUu+NcLwmkgwCiqb7//vu6ZY8ePeTll1/27UX5khPlRFZfu+666/QfW5qi/x8qMvT9yLCQBEiABEiABEiABJKKgJ8gV7169aRaQzQnCwFOWc7pLjt16iTjx48P6N4ZcRbbd2fNmhVQhwUkkIoEKMil4l3nmkkgDQRWrVolVapUsVssXrxY6tevb587M7Nnz5aWLVvaRcosXurVq2efp2qGDFP1znPdJEACJEACJEACmY0ABTn3HV2wYIHAfxxSrly5ZOfOnaK28NqV4EO5WLFicvToUe0zDs/FFStWtK8zQwKpTICCXCrffa49IQmocOQyduxY2bx5s+zdu1cHUIDjV/yha9WqleTMmdOe94MPPiinTp3S5yo0udStW9e+dujQIUGZST179tRBIFRIc/nyyy/l22+/1dtP77zzTmnRooVUq1bNVHUdMabyQ6HLEMThwIEDOnCDq9L/TvCHFk5azZzQdvr06X5Vz6ls7ty5oqJVyffffy8bNmzQf9wRaALCIZjAdD6YZZ4K3a7XriJJCbaRYoutioolZcuWlXLlyvma2Tsnm5b7Y9olIkMzNx5JgARIgARIgARIgAQiJ0BBLpAVvoPg2RxpzJgx0rVrV7vSwIEDZdCgQfq8c+fOOsiFfZEZEkh1AqnsQI9rJ4FEIwAHp+r/pKD/8ubNayn/bfa0nc5RR48ebZcj89tvv7n6eeqppyxniHXnOHDUOn/+fFd7nMCxq9OpqxLvAup4C5RFnD1uvnz5rDNnznirpPtcCZTW3XffbffvXIMz/69//ct3jDVr1liVK1cO2b5hw4bWtm3bfNun9f6gk0Rj6LswFpIACZAACZAACZAACUREwBvUQfmQi6hdZq70+++/W/iegudx9ZLcOnLkiF4unt3NdwllJWeXZ2YWXBsJpIWApKUy65IACcSOgNruGVIoMoITRCWT0iLImfbBjhdeeKHlfaD4/PPPXXNSvuPM0EGPI0aMcLVR1nhB66blgrLM0xGrgs3fWT5s2LCArhEFFsKjs16wfO7cuS3l28LVR3ruDzpIJIauBfGEBEiABEiABEiABEggzQQoyPkjcz4rd+vWTVdq27atfvbGM7j3e4Z/LywlgdQiQEEute43V5vABJQJt0ssev755y21rdJSPtusXr16Wcong75+LoKcipBkqeinOgQ7wpw/9thjrjERztyZnnnmGdf1RYsWOS/75iFkOYWuvn37+tZLa6GXz/nnn2+1a9fOmjBhgjV58mRLbcm11NZTPbZXkFPbTF1zwvzKlCljoc/evXtb9957rwVrPue8wfuXX36xp+kdP5L7g8aJxNBeDDMkQAIkQAIkQAIkQALpIkBBLjg2PFfjeRpGA6+//rqlXMjoc7ywZyIBEggkQEEukAlLSCBDCHi3Yu7atcs1D5h8DxgwwHKWp8VCDttV/bZiKr9rthAFU3NnatCggX0Nf1yVE1bnZd/8F1984WqjfN/51ktLIcZ1rlX5qbMwjjedOHHCwtZdp3Cows9bKly7Pads2bJZeChQvuRczffs2aOFOacoV7NmTbteeu4PBkgUhq7F8oQESIAESIAESIAESCBdBJQPY6tq1ar2vx9//DFd/WTGRni+bt26tWajIs/q46OPPpoZl8o1kUBUCFCQiwpGdkIC505AOTy1RSOIQirIgLV69eqQHTtFqnA+5KZOnerb19ChQ13j/vXXX3Y9Fcbcdc1P0LMr/y/z008/udrggeVcE6zRnELZW2+9FXGX4OJs+8ILLwRte/bsWat27dqu+thyipSe+4N2icIQc2EiARIgARIgARIgARIgARIgARJIDAKMsqq+qTORAAiMGjVKh+kORqNo0aLSvXv3YJfPuVxZcgmiqSJSqUlZs2aVxx9/XJ599llRfs1MsX1U2zZFiUj6XAlPrvkp56qitqjadadNm6YjkNoF/8ugXL3Jsov/+OMPufzyy/V54cKFBecmKbFOsmfPbk59j8eOHXPNFWHOVYAJ37qRFjqjlF599dWitpJG2lSv+Z133tH1EYn1119/FXANlnC9RIkS9mV8LtTWXknP/UEnicLQXhAzJEACJEACJEACJEACJEACJEACGU6AglyG3wJOIFEIlC5dWpTJedDpKJ9jsn79+qDXo3FhxYoVorZ4itqe6uoOwpqyCpNbb73VVR4NQU75V5MmTZrY/UI8g4gGoU9t7xRleq6v5cmTRw4fPmzXC5XJmTOnHD9+XFeBgAch71xShQoVRG0P0F0oP3cyd+7ciLuDyKki0+r6zZs3lzlz5oRtW6RIEVFbg3U95adOJk2apPNpvT+JxDDsolmBBEiABEiABEiABEiABEiABEggbgQoyMUNNQdKdAKJIMiBEay/2rRpIyoSkQuZ2p4qb775pr5mLkRDkFu4cKE0btzYdKmt2SDIKSNeLcidOXNGX1N+2wKEQruRJ6MitsrJkyd1aY4cOWxxzlMt4lMIkrD4Q4K13PTp0yNuW7BgQVH+4XT9Dh06iAoCEbZt+fLlZd26dbqe2sIqS5Yssduk5f4kEkN7AcyQAAmQAAmQAAmQAAmQAAmQAAlkOAEKchl+CziBRCEAC7gNGzYEnY7y6WaLNEErRekChBwVOVRUhE7Zt2+f3Sss1pRPM7npppt0WSwFOQzgtBRT4crl9OnT9lyCZY4cOSIqOIR9ORpbVuvVqyeffPKJ7lP5ZBPlW8/uP1zm9ttvl//85z+6Wo0aNeTLL78M2QQWgSrCqm3V9/DDD+t74WwU6f1Bm0Rh6Jw/8yRAAiRAAiRAAiRAAiRAAiRAAhlLgIJcxvLn6AlEANsxQ22thNWXU2iKx9QPHDgg3bp1k3//+9/2cPfff799HmtBrlKlSi7xC4ywdTVUwvZQbBM1SQV1kG+++cacpuv4xBNPyCuvvKLbYs1ff/21qOiwEfXlbAvfcSpiq5QrVy5o2ylTpkjbtm3t6xMnTpT27dvb585MuPuDuonC0Dlv5kmABEiABEiABEiABEiABEiABDKWAAW5jOXP0UnAJjBgwACBJVjNmjXtMmRgjXXllVfKjh07dDkCE2zfvl3nYy3INWrUSBYtWqTHwo+tW7e6AkXYFxyZ5cuXCyzRTIJPvHnz5plT7Yfu1Vdf1dtAcQ2CWbjkDTxxww03aEvBSy+9NFxT7W+uRYsWdj34o8MW1Hz58tllJgMfgipEuxw6dMgUiYoaKyVLlpT03B90EguG9uSYIQESIAESIAESIAESIAESIAESSE4C6ss+EwmQQAIQqFixoqX+F7FU0AJLiVqW2h6qZ6Wsyyy1VVVfw3UlztmzVX7l7HIVZdUuR0YFZ7CvoZ0StVzXzYkK6uCqh3Ym9e/f33Vt2bJl5lLQowqa4GozePBgV92nnnrKdR3jh0tgobaqutopn3bWuHHjLCWiWadOnbJUEAYLfT3wwAOWEuBcXSofea62l112maUCNVjKms86ceKEpYJ1WC+88IKlrCBd9VSEW7uf9NwfNI4FQ3tSzJAACZAACZAACZAACZAACZAACSQlAVjfMJEACSQAASP4QDzDPxWp1CpatKhLIEL5o48+as821oIchEEzHxyHDRtmjx0s069fP1eblStXuqqWKFHCdd25HldFz8l3331nKYtAV1vn3Jz5ypUru1r/+eefVoECBSJqa/qBAKgCU9j9pOf+oHEsGNqTYoYESIAESIAESIAESCDuBHr06GF16dJF/1P+ieM+fnoGVIHaLLU7RX+/UO5b9LF+/fqu59309Ms2JEAC6SdAQS797NiSBKJKQG2VDCsYFS9e3Nq9e7c9bqwFORXgwFLbQu154Y94uKR8u9n1YcV29uxZVxOvtZqKaOq6Hupk/vz5lgqSYPdvxDPv0SvIoU9Y0qmttGHboi9Y2Tk5o3167g/axYIh+mUiARIgARIgARIgARLIGAKhnsEzZkbhRx04cKB+DlY+la3p06dbWbJk0ecdOnQI35g1SIAEYkKAglxMsLJTEkg7gYMHD1rYdqqivQaIRtiy2rVr1wCRKHv27HZdbN90JuVzzr4GkWnGjBnOy3b+o48+ctVDO2d66KGH7OuwMguV9u/fbzkfUPz+wG/cuNHCWzkjotWpUydUlwHXVGAJq3v37hYs7ZxjoT+cV6tWzVI+6gLaoQDi4NixY/X2V+/21EsuucSqVauW9emnn/q2Tc/9MR1Fm6Hpl0cSIAESIAESIAESIIH4E3A+g3rdxsR/NuFHhNsZs9Okd+/eukHr1q3t53Hv94jwPbIGCZBANAgwqIP6Fs9EAolG4OjRo6J8ucm+fftEWYSJsowTRAjNiKT+gIsSquyhV69eLWo7p33uzMycOVPuu+8+XaQeVESJb3Lttdc6q+j8ggULBAEdkJRPORk+fLjOp/WH8v8mmzdvliNHjkjBggU1q1y5ckXUjbJc0wEb9uzZo6PCFipUKKJ2qJTW+xMLhhFPlhVJgARIgARIgARIgASiSiBUYLWoDhSFztRLZVE7UnRQOASH27Jli+TIkUOU/2X9nH7s2DFRL6pFuYeR0qVLR2FEdkECJBApAQpykZJiPRJIYQIQ4NauXasJdOrUScaPH+9LQ1nEiQqWoK/dc889MmvWLN96ffr0kaFDh+qHARVYQQoXLuxbLzMVRpthZmLDtZAACZAACZAACZBAMhFIJkFOWcKJCu6m8b722muifN/ZqF966SXp27evPq9UqZIo38+itrLa15khARKILQEKcrHly95JIFMQcFq0wQJt586dkidPHtfa8PatWLFi2noMf8hXrVolKhCCqw5O1qxZIzfddJP+Yz9x4kR58MEHA+pkxoJoMsyMfLgmEiABEiABEiABEkgWAskiyK1fv14qVKggym2LqGBxghfhyhWOjVkFMNM7TJTbGV329ttvi/KlbF9nhgRIILYEKMjFli97J4FMQ6Bu3bqyZMkSvZ4xY8aI8mnnWptyFCuDBg3SZZ07dxbli8J13ZwMGTJE8HAAC7mrrrrKFKfEMVoMUwIWF0kCJEACJEACJEACCUogWQS55s2by/vvv68pqsiw8vLLLwcQdVrQXXfdddrlDK3kAjCxgARiQoCCXEywslMSyHwEtm3bJioYg6igCgL/E/APlzt3br1Q+LorVaqUHDp0SFvJQXAz1zIfifSviAzTz44tSYAESIAESIAESCBRCCSDIIfdKlWqVLGRLV68WOrXr2+fm8zs2bOlZcuW5lRUwDepV6+efc4MCZBA7AhQkIsdW/ZMApmOwHvvvSfwDYfUrVs3UdFMdb5du3YyefJkueCCC+Tzzz+X6tWr63L+CCRAhoFMWEICJEACJEACJEACyUQgEkFORWCUKVOmCIJ7wWXLr7/+KiVKlNAuXRo0aGA/UzvXjZfbHTt2tIt69uwp119/vX7m/vLLL+Xbb7/VW0/vvPNOadGihVSrVs2u6820atVKZsyYoYsRxOHAgQM6eIO3HoKV5c+fX06dOqUvod306dO91XhOAiQQAwIU5GIAlV2SQGYmYAIyIIrq8uXL9R9vRGHFQ8eIESMEDw5MoQmQYWg+vEoCJEACJEACJEACiUwgnCD3xx9/CLaCfvrpp0GX0aZNG0GQhYsuusiu8/vvv7tcujz11FMyd+5c7fvNrvS/DF6Ez5kzR5o0aeK9JGfOnJECBQro3Su4CAHvww8/DKhnCmA59/HHH+vTfPnyyZ49ewRrZCIBEogtAQpyseXL3kkg0xGAU9i2bdvKpk2bBD7RcP7ZZ59J5cqVZezYsZluvbFYEBnGgir7JAESIAESIAESIIH4EAglyMES7YYbbpA///wz7GQghGErqUleQc6UBzteeOGF2sezd3cKrPLwwtwk+I6DD7lgaeTIkdKrVy/7MqzxatSoYZ8zQwIkEBsCFORiw5W9kgAJkAAJkAAJkAAJkAAJkAAJZEICoQQ5BD6D5ZtJtWvXln79+knx4sX1tlUEQvvqq6/MZZfPNj9BDkHQBg8erF+Ew/Jt+PDhMnr0aLt9s2bNtBWdXaAyvXv3lmHDhtlFixYtEmyTDZa8fuT69u0rCMTGRAIkEFsCFORiy5e9kwAJkAAJkAAJkAAJkAAJkAAJZCICwQS5DRs2SPny5eXvv//Wq23atKnMmzfPtXJcg++37777TpdXqlRJEIABySvIlSxZUu9EKVq0qL5uflStWlVWrlypT/PmzWtvTTXXGzZs6Nqiiv4xTrAEi7iaNWval/3mbV9khgRIIGoEKMhFDSU7IgESIAESIAESIAESIAESIAESyOwEgglysFx7/PHH9fKzZMkiO3bskMKFCwfggEgHyzYk+GU+fvy4ZM+ePUCQmzp1qjz00EMB7WH9Bis4k/766y/d3pxXrFhRB5Iw59u2bdPBIMy59/jzzz9LqVKl7GIIft988419zgwJkEBsCFCQiw1X9koCJEACJEACJEACJEACJEACJBAlAqNGjZKdO3cG7Q1WZN27dw96PZoXgglyjz76qIwbN84eqlu3bnbemdm9e7fMmjXLLtq4caOOpuq1kJs2bZo8+OCDdj2TQTmCRpiEIBKXX365OdUiIMpM8gp2ptwcjx07Jrlz5zanUqxYMfntt9/sc2ZIgARiQ4CCXGy4slcSIAESIAESIAESIAESIAESIIEoEShdurT8+OOPQXsrU6aMrF+/Puj1aF4IJsjVqVNHbzFN61jz58/X0VIjFeQ++OADV3RViGcQ0ZAQPCxbtmz2ttk8efLI4cOHw04pZ86c2lIPFWGtBxGPiQRIILYEKMjFli97JwESIAESIAESIAESIAESIAESOEcCySDIIdrp119/rVd60UUXScuWLSNaNSzpKleuHLBlNZiF3MKFC6Vx48Z2305BzrIsLcghAARS/vz5Ze/evXbdYBlEbD158qS+nCNHDlucC1af5SRAAudOgILcuTNkDyRAAiRAAiRAAiRAAiRAAiRAAjEkAAs4BE0IlsqWLSvr1q0Ldjmq5cEs5O655x5577339FhXXHGFbN++PU3jRmohF0qQw4BFihSRXbt26bEvuOACOX36dMh5HDlyRBAcwiRuWTUkeCSB2BKgIBdbvuydBEiABEiABEiABEiABEiABEjgHAlg22WobZSw8HKKSuc4XMjmwQS5Pn36yNChQ+22S5culdtuu80+D5eJliCHiKqrV6+2hwM7bF0NlhjUIRgZlpNAbAlQkIstX/ZOAiRAAiRAAiRAAiRAAiRAAiSQiQgEE+S+//57qVChgr1SWJohWmnBggXtMmcG11588UVBNFWIidES5Bo1aiSLFi2yh9q6datcddVV9rk3s3z5cqlRo4Zd3LRpU0EkWCYSIIHYEqAgF1u+7J0E4k4AYdPhs4KJBEiABEiABEiABEiABEgg+gSCCXIYqUGDBrJ48WJ70EsuuUQGDRokCPhQuHBh+eWXX3RwitmzZ8v777+v6yHq6mWXXRY1QW7AgAEyePBgew7Lli2TmjVr2ufezNy5c6VFixZ2Mdr269fPPmeGBEggNgQoyMWGK3slgQwhAIewEyZMkOLFi+tQ6vClwUQCJEACJEACJEACJEACJBA9AqEEuaNHj8qtt94qa9eujXjAaAtyCCyBABMmDRs2TJ5++mlzGnDs37+/DBkyxC5fuXKlDjJhFzBDAiQQEwIU5GKClZ2SQPwJbNq0Sa6//np74Pbt28vEiRPtc2ZIgARIgARIgARIgARIgATOnUAoQQ69//HHH3LzzTdri7dwo1WtWlWWLFkiuXLlipqF3NmzZ7XF3f79+/Xw4bagYg4Q4ZAQlXXPnj2SJUsWfc4fJEACsSNAQS52bNkzCcSVwJ9//ilXXnmlHUWpd+/e8tJLL8V1DhyMBEiABEiABEiABEiABDI7AQSQOHnypF7muHHjpHPnzgFLRoTVESNGyOTJkwVRTJ3p4osv1hZosFq744477Es7d+4URGc1acaMGXLfffeZU/v48ccfS/369e3zHTt26MiqdoHKtG7dWqZNm6aLChQooEU253WTP3DggOA6RDykDh066B035jqPJEACsSNAQS52bNkzCcSdwJQpU+SVV16RG2+8UUd4CuZANp4Te+CBBwSRm5CeeOIJuf/+++M5PMciARIgARIgARIgARIggQwjYFmWFsP27dunRS+8QI9HNFj4jatVq5a9bkRdxXcEb5o5c6Yt+p133nmyceNGufbaa73VeE4CJBADAhTkYgCVXZIACfwfgTNnzmjze/MGcfr06dKqVSviIQESIAESIAESIAESIAESiDEBCHDGl12nTp1k/PjxASPCIm7SpEm6/J577tF+qAMqsYAESCAmBCjIxQQrOyWBzE0Ab/pgUl+oUCGBD41g6YcffhBnYIl169a5zoO1M+VmHESNzZcvnynmkQRIgARIgARIgARIgARIIAyBBQsWCPzHIcFHHZ7f8+TJY7c6ePCgFCtWTBCIAj7jVq1aJRUrVrSvM0MCJBBbAhTkYsuXvZNAxAS2bNkiiHDkTVmzZtVRU0uUKCElS5aUKlWqCPxW+KUHH3xQTp06pS917NhR6tat61ct3WWvv/66Ds++YsUKOXTokEAoq1y5sgwfPlyqVaum+921a5f06NHDzn/11Vf2eHggyJ49uxQtWlT71LAv/C8D3xXYdgsT+w0bNuiQ8P/973/1VbRDCPYnn3zS24znJEACJEACJEACJEACJEACPgTwfQBBI5DGjBkjXbt2tWsNHDhQBg0apM/hBw/+8JhIgATiR4CCXPxYcyQSCElg6dKlUrt27ZB1cBFvsV588UW99dMb/ShcxKewnQepcOLECe3gFVtO/VK2bNm0CXzbtm1l8eLF0qBBA79qdhnEO4h6zvT9998LRMRvv/3WWezKw5y+Xbt2rjKekAAJkAAJkAAJkAAJkAAJ+BPYtm2blCtXTg4fPqwDRsBHXO7cuQU+7UqVKvX/2LsTeKvG/fHj37Wns4fTPBCJQoQyXJGIwjUkU4OKBvOYocvlZ3YN15h5Hq6oFJ2SEG6KiBCRUKQooqI0nD2cPa3/evb9n63Tek7OOe19zh4+6/W6t72/37We4f14NXzPWs9K/ZBd/ftiwYIFqbi+FaIIIJANAQpy2VClTQTqIFDTglxl04cccohMnz69yt1y2SjIJRIJ6dGjh8yZM6ey6/Qde2pzWPVmJnX4fD759ttvUz+BU3tRqENdW3mo4qHaKFYdp556qjz//POVKbntttvkpptuErXn3OZHixYtUm+OVW+nmj17thx88MGbn8J3BBBAAAEEEEAAAQQQqEZg0qRJovaHU8eIESPkoYceSv2QW70B1uVyyaxZs6R79+7VXE0YAQSyJUBBLluytItALQU2L8ipPzTVT6tUIUr9T9059sMPP1Rp9ZJLLpEHHnggHctGQU49prrpq9yvueaa1K3tqq/ffvstVaxThTh1DB8+PPXIaeWA1CvU1U/f1KHGqca7+TF16lQ58cQTq4QPPfTQ1OO7e++9t7Ru3TqVU3tbqEdkVb8cCCCAAAIIIIAAAgggUHOBq6++Wu64447UD8jVo6nq7/dqv+ZRo0bJP/7xj5o3xJkIIJAxAQpyGaOkIQS2TmDzgpzae23zn1Spu8rUI5uVd56pu87efvvt9KOumS7IlZeXp/atW7VqVWpyffr0EbU57KaH2ndC7T+hDlVA/PHHH1Of1TXbbrtt6rP6v5kzZ0qvXr3S39UHtd+cKrpVFu3U3nj3339/6tHVzR/HrXIhXxBAAAEEEEAAAQQQQKDGAmqvZrW9zKJFi1J3xaknU9Re0I888kiN2+BEBBDIrAAFucx60hoCdRaoSUFONV5WViYDBgxI97PpK8wzXZB78sknRbVfeVx11VVywAEHVH5N/Tp37tzUT9vUF1VEUy9hUI+vqs1jN32phLqbrmXLllWuHTZsmIwZMyYdU7fPq9voORBAAAEEEEAAAQQQQAABBBAoZAEKcoW8usytVgLqkUr1KvDqDvVm0Isvvri69FbHa1qQUx116NAh/fiq2lNN7a2mjkwX5C699FJ58MEHU23X9P+WLFmSGp/yrHzb6jbbbCMrV660NdGuXTv56aefUnH1Iohp06bZziGAAAIIIIAAAggggAACCCCAQKEJUJArtBVlPnUW2GOPPWThwoXVXr/XXnul3j5U7QlbmahNQe7000+X5557LtVjkyZNUm9HUl8yXZA75phj5K233qrVzFavXi1q77hzzjlHnn766dS16k459QKKTQ9V/Gzbtm06pO7869evX/o7HxBAAAEEEEAAAQQQQAABBBAoVAEKcoW6ssyr1gL5VJC7/vrr5dZbb03NURXhIpFIai+ITBfkdt55Z1m6dGmqn+uuu07UHXN/dai3oqpHVw866CD56KOPUqePHDlS7r333iqXTp48uUoBTu09p/ag40AAAQQQQAABBBBAAAEEEECg0AUoyBX6CjO/GguoO+C+/vrras/v3LmzfPnll9XmtzZRmzvkBg0aJC+++GKqy44dO0rlW04zXZDr1KlTauNX1dE999wjl19+eY2mqd7YpO7cU29GVcczzzyTehnFphePGzdOhgwZkg6tW7cudU06wAcEEEAAAQQQQAABBBBAAAEEClSAglyBLizTqr3A+vXrU3eaVXelegOoKjJl66hpQW7t2rWiinBr1qxJDaVv374yadKk1OdMF+ROOOGE9FtVL7jgAnn00UdrNP1ly5bJTjvtlD73k08+ka5du6a/qw8ff/yxdOvWLR1T3zd/YUQ6yQcEEEAAAQQQQAABBBBAAAEECkiAglwBLSZTyW+Bmhbkzj777NQdZ5Wzveuuu+Sf//xn6uuWCnKq4KjeYqrefqoKbeox0r861B1xlY+abrfddqm75Ro1aqS9TBXhVK558+by+uuvS58+fdLnqTvlSktL09/Vhw0bNlQpcA4ePFheeOGFKufwBQEEEEAAAQQQQAABBBBAAIFCFKAgV4irypzyUmDzgtyNN94oQ4cOFbWPmzrUW0rVXWpTpkxJz2+XXXZJvWhC3b2nji0V5K688kq5++6709e+9tprctxxx6W/6z7MmjVLevbsmU6p8Tz11FNSUlKSjoXDYbnzzjtFFQbHjh0r6o49dSfdRRddlD7nhx9+SN8xN3fuXFHjbtasWequuU8//TR9nhqfKgKqPegqj8WLF6eKgmruXbp0SYVVn2oPvalTp0rv3r1F7am3ecGv8np+tQuE375ePJ0HinObvexJIggggAACCCCAAAIIIIAAAlkXoCCXdWI6QKBmApsX5CqvatOmjbjdblm+fHllKP2rutvt8MMPT3/fUkFOFcGWLFmSPvfCCy+URx55JP29ug8nnXSSvPLKK+m02mtP7WHXuHFjUcW1t99+W3799ddUfsGCBaLyan87dU7loR6x7dGjR2qPPvWih5dffllUu+pRVvXyh2QyWXmqqJdrVD66umjRotQ5Kv/hhx+mzlUnqqLgueeem75G3fk3YsSI9Hc+bFkgNPVCif8yT1w7HiIlB5wnzha7bvkCsggggAACCCCAAAIIIIAAAhkVoCCXUU4aQ6DuAtUV5HQttmzZUh577DHp379/lfSWCnLHH3+8qLviKo+9995bvvjii8qv1f6qiniq6KcrCG56kSrQrV69OnX33IoVK2S33XaTYDC46SnpzzNnzpRevXqlvt9www1yyy23pHPVffj5559l++23T6Uvvvhiefjhh9OnDh8+XEaPHp3+zoctC0Teu1ui3/xv30ERQ9w7Hy4lXc8VR1PecrtlObIIIIAAAggggAACCCCAQGYEKMhlxpFWENhqgc0Lcu3bt0+9uEHttVZ5qIKUeoR01KhRss0221SG07+qR1crKipS31XB7vzzz0/nFi5cmLprrfJNsUcccUTq7rb0CVv48Mcff8gZZ5xR5U65ytNVn+puu6uvvlpUobDymDx5sqj97tS1mx7qxRjqbjd1J1zlMX369NS5uqKfKvSddtppqQKcw+FIXfLBBx+k7rhTb3NVx+Z3CqaC/F+1AtGvyiQy+56qecMhnt16i+dvZ4mjUZuqOb4hgAACCCCAAAIIIIAAAghkVICCXEY5aQyBugtsXpBTRSf1OKfaQ23VqlWpAlaLFi3q3oF15auvvpp6oYNqRL0IQu37VptD7WM3b948+eabb0SNpVOnTqlxqaKZ7lDFODWvpUuXpop1Bx54oOy+++5V9oirvC6RSKTmqh57rTxfvalVGfj9/srT0r9+//33qfkce+yxqTbTCT78pUBixTwJvnqh9jzD4RZ3pxOlZL/TxQj8WWDVnkwQAQQQQAABBBBAAAEEEECgTgIU5OrExkUIZF5AV5Dr3r17RjtSd7Hdcccd4vP5RBW01JtTOYpPwAz9IRufP3aLEzecJeLea4CU7DNUDF+TLZ5LEgEEEEAAAQQQQAABBBBAoHYCFORq58XZCGRNINsFuc8//1y6deuWujvt6aefliFDhmRtLjSc+wIbRx8tZmT9Xw7UcPvF02WwePY+VQxP4C/P5wQEEEAAAQQQQACBLQuo7VjUD8fVMXLkyCovQ9vylWQRQKCQBCjIFdJqMpe8Fsh2Qe62224T9TioukNOPQrKUdwCoVcukPivn9cYwShpLCX7DhX3ngPEcHtrfB0nIoAAAggggAACCPwpEI/HpbS0NL3v8wsvvCCDBw/+8wQ+IYBA0QhQkCuapWaiuS6Q7YJcrs+f8dWvQOS9u6w3rU6udacOfwvxWPvLeTqdJOJ01/p6LkAAAQQQQAABBApRQL1sbMWKFdKmTRtxOp3VTvGrr76Szp07p/PqhWubfk8nqvlQ2Y/aY7l58+bVnEUYAQTyQYCCXD6sEmMsCgEKckWxzDkzyehXE603rY6q43gM8R48UjydT6nj9VyGAAIIIIAAAggUhsATTzwhU6ZMkY8++kjWrVuXehnZ/vvvn3p5mnqhmTp++eUXueyyy9Kf1cvbKo8TTjhBSkpKZIcddpBRo+x/N0smkzJ69Gh577335Ouvv5aFCxdKMBhMXa6uu/XWW+WKK66obI5fEUAgjwQoyOXRYjHUwhagIFfY65trs4uv+FRCr46o9bDUHXLeXjeKa4cDan0tFyCAAAIIIIAAAoUiEA6H5eyzzxb1yKnu8Hg88vjjj8sZZ5whb775phx77JZfqKWKd6qot+kxf/58Offcc+WTTz7ZNFzl8zPPPCNnnnlmlRhfEEAgPwQoyOXHOjHKIhBQt6ufc8456Zk+99xzsvvuu6e/8wGBTAqYwTWyccxxtWrStcNB4ut1gxj+ZrW6jpMRQAABBBBAAIFCEkgkEtKjRw+ZM2dOelrt27eXnXfeWebNmydr165NxX0+n3z77bcyY8aMVPFOBdW1lYdhGOJwOFJfTz31VHn++ecrU6L2f77ppptE7Tm3+dGiRQuJxWKyYcMGmT17thx88MGbn8J3BBDIAwEKcnmwSAwRAQQQyIbAxmePErNiQ42a9na7yHrTqvVmXusvjhwIIIAAAggggEAxC6jHVM8///w0wTXXXCM333xzau+43377LVWsU4U4dQwfPjz1yGnlya1atZLff/899fWBBx6QSy65pDKV/nXq1Kly4oknpr+rD4ceeqhcd911svfee0vr1q1TuY0bN6Yekd3SnnVVGuELAgjklAAFuZxaDgaDAAII1J9AaMp5El85v0YdqmKc96DaP+Jao8Y5CQEEEEAAAQQQyBOB8vJy2WWXXWTVqlWpEffp00deffXVKqNXxbkbb7wxFdtxxx3lxx9/TH1W12y77bbpc2fOnCm9evVKf1cf1H5zquhWWbTzer1y//33px5dVXfUcSCAQOEIUJArnLVkJggggECtBCKz7pDowik1vMaQwAmPinO7fWt4PqchgAACCCCAAAKFJ/Dkk0/Keeedl57YVVddJQccUHVv3blz58odd9yROkcV0dRLGNTjq+rR1SOPPDJ9rbqbrmXLlunv6sOwYcNkzJgx6dhDDz0kI0bwQ9E0CB8QKCABCnIFtJhMBQEEEKiNQHTBSxL54N4aX+IItJbAgLFieBvX+BpORAABBBBAAAEEMiGgHu9csWJFtU2pt5RefPHF1eYzlbj00kvlwQcfrFVzS5YskQ4dOoiaQ+XbVrfZZhtZuXKlrZ127drJTz/9lIqrF0FMmzbNdg4BBBAoDAEKcoWxjswCAQQQqLVA/Oe5Enqt6l9cna33EsMTkPjPH2vbc+/UQ3zH3K3NEUQAAQQQQAABBLIlsMcee8jChQurbX6vvfaSBQsWVJvPVOKYY46Rt956q1bNrV69WtTeceoFbk8//XTqWnWn3PTp06u0owqObdu2TcfKysqkX79+6e98QACBwhKgIFdY68lsEEAAgRoLmMHfrTet9vn/5xtSsu8wKel6rpiR9RKceJokw39o2/J2v0w8XQZpcwQRQAABBBBAAIFsCORKQU69SXXp0qWpKaqXLKg75v7qUG9FVY+uHnTQQfLRRx+lTh85cqTce2/VJxUmT55cpQCn9p5Te9BxIIBAYQpQkCvMdWVWCCCAQI0ENv7nSDHcXvEefpO4tt8/fU18+UcSmjbS+m6mY5UfDKdHAv1Gi6N5h8oQvyKAAAIIIIAAAlkVUHfAff3119X20blzZ/nyyy+rzWcq0alTJ1m0aFGquXvuuUcuv/zyGjVtmqY0adJE1JtR1fHMM8/ImWeeWeXacePGyZAh1lvt//+xbt261DWV3/kVAQQKS4CCXGGtJ7NBAAEEaiVQ8dmz4tnjZDF8TW3XVXz8uFR8PtoWVwGnVYwL9H1WxFWizRNEAAEEEEAAAQQyKbB+/XqJRCLVNqneRqoKXtk+TjjhhPRbVS+44AJ59NFHa9TlsmXLZKeddkqf+8knn0jXrl3T39WHjz/+WLp165aOqe+bvzAineQDAgjkvQAFubxfQiaAAAIIZEkgEZPgy2dL4vdvtR14du8j3p7XaXMEEUAAAQQQQACBQhRQd8RVPmq63Xbbpe6Wa9SokXaqqgincs2bN5fXX39d+vSp3CpEUnfKlZaWVrluw4YNVYqKgwcPlhdeeKHKOXxBAIHCEaAgVzhryUwQQACBjAsk1/8kwbJhYsbC2rb9R98prvaHaXMEEUAAAQQQQACBQhOYNWuW9OzZMz2toUOHylNPPSUlJX8+NRAOh+XOO++Uu+66S8aOHSt9+/ZN3Ul30UUXpa/74Ycf0nfMzZ07V3bZZRdp1qxZ6q65Tz/9NH3e3XffnXosVu1BV3ksXrw4VRRUd+h16dJFVH+33nqrTJ06VXr37i3XX3+9bF7sq7yWXxFAIHcEKMjlzlowEgQQQCAnBWLfvi7hd27Rjs0oaSyBAWPFUdpamyeIAAIIIIAAAggUmsBJJ50kr7zySnpaan+7QYMGSePGjUUV195++2359ddfU3n15leVf/HFF1PnVF7UsWNH6dGjR2pfPPWih5dffllUu+pRVvXyh2QyWXmqqBdaVD66qvavU+eo/Icffpg6VxUEzz333PT5Dz30kIwYMSL9nQ8IIJCbAhTkcnNdGBUCCCCQUwKRd2+V6KLXtGNytdlX/Cc8ImI4tHmCCCCAAAIIIIBAIQksWbJEDj/8cFm+fPkWp6UKdKtXr07dPbdixQrZbbfdJBgMaq+ZOXOm9OrVK5W74YYb5JZb9D8M3fTin3/+Wbbffnu5+OKL5eGHH06nhg8fLqNHj05/5wMCCOSmAAW53FwXRoUAAgjklIAZDaYeXU1uWKEdV8m+p0vJgedrcwQRQAABBBBAAIFCE/jjjz/kjDPOqHKnXOUc1QsmLrzwQrn66qulZcuWlWGZPHmynH322aKu3fRQL6NQd7upO+Eqj+nTp6fO1RX9VKHvtNNOSxXhHA6HfPDBB6m77dSbXNUxY8aMVMGwsi1+RQCB3BSgIJeb68KoEEAAgZwTSKz6WkJTzhXTTNjHZt0dFzjxcXFu28WeI4IAAggggAACCBSowMqVK2XevHnyzTffSIsWLaRTp06pwpoqmukOVYx75513ZOnSpali3YEHHii77767bLpHXOV1iURC1H5x6rHXyvPVm1rVI61+v7/ytNSv33//fertr8cee2yqvSpJviCAQE4KUJDLyWVhUAgggEBuCkS/HC+RDx/QDs5Ruk1qPzmjRP+mMe1FBBFAAAEEEEAAAQQQQACBIhSgIFeEi86UEUAAgToLWI9ChF67WOIr/nz716ZtuTscLr6j/r1piM8IIIAAAggggAACCCCAAAKbCVCQ2wyErwgggAACWxYwg79L+cQhYkbWaU/0HnK5ePYaoM0RRAABBBBAAAEEEEAAAQQQsN6JZ238+L+dH9FAAAEEEECghgLxZR9I6I3LtWcbLq8E+j8njqY7avMEEUAAAQQQQAABBBBAAIFiF6AgV+z/BTB/BBBAoI4CkTkPS3T+WO3Vzua7SqDff0Scbm2eIAIIIIAAAggggAACCCBQzAIU5Ip59Zk7AgggsDUCiZgEJ50pibWLta14Op0k3sP+T5sjiAACCCCAAAIIIIAAAggUswAFuWJefeaOAAIIbKVAct0yCZYNFzMe0bbkP+Yece10iDZHEAEEEEAAAQQQQAABBBAoVgEKcsW68swbAQQQyJBA7Ls3JDzzX9rWDG9TKR0wRoxAK22eIAIIIIAAAggggAACCCBQjAIU5Ipx1ZkzAgggkGGB8PRrJbZkhrZV13b7i//4B63XCDm0eYIIIIAAAggggAACCCCAQLEJUJArthVnvggggEAWBMyKcuvR1SGS3LhS23rJ386Skq7naHMEEUAAAQQQQAABBBBAAIFiE6AgV2wrznwRQACBLAqMbhsAAD3bSURBVAkkVn4pwVfOFzGTth4Mwyn+k54U5zZ72nIEEEAAAQQQQAABBBBAAIFiE6AgV2wrznwRQACBLApEvxgjkY8e0fbgaLSdBNR+cp6ANk8QAQQQQAABBBBAAAEEECgWAQpyxbLSzBMBBBCoDwHr7rjQ1Isk/uvn2t7cuxwlviNv1uYIIoAAAggggAACCCCAAALFIkBBrlhWmnkigAAC9SSQLF8twYlDxKzYoO3Re+hV4tnjZG2OIAIIIIAAAggggAACCCBQDAIU5IphlZkjAgggUM8C8R/ek9BbV2p7NVxeCfR/XhxN22nzBBFAAAEEEEAAAQQQQACBQhegIFfoK8z8EEAAgQYSiHxwv0QXTND27my5mwROflrE6dbmCSKAAAIIIIAAAggggAAChSxAQa6QV5e5IYAAAg0pEI9KcNLpkvhjqXYUnj37i7fHFdocQQQQQAABBBBAAAEEEECgkAUoyBXy6jI3BBBAoIEFkmuXWkW5M8RMVGhGYoi/973ianeQJkcIAQQQQAABBBBAAAEEEChcAQpyhbu2zAwBBBDICYHYotck/O6t2rE4fM0kMGCcGP7m2jxBBBBAAAEEEEAAAQQQQKAQBSjIFeKqMicEEEAgxwTCb10lsR9maUflanug+I+7X8QwtHmCCCCAAAIIIIAAAggggEChCVCQK7QVZT4IIIBADgqYkQ0SnDhEksHV2tGVdD1PSv52hjZHEAEEEEAAAQQQyLaAGV4nkTkPiO/wG7PdFe0jgAACKQEKcvyHgAACCCBQLwKJXz6X4NQLrb5MW3+GwyX+k54SZ+tOthwBBBBAAAEEEEAgmwKx79+WyOx7rLv1HdJo+LRsdkXbCCCAQFqAglyagg8IIIAAAtkWqJg3Wio+eVzbjaNxWwn0f14Mj1+bJ4gAAggggAACCGRSwAytlcj7d6a31XA0aSelg1/KZBe0hQACCFQrQEGuWhoSCCCAAAIZF0gmJPjKBZJY9aW2aXfHY3lURCtDEAEEEEAAAQQyKRD77i2JfDBKzIoN6WadrTpJoN+z6e98QAABBLIpQEEum7q0jQACCCBgE0huXJnaT86MlttyKuDrea24dz9emyOIAAIIIIAAAghsjYAZ/F3C790h8WWzbc24tu8q/uMfssUJIIAAAtkQoCCXDVXaRAABBBDYokBsyUwJT79Ge47h9ktgwBhxNN5emyeIAAIIIIAAAgjURSD27evWXXH3SXU/FHR36CW+o26vS9NcgwACCNRagIJcrcm4AAEEEEAgEwKR90dJ9OuJ2qZSj4xYL3kQp0ubJ4gAAggggAACCNRUIFm+WiKzbpf4T3O2eInHukPfa92pz4EAAgjUhwAFufpQpg8EEEAAAbtAvEKCZcMlse5He86KeDoPEu/Bl2lzBBFAAAEEEEAAgZoIxBZNlciHD1Z7V9ymbfB3j001+IwAAtkWoCCXbWHaRwABBBCoViCx5nsJTTpDzGRMc44h/j4PiqttV02OEAIIIIAAAgggUL2A2rM2dVfczx9Xf9JmmZL9zxb1Pw4EEECgPgQoyNWHMn0ggAACCFQrEP1mikSszZV1h8PfwtpPbpwYvqa6NDEEEEAAAQQQQKCqgGlKdOEUqZjzkJixUNXcX3zzdr9MPF0G/cVZpBFAAIHMCFCQy4wjrSCAAAIIbIVA6I0rtG87U0262nUXf+97t6J1LkUAAQQQQACBYhBIbvxVIu/eJvEVn9Zpur6e11lveu9Tp2u5CAEEEKitAAW52opxPgIIIIBAxgXM8HprP7nTJBn8Xdu2t9tF4tlnqDZHEAEEEEAAAQQQUAKJFfOst6jeI4m1S+sE4j/qDnF16Fmna7kIAQQQqK0ABbnainE+AggggEBWBOIrPpPQqyOstk1b+4bDLf6+z4izZUdbjgACCCCAAAIIIJAWSCYk+s1kqZj7lJgVG9Lhmnzw93mIvWtrAsU5CCCQEQEKchlhpBEEEEAAgUwIVHz6tKj/6Q5H03YS6PecGG6fLk0MAQQQQAABBBBIC5iRDamiXMwqzplmIh3f0odA32fF2brTlk4hhwACCGRMgIJcxihpCAEEEEBgqwWsn2oHp5wnidVfaZvyWPu6eK39XTgQQAABBBBAAIG/FFB/r3jpNEms+/EvT1UnlA6eKI4mO9ToXE5CAAEEtlaAgtzWCnI9AggggEBGBZIbf7H+8jzUejNaUNuu7/Abxd3xWG2OIAIIIIAAAgggUCkQXfCStadczV8M1Wj4m7zZvRKPXxFAIOsCFOSyTkwHCCCAAAK1FYgtni7hGddrLzM8pRIYMEYcjdpo8wQRQAABBBBAAAGzYqOUjx8gZmRdjTEan/O+iNNd4/M5EQEEENgaAQpyW6PHtQgggAACWROIzLpTogtf1rbvbL2XBE56QsTh1OYJIoAAAggggEBxC0Tev0eiX5dpEbyHXC7JNYutv2e8auX/9zIpw+mRRue8pz2fIAIIIJANAQpy2VClTQQQQACBrRYwY2EJlg2X5Prl2rY8e58m3oMu1uYIIoAAAggggEDxCiTXLUvtHWcm4zYEZ+s9JXCy9QIpw5DE799JxQf3SfzXz61HVZtLo+HTbOcTQAABBLIlQEEuW7K0iwACCCCw1QLqL8qhyWeJmYxp2jLEf/zD4tr+b5ocIQQQQAABBBAoVoHQ6yMl/tMczfSNVDHOuc2eVXKxJTMktnCq+Ps8UCXOFwQQQCCbAhTksqlL2wgggAACWy0Q/apMIrPv0bbjCLSUQP9x1k+1m2jzBBFAAAEEEECguATiP7wvobf+qZ20Z/fjrbe1X6vNiWk9umrdNceBAAII1JcABbn6kqYfBBBAAIG6CVh/QQ5N+0c1P+kWce/UQ3zH3F23trkKAQQQQAABBApHIJmQ8pdOFfXI6uaH4fJK6eCJYgRabZ7iOwIIINAgAhTkGoSdThFAAAEEaiNghtdJcOJpkgyt0V7mPegS8ex9qjZHEAEEEEAAAQSKQyD6xViJfPSwdrLebheJZ5+h2hxBBBBAoCEEKMg1hDp9IoAAAgjUWiD+0ycSev1S67r/vQ1t0wbUm9H8ff8jzha7bBrmMwIIIIAAAggUiYD64V35+P5iRsttM3Y02lZKB74k4vLYcgQQQACBhhKgINdQ8vSLAAIIIFBrgYpPHpeKeaO11zmb7mTtJ/ec9ZftEm2eIAIIIIAAAggUrkDk3dskuuhV7QT9R98prvaHaXMEEUAAgYYSoCDXUPL0iwACCCBQe4FEXIJTzpHEbwu117p3O058va7X5ggigAACCCCAQGEKJNd8L+Vlw6yb6JO2Cbra7Cv+Ex+zxQkggAACDS1AQa6hV4D+EUAAAQRqJZDcsMLaT26omLGQ9jrf328X9869tDmCCCCAAAIIIFB4AqGpIyT+y6eaiRmpu+edLTtqcoQQQACBhhWgINew/vSOAAIIIFAHgdjiNyU84ybtlUZJI+sv32NE7RfDgQACCCCAAAKFLRD7/m0Jv32ddpKePfqJ99B/anMEEUAAgYYWoCDX0CtA/wgggAACdRLY0l4xrm33Fv8Jj4o4nHVqm4sQQAABBBBAIA8EEjEpf3GQqLvnNz8Md0BKB5eJ4W+2eYrvCCCAQE4IUJDLiWVgEAgggAACtRUwoyEJlg3V/iVctVWy73ApOfCC2jbL+QgggAACCCCQJwIVn/1HKuY+qR2tt/tI8XQZqM0RRAABBHJBgIJcLqwCY0AAAQQQqJNAYvVCCVkveTCTcfv1hkMC1l1yzjb72HNEEEAAAQQQQCCvBczgGimfMEC7p6yj8Q5SOvAFEac7r+fI4BFAoLAFKMgV9voyOwQQQKDgBaJfTpDIh/dr5+ko3Sa1n5zhbazNE0QAAQQQQACB/BRQe8mqPWV1h/+Ye8S10yG6FDEEEEAgZwQoyOXMUjAQBBBAAIE6CZimhF6/VOI/f6K93N2+p/iOvkObI4gAAggggAAC+Seg7pAPTj7TGrhpG7yrXXfx977XFieAAAII5JoABblcWxHGgwACCCBQawH12EqwbIgkw39or/Ue/A/xdD5FmyOIAAIIIIAAAvklEHz5HEmsWmAbtGE4JXDKWHE0a2/LEUAAAQRyTYCCXK6tCONBAAEEEKiTQHz5HAlN+4d1rf2n5YazRAL9nhVH8w51apuLEEAAAQQQQCA3BGLfTZPwzJu1g/F0HiTegy/T5ggigAACuSZAQS7XVoTxIIAAAgjUWSAy52GJzh+rvd7ZfGcJ9H1WxOXR5gkigAACCCCAQI4LxCusFzmcIsnyVbaBGiWNpXRwmbBvrI2GAAII5KgABbkcXRiGhQACCCBQB4FEzNpT5ixJrPlOe7Gn04niPexqbY4gAggggAACCOS2QMUnj0vFvNHaQXoPvUo8e5yszRFEAAEEclGAglwurgpjQgABBBCos0By3XJrP7lhYsYj2jb8R98lrvaHanMEEUAAAQQQQCA3BdRdccHxp4iZqLAN0NG0vZRae8eJw2nLEUAAAQRyVYCCXK6uDONCAAEEEKizQGzRaxJ+91bt9eqRlsCAceIobaXNE0QAAQQQQACB3BMI//caiS2dqR2Y/7gHxbXDAdocQQQQQCBXBSjI5erKMC4EEEAAga0SCM/8l8S+e0PbhqvNvuI/4RERw6HNE0QAAQQQQACB3BFIrPxSglPO1Q7I3b6n+I6+Q5sjiAACCOSyAAW5XF4dxoYAAgggUGcBMxqU4MShktz4i7aNkr+dJSVdz9HmCCKAAAIIIIBAjgiYprU/7JmS+G2hbUCGwy2BgePF0aStLUcAAQQQyHUBCnK5vkKMDwEEEECgzgKJVV9JaMp5YpoJexvW3XGBEx8X57Zd7DkiCCCAAAIIIJATArGFr0h41u3asZTsO1xKDrxAmyOIAAII5LoABblcXyHGhwACCCCwVQLRL8ZJ5KOHtG04Gm0rgf5jxSgp1eYJIoAAAggggEDDCZjRkAQnDJBkaI1tEA5fMwkMLhPDE7DlCCCAAAL5IEBBLh9WiTEigAACCNRdwExK6NVLJP7Lp9o23DsfIb6/36bNEUQAAQQQQACBhhOIfPigRL98QTsAX6/rxb3bcdocQQQQQCAfBCjI5cMqMUYEEEAAga0SMIO/Sbm1n5wZWadtx9vjSvHs2VebI4gAAggggAAC9S+Q3LDCujtukJjJmK1zZ4uO1h3uo3k5k02GAAII5JMABbl8Wi3GigACCCBQZ4H4j7Ml9OYV2usNl9f6i/1z4mi6ozZPEAEEEEAAAQTqVyD0xhUSXzZb22nghMfEud2+2hxBBBBAIF8EKMjly0oxTgQQQACBrRaIfPiA9ejLeG07qZ+2931GxOnW5gkigAACCCCAQP0IxFd8am03MULbmXvXo8V3xL+0OYIIIIBAPglQkMun1WKsCCCAAAJbJ5CISXDSGZJY+722Hc8e/cR76D+1OYIIIIAAAgggUA8CyYQEy4ZZf1YvsXVmOD0SGPSiOBq1seUIIIAAAvkmQEEu31aM8SKAAAIIbJVA8o8fraLc6WLGI9p2/MeOEteOB2tzBBFAAAEEEEAguwLRBS9J5IN7tZ2U7H+2qP9xIIAAAoUgQEGuEFaROSCAAAII1Eog9u3rEn7nFu01hreplA4YK0agpTZPEAEEEEAAAQSyI2BWbJTy8QO0L2FyBFpZd8e9JIbbl53OaRUBBBCoZwEKcvUMTncIIIAAArkhEP7vNRJbOlM7GNf2+4u/z0PW29sMbZ4gAggggAACCGReIPL+PRL9ukzbsO+IW8S969+1OYIIIIBAPgpQkMvHVWPMCCCAAAJbLaB+Ch+cOESS5au0bZV0PU9K/naGNkcQAQQQQAABBDIrkFy3TIIvnSZmMm5r2Nl6Twmc/DQ/KLPJEEAAgXwWoCCXz6vH2BFAAAEEtkogsfJLCb5yvlh/+7e1YxhO8Z/8lDhb72HLEUAAAQQQQACBzAqEXh8p8Z/maBo1UsU45zZ7anKEEEAAgfwVoCCXv2vHyBFAAAEEMiBQMe85qfjkMW1LjsbbS6D/82J4Ato8QQQQQAABBBDYeoH4D+9L6C39W849u/cRb8/rtr4TWkAAAQRyTICCXI4tCMNBAAEEEKhnAevuuNDUiyT+6+fajt27Hi2+I/6lzRFEAAEEEEAAga0USMSlfOKpkly33NaQ4fJK6eCJ1ouWWtlyBBBAAIF8F6Agl+8ryPgRQAABBLZaIFm+OrWfnFmxQduWr+c14t79BG2OIAIIIIAAAgjUXSD6xTiJfGS9SElzlBx4kZTsO1STIYQAAgjkvwAFufxfQ2aAAAIIIJABgfjSdyX03//TtmS4falHVx1NdtDmCSKAAAIIIIBA7QXM8DopH99fzGi57WJHozZSOvBFEZfHliOAAAIIFIIABblCWEXmgAACCCCQEYHI7FES/Wqiti1ny92tTaWfEnG6tXmCCCCAAAIIIFA7gcisf0t04VTtRf6j7xRX+8O0OYIIIIBAIQhQkCuEVWQOCCCAAAKZEYhXSHDyGZJYu1TbnmevU8R7yD+0OYIIIIAAAgggUHOBxJrvJVg2TPumc1ebfcV/ov6FSzXvgTMRQACB3BagIJfb68PoEEAAAQTqWSC5dokEJ50hZiKq6dkQf+/7xNWumyZHCAEEEEAAAQRqKpB6odIvn2lON6xtIp4TZ8uOmhwhBBBAoHAEKMgVzloyEwQQQACBDAnEFr4i4Vm3a1szfM2ldMBYMfzNtXmCCCCAAAIIILBlgdiSGRKefq32JM8efcV76JXaHEEEEECgkAQoyBXSajIXBBBAAIGMCYTevEriP87StufaoVvqTjkxDG2eIAIIIIAAAghUI5CISfmLgyS5YYXtBMMdkNLBZdYPvZrZcgQQQACBQhOgIFdoK8p8EEAAAQQyImBGNkhw4hBJBldr2/MeeKF49rX2vuFAAAEEEEAAgRoLVHz2rFTMfUJ7vrf7SPF0GajNEUQAAQQKTYCCXKGtKPNBAAEEEMiYQOKXzyU49UKrPdPWpuFwi99666qz1e62HAEEEEAAAQQQsAuYwTVSPqG/mLGwLelovIOUDhxvvc3cZcsRQAABBApRgIJcIa4qc0IAAQQQyJhAxafPSMWnT2nbU/94UBtPGx6/Nk8QAQQQQAABBP4UCM+4SWKL3/wzsMkn/zH3iGunQzaJ8BEBBBAobAEKcoW9vswOAQQQQGBrBZIJCb5yviRWLdC25O7YW3yH36DNEUQAAQQQQACB/wkkVi+U4OQzrS/2u85d7bpbe7PeCxUCCCBQVAIU5IpquZksAggggEBdBJIbf7X2kxsqZrRce7mv1/Xi3u04bY4gAggggAACCIgEXz5H+8Mtw3BK4JSx4mjWHiYEEECgqAQoyBXVcjNZBBBAAIG6CsSWzJDw9Gu1l6u3wgUGPC+Oxttr8wQRQAABBBAoZoHYd29IeOa/tASezoPEe/Bl2hxBBBBAoJAFKMgV8uoyNwQQQACBjApE3rtLot9M1rbpbLWHBKyXPIjDqc0TRAABBBBAoBgFzFhEgi8OlGT5Ktv0jZLGUjq4TAxvY1uOAAIIIFDoAhTkCn2FmR8CCCCAQMYEUv+omDRckuuWadv0dDlVvN0v0eYIIoAAAgggUIwCFZ88LhXzRmun7u1xpXj27KvNEUQAAQQKXYCCXKGvMPNDAAEEEMioQGLNYglNOlPMZEzTriH+Pg+Kq21XTY4QAggggAACxSWg7ooLjj9FzESFbeLO5h2sN5WP4c5ymwwBBBAoFgEKcsWy0swTAQQQQCBjAtGvJ0nk/bu17Tn8Laz95MaJ4WuqzRNEAAEEEECgWATU3qtqD1bd4T/O+gHWDgfoUsQQQACBohCgIFcUy8wkEUAAAQQyLRB64wqJL5utbdbV7mDx9x6lzRFEAAEEEECgGAQSK7+U4JRztVN1tz9MfEffqc0RRAABBIpFgIJcsaw080QAAQQQyKiAGV4nwYmnSTK0Rtuu96BLxLP3qdocQQQQQAABBApawDQlOOkMSfy+yDZNw+GWwMDx4mjS1pYjgAACCBSTAAW5Ylpt5ooAAgggkFGB+IpPJfTqxVabpq1d9Q8Of99nxNmyoy1HAAEEEEAAgUIWiC18RcKzbtdOsWSfYVLS7UJtjiACCCBQTAIU5IpptZkrAggggEDGBSo+ecJ6e9yz2nYdTXeUQL/nxHB7tXmCCCCAAAIIFJqAGQ1JcMIA7R3kDl8zCQwuE8MTKLRpMx8EEECg1gIU5GpNxgUIIIAAAghsIpBMpPbISaz+epPgnx/du/UWX68b/gzwCQEEEEAAgQIWiMx5SKLzx2ln6Ot1vbh3O06bI4gAAggUmwAFuWJbceaLAAIIIJBxgeTGXyT40lAxY0Ft274jbxX3LkdqcwQRQAABBBAoFIHkhhXW3XGDxEzGbFNytugogf6jRQyHLUcAAQQQKEYBCnLFuOrMGQEEEEAg4wKxxf+V8Az9nXCGp1QCA8aIo1GbjPdLgwgggAACCOSKwJbeQB448XFxttknV4bKOBBAAIEGF6Ag1+BLwAAQQAABBApFIPLubRJd9Kp2Os5tOov6x4g4nNo8QQQQQAABBPJZ4H8vOhqhnYJ7l6PEd+TN2hxBBBBAoFgFKMgV68ozbwQQQACBjAukNrIuGy7JDT9p2+bNcloWgggggAAC+S6g9lMtGyaJtUtsMzGcHgkMepG7xG0yBBBAoNgFKMgV+38BzB8BBBBAIKMCid++ldDLZ2v3z7E2zpHA8Y+Ic/v9MtonjSGAAAIIINCQAtGvJkpk9ijtEEr2P1vU/zgQQAABBKoKUJCr6sE3BBBAAAEEtloguuAliXxwr7YdR6CVtan1WDF8TbR5gggggAACCOSTgFmxUcrHDxAzss427NSfeYNeEsPts+UIIIAAAsUuQEGu2P8LYP4IIIAAApkXME0JTRsp8Z8+0rbt3ulQ8R1zlzZHEAEEEEAAgXwSiLw/SqJfT9QO2XfELeLe9e/aHEEEEECg2AUoyBX7fwHMHwEEEEAgKwJm6A8pn3iamOG12va9B/9DPJ1P0eYIIoAAAgggkA8CyXXLJPjiqWKaCdtwna33ksDJT1m7NRi2HAEEEEAAAeu3R9M6gEAAAQQQQACBzAvEf/pYQq9fZjVs/6M2tcl13/+Io8Uume+YFhFAAAEEEKgHgdDr6m7wOZqerD1T+z4jztZ7aHKEEEAAAQSUAAU5/jtAAAEEEEAgiwIVHz0qFV88r+3B0bS9lPYfLeIq0eYJIoAAAgggkKsC8R9nS+jNK7TD8+zeR7w9r9PmCCKAAAII/E+Aghz/JSCAAAIIIJBNgURcglPOkcRvC7W9eDqdIN7DrtHmCCKAAAIIIJCTAtafbeUTT5XkuuW24Rkur5QOniiG9RIjDgQQQACB6gUoyFVvQwYBBBBAAIGMCCTX/yzBsqFixsLa9vxH3SGuDj21OYIIIIAAAgjkmkD0i3ES+egh7bC8B14onn2HaXMEEUAAAQT+FKAg96cFnxBAAAEEEMiaQOy7aRKeebO2faOkkQQGjBVH6TbaPEEEEEAAAQRyRcAMr5Py8f3FjJbbhuRo1EZKB75obcXgseUIIIAAAghUFaAgV9WDbwgggAACCGRNIPzOzRL7dpq2fde2+4j/hEdEHE5tniACCCCAAAK5IBCZ9W+JLpyqHQp3fGtZCCKAAAJaAQpyWhaCCCCAAAIIZF7AjIZSj64mN6zQNl6y35lScsC52hxBBBBAAAEEGlogseZ7688x63FUM2kbiqvNvuI/8TFbnAACCCCAgF6AgpzehSgCCCCAAAJZEUis/kZCU861/i0Tt7dvOCRwwmPibLO3PUcEAQQQQACBBhYITb1I4r98phmFIYH+z4mzZUdNjhACCCCAgE6AgpxOhRgCCCCAAAJZFIjOf0Eicx7U9qD2kQv0HyOGt7E2TxABBBBAAIGGEIgtmSHh6ddqu/bs0Ve8h16pzRFEAAEEENALUJDTuxBFAAEEEEAgewKmKaHXLpH4irnaPtwdeonvqNu1OYIIIIAAAgjUu0AiJuUvDhLdlguGp1RKB5eJ4Wta78OiQwQQQCCfBSjI5fPqMXYEEEAAgbwVMINrpHziaWJG1mnn4O1xpXj27KvNEUQAAQQQQKA+BSo+e1Yq5j6h7dLb/TLxdBmkzRFEAAEEEKhegIJc9TZkEEAAAQQQyKpAfNmHEnrjH9o+DGeJ9ejqaHE0a6/NE0QAAQQQQKA+BMzg71I+YYCYsbCtO0fjHaR04HgRp8uWI4AAAgggsGUBCnJb9iGLAAIIIIBAVgUiHz4o0S9f0PbhbL6LBPr+R8Tl0eYJIoAAAgggkG2B8Mx/Sey7N7Td+HvfJ652B2lzBBFAAAEEtixAQW7LPmQRQAABBBDIroC1L09w8lmSWPOdth82ytayEEQAAQQQqAeBxOqF1p9RZ1o9mbbeXDscJP7j7rPFCSCAAAII1EyAglzNnDgLAQQQQACBrAkk1y2TYNlwMeMRbR/+o+8WV/se2hxBBBBAAAEEsiUQfPkcSaxaYGveMJwSOGUs2yrYZAgggAACNRegIFdzK85EAAEEEEAgawKxb1+X8Du3aNs3vE2s/eSsf/iUttLmCSKAAAIIIJBpgdjiNyU84yZts57Og8R78GXaHEEEEEAAgZoJUJCrmRNnIYAAAgggkHWB8PTrJLbkbW0/ru3+Jv7jHxIxHNo8QQQQQAABBDIlYMYiEpxwiiSDq21NGiWNpXRwmRjexrYcAQQQQACBmgtQkKu5FWcigAACCCCQVQGzotx6dHWoJDf+qu2nZP9zpGT/s7Q5gggggAACCGRKoOKTJ6Ri3rPa5rw9rhTPnn21OYIIIIAAAjUXoCBXcyvORAABBBBAIOsCiZULJPTK+WKaCVtfas8e/4mPi3PbzrYcAQQQQAABBDIhkCxfJcHxp4iZqLA152zewdpCYYyIw2nLEUAAAQQQqJ0ABbnaeXE2AggggAACWReIfv68RD5+VNuPo1Gb1D+GjJJSbZ4gAggggAACWyMQnn6ttX3CDG0T/j4PiattV22OIAIIIIBA7QQoyNXOi7MRQAABBBDIvoCZlNCrIyT+yzxtX+6djxTf32/V5ggigAACCCBQV4HEyi8lOOVc7eXu9oeJ7+g7tTmCCCCAAAK1F6AgV3szrkAAAQQQQCDrAsny36z95IaIGVmv7ct32NXi7nSiNkcQAQQQQACBWguYpgQnnSGJ3xfZLjUcbgkMHC+OJm1tOQIIIIAAAnUToCBXNzeuQgABBBBAIOsC8R/el9Bb/9T2Y7i81qOrz4mj6Y7aPEEEEEAAAQRqIxBb+IqEZ92uvaRkn2FS0u1CbY4gAggggEDdBCjI1c2NqxBAAAEEEKgXgcjseyX61UvavpwtOkqg7zMiTrc2TxABBBBAAIGaCJjRkAQnDJBkaI3tdMPXXEoHTxTDE7DlCCCAAAII1F2Aglzd7bgSAQQQQACB7AvEoxKcbD1CtHaJti/PXgPEe8jl2hxBBBBAAAEEaiIQmfOQROeP057q63mduHfvo80RRAABBBCouwAFubrbcSUCCCCAAAL1IpD84wdrP7nTxUxUaPozxH/sKHHt2F2TI4QAAggggMCWBZIbVlh3xw0SMxmznZi6E7v/aBHDYcsRQAABBBDYOgEKclvnx9UIIIAAAgjUi8CW9vYxvE2ldMA4MQIt6mUsdIIAAgggUDgC4Tf/KbEf39dOKHDi4+Jss482RxABBBBAYOsEKMhtnR9XI4AAAgggUG8C4f9eLbGl72j7c23fVfx9HrTuYjC0eYIIIIAAAghsLhBf8amEXh2xeTj13b3L38V35C3aHEEEEEAAga0XoCC39Ya0gAACCCCAQL0ImJEN1qOrQyVZvkrbX8kBF0jJfsO1OYIIIIAAAghUEUgmrD9Thmn3KDWcHgkMelEcjdpUuYQvCCCAAAKZE6AglzlLWkIAAQQQQCDrAolf50tw6gVibfZj68twuMR/0pPibL2HLUcAAQQQQACBTQWiX02UyOxRm4bSn0v+dpaUdD0n/Z0PCCCAAAKZF6Agl3lTWkQAAQQQQCCrAhWf/Ucq5j6p7cPReHsJ9B8jhsevzRNEAAEEEEDArNgo5eP7ixlZb8NwBFpZd8e9JIbbZ8sRQAABBBDInAAFucxZ0hICCCCAAAL1I2A9ZhSaepHEV36h7c+969HiO+Jf2hxBBBBAAAEE1J1x6g453eE74mZx73qULkUMAQQQQCCDAhTkMohJUwgggAACCNSXgNpHLjhxiKi7HHSHr9f14t7tOF2KGAIIIIBAEQsk1y2T4IunimkmbArO1ntJ4OSneEGQTYYAAgggkHkBCnKZN6VFBBBAAAEE6kUgvvRdCf33/7R9qUeN1KOrjiZttXmCCCCAAALFKRB6/TKJ//SRZvKGBPo+wz6kGhlCCCCAQDYEKMhlQ5U2EUAAAQQQqCeByHt3SfSbydrenK06SeAk604Hp0ubJ4gAAgggUFwC8R9nS+jNK7STVndVq7urORBAAAEE6keAglz9ONMLAggggAAC2RGIV0h52emSXPeDtn1Pl8Hi7X6pNkcQAQQQQKCIBBJxKZ94qvXnxXLbpA2XV0oHTxTDeqEDBwIIIIBA/QhQkKsfZ3pBAAEEEEAgawLJNd9LcPKZYiaimj4M8R93v7h2OFCTI4QAAgggUCwC0fkvSGTOg9rpeg+8UDz7DtPmCCKAAAIIZEeAglx2XGkVAQQQQACBehWIfj1ZIu/fpe3T8DWX0gHjxPA30+YJIoAAAggUtoAZXifl4/uLGS23TdTRqI2UDnxRxOWx5QgggAACCGRPgIJc9mxpGQEEEEAAgXoVCL95pcR+fE/bp2uHbuLvfR9vztPqEEQAAQQKWyAy63aJLnxFO0n/UXeIq0NPbY4gAggggED2BCjIZc+WlhFAAAEEEKhXATO8XoJlQyQZ/E3br7fbxeLZ5zRtjiACCCCAQGEKJNS2BmXW46hm0jZBV5t9xX/iY7Y4AQQQQACB7AtQkMu+MT0ggAACCCBQbwKJFfMk+OpFVn+mrU/D4Rb/yU+Ls9VuthwBBBBAAIHCFAhNvUjiv3ymmZwhgf7PibNlR02OEAIIIIBAtgUoyGVbmPYRQAABBBCoZ4GKuU9JxWfPaHt1NGmX+geY4fZp8wQRQAABBApHILZkpoSnX6OdkGePvuI99EptjiACCCCAQPYFKMhl35geEEAAAQQQqF+BZEKCr5wviVULtP26O/YW3+E3aHMEEUAAAQQKRCARk/IJAyW58RfbhAxPqZQOLhPD19SWI4AAAgggUD8CFOTqx5leEEAAAQQQqFeB5MZfJThxqPaNemogviNuFveuR9XrmOgMAQQQQKD+BCo+e1Yq5j6h7dDb/VLxdBmszRFEAAEEEKgfAQpy9eNMLwgggAACCNS7QOz7tyX89nXafg13QAKnjBFHo+20eYIIIIAAAvkrYAZ/t+6OGyBmLGybhNq6oPSUF0ScLluOAAIIIIBA/QlQkKs/a3pCAAEEEECg3gUis26X6MJXtP06W+8pgZOeFHE4tXmCCCCAAAL5KRCe+S+JffeGdvD+Y+8V147dtTmCCCCAAAL1J0BBrv6s6QkBBBBAAIF6F1B3RwQnDZfkuuXavkv2GSol3dRbWTkQQAABBApBILH6GwlOPsuaiv1t264duon/uPsLYZrMAQEEEMh7AQpyeb+ETAABBBBAAIEtCyR+/05C1j/OzGRMc6Ih/uMfEtf2+2tyhBBAAAEE8k0g+PI52pf6GIbT2qpgnDia7ZRvU2K8CCCAQEEKUJAryGVlUggggAACCFQViC54SSIf3Fs1+P+/OQItJdB/LG/b0+oQRAABBPJHILb4TQnPuEk7YE/ngeI9eKQ2RxABBBBAoP4FKMjVvzk9IoAAAggg0CACoWmXS3z5B9q+XTseIv5j79HmCCKAAAII5L6AGYtIcMIpkgyutg3W8DaR0kETxfA2tuUIIIAAAgg0jAAFuYZxp1cEEEAAAQTqXcAMr5PgxNMkGVqj7dvbfaR4ugzU5ggigAACCOS2QMUnT0rFvP9oB+ntcaV49uyrzRFEAAEEEGgYAQpyDeNOrwgggAACCDSIQPznuRJ67RKrb/tm34bDLf5+/xFni10bZGx0igACCCBQN4HkxpXW3XEDxUxU2BpwNu9gbUswhjdq22QIIIAAAg0rQEGuYf3pHQEEEEAAgXoXqPj4Man4/Dltv46mO0qg33NiuL3aPEEEEEAAgdwTCE+/VmJLZmgH5u9jvbinbVdtjiACCCCAQMMJUJBrOHt6RgABBBBAoGEEEnEJTjlXEr99o+3fs/vx4u15rTZHEAEEEEAgtwQSv86X4CvnaQfl2ukw8R9zpzZHEAEEEECgYQUoyDWsP70jgAACCCDQIALJDSus/eSGihkLafv3/f02ce98hDZHEAEEEEAgRwTMpAQnnSmJ3xfZBqS2IQgMHC+OJm1tOQIIIIAAAg0vQEGu4deAESCAAAIIINAgArHv3pLwzBu1fRueUgkMGCOORm20eYIIIIAAAg0vEFs0VcLv/ls7kJJ9hkpJt4u0OYIIIIAAAg0vQEGu4deAESCAAAIIINBgAuF3bpHYt69r+3du00UCJz7GRuBaHYIIIIBAwwqY0ZCUj+8vZnitbSCGr7mUDp4ohidgyxFAAAEEEMgNAQpyubEOjAIBBBBAAIEGEVD/oAuWDZfkhp+0/Zfsd7qUHHC+NkcQAQQQQKDhBCJzHpbo/LHaAfh6Xifu3ftocwQRQAABBHJDgIJcbqwDo0AAAQQQQKDBBBK/LZLQy2eLmYxrxmBI4IRHxbndvpocIQQQQACBhhBI7QM6YZD1+3bM1r2z5W7W27KfFTEcthwBBBBAAIHcEaAglztrwUgQQAABBBBoMIHo/BckMudBbf+OQGtrP7mxYngba/MEEUAAAQTqVyD85j8l9uP72k4DJz4uzjb7aHMEEUAAAQRyR4CCXO6sBSNBAAEEEECg4QRMU0LTRkr8p4+0Y3DtdJj4j7lTmyOIAAIIIFB/AvGf50rotYu1Hbp3+bv4jrxFmyOIAAIIIJBbAhTkcms9GA0CCCCAAAINJmCG1kr5xCHaDcLVoLyHXCGevfo32PjoGAEEECh6gWRCghOHSuKPpTYKw1kigUEvWm/H3taWI4AAAgggkHsCFORyb00YEQIIIIAAAg0mEF/+UepOORHTNgbD6UntS+RovrMtRwABBBBAIPsC0a/KJDL7Hm1HJX87S0q6nqPNEUQAAQQQyD0BCnK5tyaMCAEEEEAAgQYViMx5yHpz3zjtGJzNO0igr7VZuKtEmyeIAAIIIJAdATOyQconDBAzst7WgSPQyro7bqIYbq8tRwABBBBAIDcFKMjl5rowKgQQQAABBBpOIBGT4MvnSOL3RdoxeDqdLN7DrtLmCCKAAAIIZEcgMnuURL+aqG3cd/i/xN3xaG2OIAIIIIBAbgpQkMvNdWFUCCCAAAIINKhAcv1PEiwbJmYsrB2H/6g7xNWhpzZHEAEEEEAgswLJP36U4EuniWkmbA07W+8lgZOfEjEMW44AAggggEDuClCQy921YWQIIIAAAgg0qEDsu2kSnnmzdgxGSWMJDBgrjtLW2jxBBBBAAIHMCYRev6yat2Ab1jYCz4iz9R6Z64yWEEAAAQTqRYCCXL0w0wkCCCCAAAL5KRCecaPEFr+lHbyrzb7iP+ER664MhzZPEAEEEEBg6wXiP86W0JtXaBty79ZbfL1u0OYIIoAAAgjktgAFudxeH0aHAAIIIIBAgwqY0WDq0dXkhhXacZTsf7ao/3EggAACCGRBIBGX8pdOleT65bbGDbdPSge9JIb1QgcOBBBAAIH8E6Agl39rxogRQAABBBCoV4HE6m8kZL3kQbd3kbo7LnDi4+Lctku9jonOEEAAgWIQiM5/QSJzHtROteSAC6Rkv+HaHEEEEEAAgdwXoCCX+2vECBFAAAEEEGhwgYrPx0jFx9bjqZrDUbpNaj85o6SRJksIAQQQQKAuAmZ4nZSP7y9mtNx2uaNRGykd+KKIy2PLEUAAAQQQyA8BCnL5sU6MEgEEEEAAgYYVMJMSeu0Sia/4VDsOd4fDxXfUv7U5gggggAACtReIzLpDogunaC/kTddaFoIIIIBAXglQkMur5WKwCCCAAAIINJyAGfxdyicOETOyTjsI76H/J549TtLmCCKAAAII1FwgsWaxBCcOsy4wbRe5ttvPeqHOo7Y4AQQQQACB/BKgIJdf68VoEUAAAQQQaFCB+LIPJPTG5doxGC6vBPqNFkeznbR5gggggAACNRMITb1Q4r/M05xsWFsEPC/OFrtqcoQQQAABBPJJgIJcPq0WY0UAAQQQQCAHBCIf3CfRBdbeRZrD2XwXqyj3rIjTrckSQgABBBD4K4HYknckPP1q7WmeTieL97CrtDmCCCCAAAL5JUBBLr/Wi9EigAACCCDQ8AKJmAQnnSmJtYu1Y/HsOUC8PfR30WkvIIgAAggg8D8B6/fX8gkDJbnxF5uI4SmV0sFlYvia2nIEEEAAAQTyT4CCXP6tGSNGAAEEEECgwQWS65ZJsGy4mPGIdiz+Y+4R106HaHMEEUAAAQT0AhXzRkvFJ49rk97ul4qny2BtjiACCCCAQP4JUJDLvzVjxAgggAACCOSEQGzRqxJ+9zbtWAxvUykdMEaMQCttniACCCCAQFWB1ItzJgwQMxaumrC+OZq0k9JTXrC2A3DZcgQQQAABBPJTgIJcfq4bo0YAAQQQQCAnBMLTr5XYkhnasbi221/8xz8oYji0eYIIIIAAAn8KhGfeLLHvpv0Z2OST/9h7xbVj900ifEQAAQQQyHcBCnL5voKMHwEEEEAAgQYUMCs2Wo+uDrX2O1qpHUXJAedLyX6na3MEEUAAAQT+J5BY/Y0EJ59lfTFtJK4duon/uPttcQIIIIAAAvktQEEuv9eP0SOAAAIIINDgAomVX0rwlfOtf0cmbWMxDKf4T3pCnNvsZcsRQAABBBD4n0Dw5XMksWqBjUP9Hho4ZZw4mu1kyxFAAAEEEMhvAQpy+b1+jB4BBBBAAIGcEKj47FmpmPuEdiyORttJQO0n5wlo8wQRQACBYhaIffeWhGfeqCXwdB4o3oNHanMEEUAAAQTyW4CCXH6vH6NHAAEEEEAgNwSsu+NCUy+S+K+fa8fj3uUo8R15szZHEAEEEChWATMWkeCEUyQZXG0jMLxNpHTQRDG8jW05AggggAAC+S9AQS7/15AZIIAAAgggkBMCyfLfJDjxNDErNmjH4+t5nbh376PNEUQAAQSKUaDikyelYt5/tFP39vinePbsp80RRAABBBDIfwEKcvm/hswAAQQQQACBnBGI//CehN66Ujsew+WVQP/nxdG0nTZPEAEEECgmAfUynOCEgWImKmzTdjbrkHrUXxxOW44AAggggEBhCFCQK4x1ZBYIIIAAAgjkjEDk/Xsk+nWZdjzOlrtJ4OSnRZxubZ4gAgggUCwC4enXSmzJDO10/X0eElfbrtocQQQQQACBwhCgIFcY68gsEEAAAQQQyB2BeFSCk06XxB9LtWPydB5kbVJ+mTZHEAEEECgGgcSv8623U5+nnaprp8PEf8yd2hxBBBBAAIHCEaAgVzhryUwQQAABBBDIGYHk2qVWUe4M7aNYIob4e98rrnYH5cx4GQgCCCBQbwLWS3DU74+J37+1dWk43BIYNEEcjbe35QgggAACCBSWAAW5wlpPZoMAAggggEDOCES/eVki7+nv8nD4mln7yY0VI9AiZ8bLQBBAAIH6EIgtmirhd/+t7cqz9xDxHjRCmyOIAAIIIFBYAhTkCms9mQ0CCCCAAAI5JRB+6yqJ/TBLOyZX2wPFf9z91g1zhjZPEAEEECg0ATMakvLx/cUMr7VNzfA1l9LBZWJ4/LYcAQQQQACBwhOgIFd4a8qMEEAAAQQQyBkBM7JBgmVDJVm+Sjsmb7cR4tlniDZHEAEEECg0gcichyU6f6x2Wr6e14p79+O1OYIIIIAAAoUnQEGu8NaUGSGAAAIIIJBTAolfv5Dg1AtFrH2TNj8Mh0v8Jz0lztadNk/xHQEEECgogeSGFRKcMMj6rTBmm1fqDdT9nrXuGHbYcgQQQAABBApTgIJcYa4rs0IAAQQQQCCnBCrmPiUVnz2jHZOjcVtrP7nneUxLq0MQAQQKRSD85pUS+/E97XQCJz4hzjZ7a3MEEUAAAQQKU4CCXGGuK7NCAAEEEEAgtwSSCQlZd8nFV87Xjsu96zHiO+ImbY4gAgggkO8C8Z/nSui1i7XTcO98pPj+fqs2RxABBBBAoHAFKMgV7toyMwQQQAABBHJKILlxpQQnDhEzWq4dlyrIqcIcBwIIIFBQAtYPJIITh0rij6W2aRnOEgkMelEcjba15QgggAACCBS2AAW5wl5fZocAAggggEBOCcSWvCPh6Vdrx2S4/RIYMEYcjbfX5gkigAAC+SgQ/XqSRN6/Wzv0kr+dJSVdz9HmCCKAAAIIFLYABbnCXl9mhwACCCCAQM4JRGbdIdGFU7TjcrbqJAHrJQ/idGnzBBFAAIF8ElBvmi6fMEDMyHrbsB2B1tbdcS+J4fbacgQQQAABBApfgIJc4a8xM0QAAQQQQCCnBMxYREKTTpfEuh+14/LsPUS8B43Q5ggigAAC+SQQmX2vRL96STtk3+H/EnfHo7U5gggggAAChS9AQa7w15gZIoAAAgggkHMCiTXfS2jymWImopqxGeI/7gFx7XCAJkcIAQQQyA+B5B8/SvCl08Q0E7YBO7fpbN0N/KSIYdhyBBBAAAEEikOAglxxrDOzRAABBBBAIOcEol9NlMjsUdpxOfwtrP3kxonha6rNE0QAAQRyXSD0+mUS/+kjzTANCfR9Rpyt99DkCCGAAAIIFIsABbliWWnmiQACCCCAQA4KhN64QuLLZmtH5trhIPH3vpc7SLQ6BBFAIJcF4ss+kNAbl2uH6N6tt/h63aDNEUQAAQQQKB4BCnLFs9bMFAEEEEAAgZwTMMPrJVh2miSDv2vH5u1+qXi6DNbmCCKAAAI5KZCIS/lLp0py/XLb8Ay3T0oHTRQj0NKWI4AAAgggUFwCFOSKa72ZLQIIIIAAAjknEF/xmYReVS9xMG1jMxxu8atHu1p2tOUIIIAAArkoEJ3/gkTmPKgdWskBF0jJfsO1OYIIIIAAAsUlQEGuuNab2SKAAAIIIJCTAhUfPy4Vn4/Wjs3RtJ0E+j0n6s4SDgQQQCCXBczwOikf31/MaLltmI5G21l3x70o4nTbcgQQQAABBIpPgIJc8a05M0YAAQQQQCD3BJIJCU45TxKrv7KNzdF4e+utq/eLo8kOthwBBBBAIJcEYktmSGTGjWIm47Zh+f5+u7h37mWLE0AAAQQQKE4BCnLFue7MGgEEEEAAgZwTSG78VYITh1a5s8S96zHi7XGlGB5/zo2XASGAAAI6geS6ZRL54H7rDatz0mnXdvuJ/4RH09/5gAACCCCAAAU5/htAAAEEEEAAgZwRiC2eLuEZ11uPp/rFe+iVogpyHAgggEA+CsSXz5HIh/dJct1PEhjwvDhb7JqP02DMCCCAAAJZEqAglyVYmkUAAQQQQACBuglEP39eXDsfIepRVQ4EEEAgrwWsN67Gf/1cXG275vU0GDwCCCCAQOYFKMhl3pQWEUAAAQQQQAABBBBAAAEEEEAAAQQQqFaAgly1NCQQQAABBBBAAAEEEEAAAQQQQAABBBDIvAAFucyb0iICCCCAAAIIIIAAAggggAACCCCAAALVClCQq5aGBAIIIIAAAggggAACCCCAAAIIIIAAApkXoCCXeVNaRAABBBBAAAEEEEAAAQQQQAABBBBAoFoBCnLV0pBAAAEEEEAAAQQQQAABBBBAAAEEEEAg8wIU5DJvSosIIIAAAggggAACCCCAAAIIIIAAAghUK0BBrloaEggggAACCCCAAAIIIIAAAggggAACCGRegIJc5k1pEQEEEEAAAQQQQAABBBBAAAEEEEAAgWoFKMhVS0MCAQQQQAABBBBAAAEEEEAAAQQQQACBzAtQkMu8KS0igAACCCCAAAIIIIAAAgg0oMD69etl6dKl0qxZM2nbtq24XK4GHA1d6wQSiYR8//33Ypqm7LrrruJ0OnWnEUOgYAUoyBXs0jIxBBpG4JFHHpHPPvss1bnb7ZYnnngiqwNZtGiR3HXXXdKuXTu56aabstrXo48+Kp9++mm6jxEjRsh+++2X/v7aa6/J5MmTU98bN24s9913nxiGkc7zAYGaClx++eXyxx9/pE4//vjj5eSTT67ppZyHQEYF7rjjDvnuu++0bap/5I4aNUqby6VgLBaT8847r9oh9ezZU4YNG1ZtngQCCOSHwOeffy5lZWXy5Zdfpv63fPny9MBVoUcV5VTR55RTTpGBAweK+rsaR/0KqN+Px4wZI3PmzJH58+fLV199JeFwODUIr9cre+21l+yzzz6i/o6999571+/g6A2BBhCgINcA6HSJQCELHHfccTJt2rT0FBcvXiy77LJL+numPwwfPlyef/55CQQCsmHDBnE4HJnuItXeypUrpX379hKJRNLtf/zxx3LAAQekv6t/8D355JOp76oYqf6CwU/60jx8qKGA+suq3++XeDyeuuKiiy6Shx9+uIZXcxoCmRXo0KGD/PDDD9pG1e+75eXl2lwuBdWfQx07dqx2SOof5hMmTKg2TwIBBHJbIBqNyi233CLqBwiVf3b+1YjVn7P9+vVL/VChVatWf3U6+QwIqH8fjBw5stof8mzahfp79DXXXCPXXnutqM8cCBSqAAW5Ql1Z5oVAAwncfPPNcuONN6Z7HzdunJx66qnp75n8oH7yqYp9qoChDnW33G677ZbJLtJtXXXVVak78SoDRx55pEyfPr3ya+rXI444QmbOnJn6rH4CW91dJVUu4gsCmwlsXjxQd1pedtllm53FVwSyL6B+b/X5fKIeKdIdnTt3Tt2FosvlUuyNN96Q3r17Vzsk9Y++2267rdo8CQQQyF0BdVfc6aefXu3vReoHB6pgV/l3xc1nou7Q2nPPPTcP8z2DAt9++22qEKd+L970UEXRfffdN3Xn4tq1a1N3zK1evXrTU1J3yanr2rRpUyXOFwQKReD/AQAA//91helLAABAAElEQVTsnQe4JEXVhs9NuzcsOUvOUSUqOeckLCCCgCxZSQKCiPKjCAqCgARRkBwEYWHJIoIEkYzknJEseW8O89c32ENPT01Pz525d9Jbz7Pb3VVdVafe6tvTffrUOU0pl4wEAQhAoEwEbr31Vtt0000zrR188MF22mmnZY7LuXPIIYdktX3FFVfYjjvuWM4u0m19/PHHtuCCC9rnn3+eafsf//iHrbvuuplj7eicN954I5232Wab2c0335xVzgEEkhC45ZZbbPPNN8+cesMNN9iWW26ZOWYHAuNF4MUXX7Qlllgib3eTJ0+2qVOn5i2vloIzzjjDDjrooLzinH/++TZlypS85RRAAALVSeCVV16xr33ta9bd3Z0RcIYZZrAf//jH6Wc03b/mmGMOGx4etjfffNN0T5s2bZpddtll9umnn6brTJ8+3bq6ujL12Sk/gQ033NBuv/32TMNf//rXTe8H22+/vWm+wum+++5L34+ff/75TPYmm2xif/3rXzPH7ECgrghIIUeCAAQgUC4CH374oZT8mX+rr756uZrOakf9uAeoTD/q0z2AZZ1TroNjjz02q5811lgjp+ne3t5Uc3Nz5rwDDjgg5xwyIJCEwOmnn565jnRdP/vss0mqcQ4Eyk7AfVTIuhbD93btH3HEEWXvcywadMq42HHcfffdY9EtbUIAAmNIYGRkJLXOOutk/W3vvvvuqXfeeadgr06Bl7rgggtSOp809gTmn3/+9DxNmjQpdcopp6SGhoZiO+3p6UntsssuWXN75plnxtahEAK1SqBJgruHKhIEIACBshHQF0l9hVTq6Oiwzz77zFpbW8vWvho67rjj7Oijj85qcyy+oOmrqyzfnAIw09dNN92UZcGkAqc0sWWWWSZzjqwC9fWPBIFiCei6cUq5dDWn5DWn7LUJEyYU2wznQ6BkAoUsy8455xzbe++9S+5nrBvYYostYi2W3377bZtnnnnGWgzahwAEykjAKWjswAMPzLS455572p/+9KfMMTvVQUDPMLJA1LO0Vo4svfTSiQRTPVk/vvTSS+nz9T7x8ssvc69ORI+TaokACrlami1khUCNENh1113t0ksvzUj76KOP2gorrJA5LnVHP9L6Yf/ggw+ympprrrns3Xffzcor9cB9ybPDDjss04zGofFEk5YVbr311plslhlmULBTJIGw8kDX+WuvvVZkC5wOgfIQ+OEPf2i/+93v8jZ2xx132HrrrZe3vFoKllxySXvhhRe84uhFUUvWSBCAQO0Q0O/icsstl1mqqg/BejZj6Wn1zaE+bO+7777229/+1vScXky66667stzDXH311bbddtsV0wTnQqDqCaCQq/opQkAI1B6B6FfLP/7xj7bPPvuUbSC///3vbf/99/e2V05Lh/7+fltkkUVMbQbpqquuSvu8CI6D7amnnmqHHnpocJi2mFtqqaUyx+xAICmBsPJggw02sL///e9Jq3IeBMpKQL4LZRGcL8kn03zzzZevuCry3dIo6+zstMHBQa88ssB4/PHHvWVkQgAC1Ung+OOPt5/97GcZ4aS4WXvttTPH7NQPASnx3n///fSAjjrqKNPckyBQTwRQyNXTbDIWCFQJgYceesi+8Y1vZKQp5zICOebVl1A58vUlmcMroEI5khSJ++23X6Ypmdk/9dRTpmWE0SQFoRSFSip3/i9s4sSJ0dM4hkAsAV3fWpYRKA/0VfkPf/hDbB0KITBWBHTPe+6557zN6zqV5UNTU5O3vFoy9Vux6KKL5hWnVgJT5B0ABRBoQALruqBaUsIpSWHj/MZV/b2oAaepLEMOB4QYC9c0ZRGSRiBQAgEUciXAoyoEIOAnMDAwYDPOOKPJwkypnBYIiqS60047+Tt2ufpypi9opSZZVchSKaz4u+iii2y33XbzNq2HhL/97W/pMue8NhNt1XsymRDIQyCqPDjppJPsRz/6UZ6zyYbA2BFwDtPTlmXBfTza07LLLpv+QBHNr7Zj3Zd1f86XXGAKO/HEE/MVkw8BCFQZAX0ImHXWWU3PmkouMIO5AA1VJiXilIvAd7/7Xbv88svTzZXzfaJc8tEOBEolgEKuVILUhwAEvARWXXVVe+CBB9JlLS0t6cAOWjZUalpxxRXt3//+d6aZ73znOyYlXZB22GEH+8tf/hIcjnp72WWXmYvwlKm/8MILp30Q5QtOIQuMQHknn0ryrUSCQLEEosqDa6+91rbZZptim+F8CJRM4I033kj76szX0Le+9S2bNm1avuKqyY9zcSAhy+1SoWoGjiAQqFMCWgkhX6tByudKJChnW9sEwtaQm266qd1yyy21PSCkh0CEAAq5CBAOIQCB8hAIR4pUi3fffbettdZaJTV+22232cYbb5xpQ8oxRVyS8k/LFZQWW2yxTITXzIlF7ij49Fe/+lV7+umnMzXPPvvsrOWrmQK3o+WFWr6l5YZKe+21l5177rnpff6DQDEEosqDJ598Mu24upg2OBcC5SCgjwryYZgvyXJTFpzVnuTbUz4+86Xbb7/d1l9//XzF5EMAAlVG4JBDDjFFsg/Sf/7zH5t33nmDQ7Z1RODDDz+0r3zlKxlrSLmR0fM4CQL1RACFXD3NJmOBQBURkHm5zMyDpOhK4aAHQX4x27AfCdWTBdsll1yS9hn317/+Nd2U/Bl9+umnNsMMMxTTdNa5svrYdtttM3l6GJD1Wz6fcC+++GLar11Q4YQTTrAf//jHwSFbCCQmEFYe6FrW0hwpe0kQGG8C+qgQF4xHvg3l47Dak6JfK+p1vvT666/bAgsskK+YfAhAoMoISIH+j3/8Iy2V3KPomY9UnwQU5VvRvoOERXNAgm09EUAhV0+zyVggUEUEZLkma7Ug7bjjjllLS4P8pNuHH37YVllllazTFRlP/iR+8pOfmJRgQbrnnntszTXXDA6L3ioghQJTBKmQMlHm85tvvnlwurF8IoOCnSIJhJUH+uKvL/8kCFSCgD4q/OY3v8nbtaL/xlnQ5a04zgXLLLNMOuq1r9v29va00tsXqMd3PnkQgEDlCSy44IIZP73f/OY37f7776+8UEhQdgKffPKJLbXUUvbee++l255pppnSz0STJk0qe180CIFKEkAhV0n69A2BOicw++yzm8zNleSDLfCxNpphf/vb304ruoK6YT8S0UAPZ5xxhh1wwAHBqUVt9ZK50UYbZepoDK+99pp1dXVl8qI76u+ggw7KZMvH3fLLL585ZgcCSQmElQfrrLOO3XnnnUmrcl4NEZAzcilbZf0499xzV1V0wCDCrz6iyIdhvqT7ol6MqzkVCkyhKLLPPPNMyUN4//33TS4UNJ9jbdEq1wh6QVUkbzm2n2WWWarq+ikZJg1AIIaA7p36G9PfttKUKVPs/PPPj6lBUa0SiD73H3bYYXbyySfX6nCQGwJ5CaCQy4uGAghAoFQCshoLO1/94IMPTAquYtNLL72UjngaPICpvvwbKXiC0nPPPWd6sQrSnnvuaX/605+Cw6K2ajOsBPnlL39pP/vZz2LbkDJOSrkgffbZZ94ls729vWml4mOPPWbPP/+8vfnmmzbPPPOk/dUtt9xyNnny5HR02qCd0W4VIVb+9hQgQH289dZb6Zc3sdfLv3z5yRJLS3FHk6ZPn26PPPJIpqp8+PmW8953333p+ZeCUsvCFPhipZVWMi09Vp18aazlz9fvWOVLcXHXXXelr1MpqD/66KP0v48//jj9UqFgJ/ryq5frK6+8MvOisccee9h5551XtFjip7mXbywpffRPfShf19vqq69ucsivaMCVTP/973/txhtvzCuCrK+iMr777rumaMeymJWCX4oQXceKiCxLCflv9F2L0U7U96WXXpq2hFU7+huZa6650ha3X//619N8yqlsktLnnHPOSful1P1MAROC+5nk1TgXX3zx9DL87bbbLtEYomMq5vjRRx81LfOXHGKqf/LDKUVPvqiq4fYLWZbpen/11VfDVTL7UiDp+kuS9Jtx00035T1VH2Z0T8uXCgWm2Gqrrez666/PV92bL4WlXDLo2pUluObz888/t7a2trQ/UV1HiyyyiCng0Pe+973037a3oYSZ6k/3c3140t+05ijwV6ompAjUNav7ql5gFfio2pKUh5JdvwmSX3+3+tfX12dzzDFH+m9Y9yb9LWurj2K6TpKmsbzn6V4R+JNVgKp81ve6nyuglH7fdV3ob2q++eZL/75rbsIf+uLGpbnVtSVWurb0d6R7hKIa6zlB17zud+VK1fp7q2sjCAwWHqvuUzvttFMmS0GPwksaMwVuZ4011kj/fQR54/HsMpa/vfV+LQbzpK0iXx955JGZLPl1liVkOYLDZRplBwLVQsA5LydBAAIQGBMCP//5z1PuXpf55yJjjaof58Q104baW3nllbPacQ+UKfcjnTnHvZBklSc9uPfeezNtqB/nmyTlTOYLVt9ss80y9eacc86c890DZMotq03NNttsmfPCXIJ991Keci/JOfWTZriXntRRRx2Vci84sf2oP+efLOUUlyn3cpS0+cx5Tsma1b57eciUaccpnVJOmZTuIxhbeOtegrPODw7GS/6gv7HcitGuu+6acr6psliFORTa/9WvflWUiM7fXMo9wKZ0DRZqW/O/2267pXRtVio5RUisnC5gQEY091Ka2n777VNO+RBbxylCUnH3Gad8SznLr9SECRNi23E+KFNO8Zfpf7Q7TrmfctEA8/4t+ObJKc7Tf8fuhXS03ebUc8q/lHvBT7lADClnrRw7dp9M0Tz3ASSnj3CGU/rn7cMpF8Knxu5fc801eduRTM65d2x9pwSKre+cw8fWDxc6P1UpXZNuKXlsm2FWzrI65aw6Uk7JGW4q8f4///nPlFPUJu5PfTuFTcq5bUjcx1idqGvOKS5Tm2yySUpzHuZSaF/3zyRpPO55LtBOluxO2ZYlmlPApZy/3JRTUmedFx2jUxylnII4q274QM8axx9/fErPAdG64WPdu4455phRX1NBn9X+ext9HgszSLrv/MwFw01vx+rZZTyuQw2gXq/FrElyB7pv6BklmGennE855XT0NI4hUDcErG5GwkAgAIGqIxB9+PnFL35RtIzuK3POg677Cp3TjvP7lvnx1sO/syrIOadQxpZbbplpQw8CUqIlSeEXptVWWy2rirN4SulhIniwSLJ1/vCy2khyoAdPZ4FWVD+SRS//ztotSReZc5w1YFY/4frOEq6gEsqnkBtP+TMDKfOOXkCnTp2aclaAWXySzLnvHN91nk9kvbg7n41F9yslsbM2y9fsmOY734yx8oqlmOolxPmMiT03zE8vrFI+RZMLQlBUO2pTHwNGk6S8kSIuLFex+7qnOQvX0XSfVUdKqfA9qlg5fOf7/obDnTorsbxjlyxJk7OSyNuO5CqkuHEOwGPrn3XWWYlEkXIgyYcOHyvlOUudlH7Likn6HXC+7WLlz9ef8t2S95TzhVpMl2U711mwp/RhLE6+uLIHH3ywoCzjdc9zgXayxiHli5KzikxJoeus5rLK48a10EILpZyFdM7Y9PtX7McbfZgcrdK+Fn5vXVCZxFzzMX/hhReyWI/Fs8t4XYcaSD1ei1kT5A5uvfXWrA9m+j13VofR0ziGQF0RQCFXV9PJYCBQXQRkKRV+UNILarFJFl/hNqR0cks6cppx0QCzznMBH3LOicvQV+5wP7K4c0tq4qqky6T4c0uVMnVd5Nd0vlu2mtp9990z+UHbsvBxy05SbslF2kJNL2rhL4E6Tw8gbolbwb6DE9xSpryWQ27JTEpf5iWLC4qRo9xUf265ZOqJJ54Imiu4Pfjgg7PGJeWDkni5pTRZZeLolqel9t9//5QsCcUq+jI/3vIXHOAoTpDlg16QgnkOb8XALVlKWz/oRVVzK8tEXTtippdPKd/ckqas+m5ZYSJJVNf3UiiLDbWpvw23dC593fle8GUJ+uSTTybqq5wnff/7388ab5iZ9qXodcv+Ys+J1gmOZcXklpClxdULtK65oKzY7d13313UsN0S5TTrYvvxnS9rx7DCuxhB9OIvS1Vfu6XmxVmW6X4Q177uA0nT3nvvHduWi3Ad29Thhx8eWz+JRbKsNYq18PKNXwoXt+QsVt6gUPdEXxvF5h1xxBFBk+Oy1T2t0JzpA5XzlZlad911U85FRMoFZsr5aOVTWoUHMJ73POfeITMXbjltWgwptPTbGp0P5ckiUPc23XNXWGGFnHOcK4/0hwY1JF6yWvXdl9XXxhtvnP4o4KK+pz+eRfuT9WWxqVZ+b/U7KTbRf3peCTjody9aHj4W33Aq97PLeF6HGke9XYvhudG+/q6cf8DM/OpZOLoCI1qHYwjUAwEUcvUwi4wBAlVMIGyZ4VvOGSe6lFozzzxz5sdZD2GylvGlqCl/scvNtIwteMjTVg9uSZKWr4TraZmuXrqi1gF6oTvwwANTb7/9dk6zWtampXbhdtZee+2c83wZf/7zn73KGC2XdL7DcqroAdX518t66FG/UhglTWHLH1nYKWmuomOWdVF0SaTzCZUlVyXkTzrOpOfpC7lvmaiufecDLvHSoqiCSkwLJSkU9NAavnakYNPSOt/Lv5QlPiWNXiLHO0UVkOExaF8v69G8Yo51nWu8zudTSe1I0SpLvSTJ+TxKxVmHFSN/cK4sH53voyTdZ8657rrryi5HII+2+e7DEkBWWeFzo/su4E5GzkI7cdeAPm5EX7ij7UmREe0/fFxoGZSWjUc/mITrF7u/ww47REXMOZaSPvxSWmwf4fPHUyGnazT82xCWQ4opsYxT/Eu5eskll6SOPvroHCbhjPG+50l5GIxF9xJZVUaXzktxJmtN331CCrDoc8y//vWvlAtOkHL+IjNtB32sv/76qRtuuCGnLSkppeQLztNWirxirCDr4fdWv1UBAz03FZPC12epzy7jfR1qnPV0LUbnTVbtYUt4fbyVWwsSBBqBAAq5RphlxgiBChKQb5Xg4UlbWY8kTS6aUlZdLRmS3xNfivobcU5+fad581yAhawv1FJw+JRZvsoyrw+PTzLLAi6cp4fuQkvP9KISfcgvZCXggiVkWeepT+cUO/0w75M1nCeLuKg1W9JlAeF6LjhDullZ/AVj1sukrEoKpUrJX0iuYsr1shVViMkCRC+W8m1YTNJLXcAwifLaBTPJ8p2ourr2ost0fDJouWjUKkPWe+OZyuHLLODl28oiNKok9p2XJC+JlZyUzT7FbJL2C50jq6OkyUWKzrkvFGq/2PK4e8Vll12WuY597Z566qlJh+K1QgraTPIy7hyB55VFL3xxCj29cAd9FdpKCS5/cYXOU7nuGXHJBRJK1E6SvsZLIaffZf0WRGWSP7QkvwVxPMJl433Pk4ItzjecC/yS0geZQklKvDAbWbZFrXbll1EK/UJJFnbhtuR3Lkmqh99bjTPsmkG/mcWkcj27jPd1qDHW07UYnbNnn302y8ey7s3Tpk2LnsYxBOqWAAq5up1aBgaB6iBw+umnZz08ysQ/SZIT7Kjz7GOPPTZvVVkThS0ZtBwmaYpaDBXz8ht90A4/KEue4447LqkY6WUu4fpxD/ryHRNV/MkiS8uEkyb5ewsvxZIVg+8Lf7g9LRcOK6CkcA37epEyLonyolLyh8dS6r5ensL8NHcu2mEq6vQ7aT9hK0kXCbVgNV3j4etF8x8s0yxY2Z3w05/+NKu+FFjjlfT37VtmGx5PdN9F00z/jehB/aqrrirJL5qWUbsoqyn5V4sGjYn2q2Nd44WSrJ98daN5soCRQlQBC+QjLc4KLFxXHx0KJb3YRK1xwm349rWkWn/7Wko6ZcqUdGCQ6MeBaD0Fx8iXjjnmmFgOsv5JkqTkCd/TozIUehnXvUxji9YLjvViny/Jwb5vSWJQV1tdv/LpFHxsUX96UZe1cfi86P7yyy+fr9t0fiFFte6/UubIolbbuCAA46WQi/6GasyyTsz3AS0WQEzheN/zNLfR+QuOXUTbREGfNBwpfvP5INQ1Luv5pKz0ux3IoK2C3RRK9fB7G3AM35t+8IMfFBp6prxczy5qcLyvQ/VZL9eixhJOLsJ5Sort4JrW/Mp/LAkCjUQAhVwjzTZjhUAFCEhpEfzQaiv/HknSBRdckFVP1geFlE3hL6eyUkqS5PtLX+MCGfWSVYxCRb6UgrrhraI0Fmtuf/HFF2e1JYfk+dLPfvazrHPFJ245UL52ok6CtdQtLsnCMTxOWf8Fy6vEMS7CZbjdSskflqGUfS09ljVimIUUGklfqqJ9a/lSWEGlCKhx6cILL8zqW0s9nn766bgqOWX6e9J1Gh5DnOVTTgMlZEhxEe630L6UXYGvwqBbLe2LU7j42pQy5P777w+ayGwLKdN+/OMfZ8717cjqyddfOE/3JP2N+1L07yFcL9jfa6+9fFUzebLQCyt1g3q+rf5mNWZ9IAmc1GcacjvhpVHR+lIIxVl/hq1lo3V1nNQ/5lNPPRXLtNDLuF70fP0HeYpGmi9FlwYGdYKt7re+60jt6cW/kO/DfAykOIlTQsoST3870aTl6WeeeWZqiSWWyBrzeCjkor/VYiSfpXHXSFT+JMeVuOfJp1Uw5+Ft3MfBfGORv9hwG9rX39K1116br0refPmXC9qSFWihFL2/jNfzQiG5ii3XEvNg3Nrqw0bSVK5nl0pchxpjvVyL4fnShw/5kAzmVM9AWlZNgkCjEUAh12gzznghMM4EZAkTtiJS5LdCSZYGWr4R/Ehre9BBBxWqlv5SHK7z6quvFqyjdsN1ZPFVTIouO1FbUhIksWaJ9iPLsrAsv/nNb6KnpI8V3S3s2Fh1RrssSC9yejgP+pUPvLik5XDBudGtXsySpErKn0S+JOdssMEGWRxkGadxjTbJf1KYZ9wLn174oxH5Cs1bPrkUSTjcr3zPjUeSo+Zwv/n29cKqyHj5khSX+epG86U8zueLTct1o+eHj1U3X5KiUNFqw+dH93UPlN+ouBRVjkfb0N98b29v3iYUvCVax3cs/5RxHx10/41bpqdlX3EpLsqwlE1xYwi3K0WFT/4gr9DLuHxzBuf6tvl82SnIiu/8cF6hl8ao9XG4rvalIPEluU+Inhs+LmQNpbmTcjhQzI61Qk4+QqNKcTmelxzlTJW654Wtv4N5iLs3x4158uTJWXOre1uxH+2C9sNR5bUsPy7Vw+9tML7oMvJCHxCDetqW49mlUteh5K+Ha1HjCJI+PoQtDeVCo1jfz0FbbCFQ6wRQyNX6DCI/BGqAwDe/+c3Mg6gscvRQE5f0kBU8/GorE/Ykvue0PDRcr9CXZ0W6DKy7VE8vi8VaGUUVh8VYiUUZyDopLP8f/vCH6Cnp49NOOy3rPL0AlZLC8xNEic3XnmQKyxjsFxPtrZLy5xtXMflacheMW1v5DZOlZSlJDsHDbcYpWKPnStmja3k0KaoA0PLN8UjRayA89mBff0saa1xK6m9Livc4JUHcciDJowAU+ZKCRwQy59uef/75+apn8t99992s+5GvLS1z9SVZjkR9Akbr6/4mJVYcB7Ut/5nRuuHjQtGyZcUVPj+8r+WVSZOUw+G60f1CL+OF5kXXoC/J8i7aV/hY1m9Jkixmw/XC+/ms8xQdPHxedF+WUXqRLZRkcSt3EbfddluhU0sql6/WsIyyGvYFkympE1e5Uvc8WcaGxzdaZZzGHw5io3vbaJVxamuhhRbKyFXo7zF6rx3P5wXJWs4UdRFSzPNaOZ5dKnUdimE9XIvhayH8MVC/TUncQoTrsw+BeiKAQq6eZpOxQKBKCUSt0LQUKS7Jf1b4IXjnnXeOOz1TFlWU/N///V+mzLcTfiBQf/J5U0ySYjFs/aeHCvmlGm0655xzssad74F92WWXzTovTnmTRBbxDXivttpqsVW05Dg4N9jKl1MhJWu40UrKH5ZjNPtahhVVwurFv9Sk5WYBT21lpZMvRS0ttLxutEkWrGFFTpziabR9+Ortv//+WeMNj137kqmQFZLalYIpWjd6LAVKISWUlntG64WP5S8qX5LVb/jc6L6WqiVN++yzT2xb+Ry4RxUjURl0XCh6ZSBjIcuyuAjUspjy9R3kySIiadp3331j2yr0Mn7kkUfG1peVZjTp76GQtWPSqJaywAvGHd3mW2YYtSiP1tOx/v7zWXpGxzOWx5rr8ActyXbTTTeNSZeVuufJMjaYA7kFKCWFlxPLb+RoU/SeXSiAVS3/3kYZhV2E6HkrqbWt2inHs0ulrkPJXw/XosahpAAjYV+Av/71r78o4H8INCgBFHINOvEMGwLjSUBKquChVts4a5Hosk2drx/vJClq5RL3JVjO76OWHA8//HCSbjLnRJ0ra8lZKSn6BVQO2qNJfrPCLLW0rJSlkmpfL+pBm4Wie0aXxelLfSHffuExVFr+sCyj2Y8uo9OLdTHKyHx9RhUq+SLsyjpGS52C+dL20Ucfzddsovxw8JQkkSsTNVrgpE022SRrDOHxaF+WEElS1DdStB29+OoFtlBSZNpo3fBxPn9lstzVi2H43Oh+MUoK+Y2M1g8f++TwLUkL19F+McsIzzvvvFgZ4pYQ33XXXbF199xzz0JTkSmPLgsPjynJy7iWd4brRPd999fo33e0jo71Up7kX3RZebitWWedNTPO6I4+ioTP9e3L71JcYI1om2NxHLVOHaugMJW858kVQcBf+6NN+pAjq7igrWICR0X7jPrf1MecfKnWf2+j4wq7CFHQlWJSqc8ulbwONc5avxaDudLfQtitwZZbblnwg1lQly0E6pVAkwbmfiBIEIAABMaMgFtOZS4CZKZ9tyzOzj777MxxeMe9OJqzdMtkOesru/XWWzPHhXZmn312cwqi9GlueZS5pYTeKs7SxNzLfKbMKQjM+SfJHCfZcX6nzL00Zk51DtLNOUrPHBe7o7pXX311upqzEDIXIMCcBV5WM27JmrkvpVl5LsJd1nGxB87BuDkH5Zlq7gXfnDVA5ji84xRQ5iwc01nOAa+5SLC26qqrhk+J3a+0/LHCJSh01mjmHPNnzrzyyivNWU9ljke74140zFnspKs7Cx1zy768TbkgKVm8nT8wc0oQ77lJM50Flzn/ZunTNacuIqA5hUfS6qM6zwVgMefHzFvXKQjNLZv0lkUznUWZuaUu0ezMsfMblPU3mimI7ET/liPFpvvFUUcdFc22X/3qV+ai1ebkBxnOmtL095U0uSWGpntevuSiw5qLMptV7CxrzVmTZeWFD3QvEWunPA9n593XeDSufEn3Sd0vfclZi5pTNviK0nnOEsKc5Vre8nDBggsumPf+7V7GzX2ACZ+es+8ix9pjjz2Wk68MMXHWNeaU21nlupc6X5hZeWN14BT5aTmi7Z9yyinmXABEs3OOnfsH07ku2EdO2XhkhO8b6s9FRjSnqCx715W854mxs0ZMj8kpeM1Fdx7V+Jw/W3MfOzJ1XXRlc/79MsfF7DgFvzklRqZK3N9jrf/eZgb5vx0XbMacIj195KxtzQU6iJ6S97jUZ5dKXocaVK1fi8HEuA865lbNpA91j3cf3M0FPAqK2UKgMQnUq6aRcUEAAtVFILwMSF/HfElLWaPWJnLEW0xaf/31M1+h3V3da72lqIJzzDFH1nmyzCs2Ra1ZSrVUci+QGZncg4pXnKgVncZY7n/5rLO07C/swNspB7wyxmVWUv44uZKUOUVVSpEyA95arlyqdWLQb3gZrBx250vyCxX0r22+wB/56vvyw38zWipaaHmnr41i8uTfKrxcJTwe7WsJaNIUZ0WltmTFmiRF/5ajMslRvi8VssI6/PDDfdXy5hUKLuH7m/v+97+fdU1EZS/kYyoqzI477hjbnvzV5Usab7T/8LGiuiZJskbRtRiuG953L+MFm5G/0nCd8H6++6ssKsPnjdW+7qP5kixu11prrcRyaCmbohWOZ5KFeTgqtKzNyx1VNRhPpe550eXXWgI92hT1D+uUl6NtKnXqqadmXRtxf4+1/HsbBaS/i3CwmUJRp8P1y/HsUqnrUOOoh2sxmI+llloqc/0WCnQU1GELgXonwJLVep9hxgeBKiEQdnCtpRt64Yom+cIKvwAVih4Wra/jaKRCn0Iv6uRYUQdHk6Ivn5999tlomsnUCS+hlaLBl6ZMmZLFKMyrXPv5XqzeeuutrL6dxZBPxNi8SsofK1iCwqjDdV3T5Uh60Qj7IozzmRiNKuos9EoWQX9nwbUTt5Su5I7+10Ch5aHFLGuUYiWQPboVU7FNkhSNMlo/fJxvOXs4IEr4/GDfWfcm6T5zzj/+8Y9YOTbccMPMucHO5ptvHlunUPCDoJ1gu/LKK+dtT/duKabzJSkMg7H7to888ki+qln5zqowtp1CL+MKkOHrP8jLd391lpux9YL6pW4LLQ13FlU5LhXi+lx00UUTu3bIAj3KAy3DDsujpYRjlSp1z3PW31ljLMVX6O9///ustvRbMtoU9r+pDxtxf4+1/Hsb5aPASeFr7oQTToiekve4HM8ulboONah6uBY1jvvuuy8zh1qqSoIABL4gwJJVd3cnQQACY0/gF7/4hf385z/PdOR+mLOW3mn5kXupSC+XC05yVinmrDWCw0RbLSfUssIgnXzyyVnLf5x1Trqf8JK4uCUfQTu+rZbnOJ9D6SLne81cpEvfaYnynFN5UxtB0hI0FxUsOMxstWTNBXvIHB944IHeZU+ZE4rccS/c5qIbems5K0Jz1kuZMucL0NwDf+Y4yU4l5U8iX9w5LsKaOeVH5pRjjjkm65rOFBS5o2XVTrGUqeWCkZj+XnxJSwWdtUWmKPp3lCkoYmfhhRc25wstXUN/g1piPpYpyjHaV9Jljfpbdk7lzVk/RJtIH2t5k3P87y2LZmoZuJZ35UvOAslmmmmmnGItnXQvezn5ytCyX2eNm5bRe4InU0vi4pZA69524YUXZtV0Tttjl8VKPhf9MqtO3IFTypqzgPKeoqXGL774ordMmeElZb6T8nGMnqt7nO4V+VKha+Tee++1NddcM1/19BJf3/1V15P7WJS3XrkKXOAik4xxySkv00sTnXIx7rRMmdwMOOWrOYvXTN5Y7USXJmvprHO4PybdVeqed9FFF9nuu++eGZNcA8hFwGiSliCLUZC0DLarqys4LGrrIvRm3HgU+nus5d/bKBT3sSLr2ta9UsuIk6RyPLtU6jrU+OrhWtQ4nDV35rlWbjKcv0xlkyAAATSTEIAABMaDwM0335z5MubuvCmZ/4dTOHqWyp2SYFRLYKJWTLvssku4m3RodbUf/JM1yGjTcsstl2mnmEiKvv7CXw4lm1OK+U5LqZ9Adjn3H88UdfY+mmW+lZS/VFaykAjYa+v8IJbaZLp+dJmie/jO266Ws4ZlePvtt/Oem6RAVp3hpYGl/D0k6U/nRJf+hMejffeilaipqHPzaDtxQV2iHchJfrR+cKzl9r4ky5Qwu+D8YOv8WfqqxeYp2lxQ37f1WaW6F/u8dWTRltRKUIIpQIuv3yAvzipUlrXRgCNBPW2L4VEoem6ha8QpLWPH4bu/Foq0u/zyy6dkkVqOf0nvnQoaEnbmHubp25dVaFyE5tiLr4hCRfsN969gGGOVKnXPiwaMkZXVaJPuRQGveeaZZ7TNpOvJGjJoyynnYtuq5d/b6MCiUeiTBvtSO+V4dqnUdSj56+Fa1DiC39nxeM5QfyQI1AoBlqzWykwhJwRqnIBzUp95iNTDZFhRppdA93U/qzwuclgcCvmnCr8UOuuRzOl6YYwuSXJWMZnyYnaiPkm03LaUFI1Em+8Fx1lWZDhJGZAkgmQpcoXr/uQnP8n0rTkcjTKokvKHxzKafflrC16EtHUBOEbTTE6d6IuGs5zJOSfIWGKJJTIy6Dov1d9bdImkCywSdDVmW+fQOTOGMM9gP+mLlguCEduOs0pJPIY4pZaWpfqSrv9AZt/WWYv5qsXmaUmqr60gLxp9NnpfDc4Lts4yLra/aKGUOUFd39ZZ5EarZI6jUa6j9fNxzDQQ2inkF6/QNRJ9gY3K4ru/Ogvn2LHvuuuuIQnHb9cFn0i5QEixsoXHp9+8sf5dOPjgg7Pk0ZK6sUqVuud95zvfyYxR94dSkuYkmCP5Bxxt0keAsP/NAw44ILapWv69jQ4s6g+vGBch5Xh2qdR1KA71cC3q2g2ezX0flqLzzTEEGokACrlGmm3GCoEKEwgrw5ZccsmMNMcdd1zmYVUPrbKkcBFGM+XF7siSIXj41cOrXmiU/vznP2fyVa6H5NEqNNyS16y2fvnLXxYrZtb5boliVnsKcOFL8rsRjE1btyTPd9qY5ElZE/Q92heUSspfKhS3xC0zfnGQErUcKfqiIcVAvjTXXHNlZJAVaanJRdPMtKcxuWVVpTZZsH7Yn2RwPYW3SV+0or4gw21oP6kFY9RhdrSd7373u94xffrpp1nsovUK+QmLNqp7XthpebQ9HbsI1FnVCll1yUJOHymSpug9MirD7373u7xNKahN9PzwcZxvxGijhRSTha6R8AtsWIZg/8knn4x2mbYkDCs7gnODrXxIVTIpsEhcoIpATm1dpNwxFTXsx0z9ycJ7rFKl7nlhX4qy7Blt0jNG+O9aft1Gm9xy8ay/Md0D41It/95Gx6XgJcE1rmuimFSOZ5dKXYcaZz1ci+FrV6sCSBCAwJcEUMh9yYI9CEBgjAnoxTZ4oFI0VUWG00uo852WyVe58zVXkiTO70tWe7L60EOxC3uflV+KQuXOO+/MaksvsqWksMNgscmnkJQlXsBQ23JZaSWRPRwFdrQvKJWUP8kY486RVU2YvZbVlSOFXzScn7LYJsPLlZz/sthzkxRG/ybG8sU6kGfxxRfP4hhmWsyLlqy1wnWj+76ALoEM4e0999wT247z6Rc+PWs/atkblkF/x1LaJU2XX355rBzOv5k3qm848nG4/2Dfp3zKJ1P040jQRrCVM/98yfnijJVfVmtJkiwp5p133rxt6feiUAq/wAayB1vNiyJt+5L+poLzotuNNtrIV2Vc87RMOxyROSpjcDzWwVmOPvroLE6XXHLJmHGo1D1v5plnzoxR9+jRpqjlqJb7jjY5/5sZmTTXshKOS7X8exsdV3jptiz/iknleHap1HWocdbDtahncD2vKtL2WFvwFnNtcC4EqoEACrlqmAVkgECDEIj6jrrttttS0ehjernUMqxSUtRyRksCnZPwrAdZPVzliyaapO+oP7F8URiTtKVzwr5e4hQtMvUPXrq0LdUyL6l8Oi8cBdYFtCimaubcSsqfEWKUO4oQGWbvAo6MsqXsauEXDb04xCXnWDojQ0tLS2yEvbh2VBZdnqi/vbF+UJayRVZbYY7hff0dJE2FLO3kfytJuuCCC/LKI9lcoJi8zSy11FKxdZ0j+Lx1wwXy81ZI0ZJvObGWxoYZRvflTy1pKhSVURFy8yUpZaJ9h4//+Mc/5quaydc92QW1iG0nyct4+AU2LIP245bxxkXNnWWWWcb87yMDImZHSt44OYPxuuAsMa2UViRLyaAfbbUkcKxSJe550aXgsmIebYq6BSglMvYZZ5yRxV0K2rhUy7+30XGFP34Ua61ajmeXSlyHYlAv12J0PjmGAAS+JIBC7ksW7EEAAmNM4P777896mDz22GNT4a+OerAv5BMliYjRB2D5I1p11VWz+paSrpR05JFHZrUna79S0txzz51pb911183blCxUwi9CLkpV3nPLWRD1r3TEEUeMqvlKyT8qYSOVpDTRcuqAv6zZpGAqNYVfNPIpXYI+fvCDH2T6lxyvv/56UFT0Nmyxqrb22muvotsotsLLL7+cJX/AMtjKoiNpirO0k3P7pMEMCvkbc9Hg8ookq6lAdt/2hBNOyFs3XCCln69+OC+fv0sX+Te2rovgmyq0xDOQRf6twn2G97WcM275a6FADIUskjVf8tMW7tO3X8iXW/QFNtqGi5QZDDdnW6j/sbRIli9VF902RyZfhs4NL6GLjlHHsrgcq6Sl0+E+XTTPseoqVYl7XvRZRR/gRpvOPffcLFb6sDPaFPa/mcSHbC3/3oYZuUjDWQz17Jg0levZpRLXocZYL9di0vniPAg0IgEUco0464wZAhUi0NfXl3HqGn6YD/Zl8fPKK6+ULF2hSIGyQCvVEmj77bfPPCAWEz3QN7jp06dn2hKLOMWIrCPEKWCm7Vi+JAbySikR7nO0Cs1KyR+Mo9RtVIlVSMlQqL/oi0YhSxP5eAvPQ9JlmVE55IRdS/fCbRVylB9tYzTHhZY1JrX4lCVVnKWdrM2SpkL+xuJ8+hVSQskyI86qTDLqBT2slA3PSbAv5a/un7508sknZ81jUCe83XPPPX1Vc/JkPRauF94v5BNPfsvC50f34/wTagnpTjvtFFs/aE/+NuNS9AU2qBds99hjj7zVo5bUQZ1gK4vIfMtd8zb6vwJFGl9//fVTUkr7kv72Nc/y65ikj6jf0UDGYJtUGeyTpVCePkCFIwzrNzCpMrFQ29HyStzzLrvssqxrUS4qRpuiPkKLWcYe7TOsfF9ooYWixTnHtf57GwxIv1fBda1tMcrmcj27VOI61Pjr5VoM5pItBCCQSwCFXC4TciAAgTEkEA0dH37I0otxudICCyyQ9QAX7qeQI+QkMoQDR8j6rpT0+OOPZ8la6EVq0003zTpfVoalKhgLyR+14CnFKW8l5C80vqTl0ReD+eefP6+/vyRtRts777zzYqtdd911WXOv5X3FJlk5ha9f/W2sueaaxTYzqvMVPTn8txjdT+qLUYr7aN3w8VZbbZVYvjh/Y3KiH5cUaTWsmAjLEOzLT18+BYv8u4WtY4M60W2c3ykpQuKixAZt/ehHP/L6oAvGJ7+VUSVtUFfbjTfeODjVu5UyNXx+dF9Lvnzp+eefTy233HKxdcNt6QU1LklJHj4/ui+FV76k+6j8r0XrhI+llI+zFIy2LdcM+vsK2tB4fSl8X5xnnnlSWuIbZ4EbjXQatB9sL7roIl83ZctbZZVVMmNSn3vvvXfZ2g43VIl7XlTZqSBOo01hH6FzzDHHaJtJ1wtH+lTgkyQpfF1pnsbjeSGJXMWco2X3wXWt7QMPPJC4ermeXSpxHWqQ9XQtJp40ToRAgxFAIddgE85wIVBpAnGO2BWlr1xp6623znqACx7m5BA838txMX2HLVoKLaEq1K6WogXyaVvI4k3KsPD52i81wICsdOKUenJsH+7zjTfeKDSsvOWVkD+vMKMoiPou0/yPNlqvXprDXAv5HJMCKByxT9aS+Sxu8g0t6uhblmbjYR0neX74wx9mjTc8du0/9NBD+cTOyv/b3/4W286hhx6adX7cgXyDReUIjgv59FO7hSKCqi2dE46IrMiuhx9+eKyVXyCDlKdxihnJoGX5wflxWwVLkNLTZ6WjyM5xdRVZMy5F/Wr62gr7kVOEWDEoFJQi2o4s4OLSMcccEzsOORWPS1IsRfuMHsuP3TPPPJO3GVm+ys9a1MegFKf5llL7FLNalq1AG+FrRx9wFPiokCL47rvvzitfOQqiShIpc8eiz0rc88JLl3V9jvb+Ls5hH6GluJiIWgXvt99+iaax1n9vNcioWwGtgkiayvXsUonrUGOs9WtRH4z0kTn8T/d+EgQg8CUBFHJfsmAPAhAYBwL5rBeSfu1NKmL0ISx4oYqzjkjadnSZYaElVIXaPemkk7JeAB977LFCVbIsLjS2CRMmpANXFKwYOUHLZffZZ590/3HKoJ133jkjoxRCpbygSISwxch4yB8ZdkmHUlxElQjyfTiaICHRaIVvvfVWQdmiS6DiluBFG/P9XZQa1TjaR9zxlltumbmOgr/J8Dbpsrezzz47th0Fi0mSCi1vL+TTT33cfvvtsZZlwfiksNBy+WhU6aDct5XftiQfKqSU1XJHXxv58mSFJX+VsrKUkllL4PKdq/xTTz01FmnUd2e+thShWVaJ8vOX75y4/EJBf6LLyqNtFeKpYCBhJ/DR+uFj+dyTsuCss85KfxSR1ZoUqPksDfMFpJCCNtyub1+8ks6xLDuT+g2MndSYQimJ5Z8wLKssuIr9QBB00dvbG+zmbMf7nifFWTAuWbiWksIf74oNRhDuN2oVrKXqSVMt/95qjOHl7PqAUkwq57PLeF+HGmetX4tS0gd/S8FW1oYkCEDgSwIo5L5kwR4EIDAOBF588cWcH2f9SMvipZxp6tSpOf0o8p7PMqTYfqPLDAstoSrUvr50Bw8q2kpJVii9+uqrqWgkQVk6KQpbPguMcJtSHsk6S36hgr7jFHLhpcaK6lhqGm/5S5U3Wt8XUVLL7or16RZ+WZD1TBJFp/w3zTbbbJl50/zJQipOIaiXc1/0SikIill+F+VQ7PGSSy6ZJXdw7WmrMSVNhx12WN521JaWCSZJWvoUliG6r+AtSVKh5YPRdpMeF6Ms1T1UCrykbRd7nhz5xyUpaMJBT4ptP8n5SV7GC0UgTfIbUCjybhJZfefIMbwv3XLLLWWdN1mij0eST8iopZ7uY7IOTPI7JBll8aiPQlI25lOkj/c9T0tLg/kbbURxjS2qaC0mGIHqh1PUKnjatGnh4tj9Wv+9DS+P1n4xqZzPLuN9HWqctX4tnn/++Zm/peBv6oknnihmCjkXAnVPAIVc3U8xA4RA9RGI+uiRRUG5k77SBz/+wVaWDOVI0aU6xfgz8fUfjtQoq5WkSV8ZowEeNFZZn0jZ5rM2kvWdfD0ttthiOXziFHLhOdNy4HKk8ZS/HPJG28jnxH6bbbZJbCUSflkoxhJD1krBdR1st9hii9Q999yTCluavP/++2kn8bLMCs4Ltuo7iXIiOu7RHkthKEvOoP/oVoqUpElRHaP1w8d6AU2S5Bw8XC+6nzS6opgrkES0finHcX7j8o1NS0JL6TOu7rPPPpuv20z+IYccUlL/sjYOL8mOyrPSSitl+sq3E1VWh9soxoeX/o7Ddcuxr4ibviTL7XK0rzaktJSV33ilqJVvMA5ZZV1xxRUpWRSHlf5Satx3332pE088MedvJs6H5njd83RPDMag7Wgjiou/7sfhtooJRhCdPykrw22JazGpln9vw24FivU1XO5nl/G6DjW39XAt+p6TPv/882IuXc6FQN0TQCFX91PMACFQfQSiToZLeUjNNzpZGoWXHemrfaGlTvnaiuZH/Zl89NFH0VOKOg5bqWkJVDHppptuyhuhUdYyWoYmBZ2UBXHLndZYY42UluL6ksYXfhEoxj+Xr71w3njIH+6v3PtS2PiifYr9sssum5JDb10vWqotaxJFp5XfLL3sqzysUN12220Tiyd/f3KSH56XYF/y6CVEETPzLZ3TMhi9GI9nkpIskNG31VLDpEnsfG0oT0q/pNY5sljJ147yi4muqOXGCvAS116SMslfSvRe+YgrZllsEpl0ncb5mAzmTS9a0WWMSdrXEktZnb755pux/HbcccegK+82eq+K9l2MDy9FtdVy8GgbpRw//PDDXrm1BDvf32ox/em+oyXU45n0W6sIsXFy6p6k5azyYRh3nhRY+dJ43fPkTzUsYz4laj45w/lRS8sHH3wwXFzUvn53A7l0rSgIS7GpFn9vo24FivmwGr0flOPZZbyuQ81tPVyL0SjmxXwUKfb65nwI1CoBFHK1OnPIDYEaJhB2ui2FUdxSu1KGGfabUo4HsUCW8AOGFB+lJC3zCi8zmzJlStHNySF/oWVawYN8dCv5Cy1z1UtEuF6+ZUVFC/6/CmMt/2jlSlpPFpJJHPuHGfr2pagrJunFIHwt+tqM5knZI8urQkECipEj6blaRhqVJ3ys+0KSJAVAR0dH3rbkSD9pkk+nsAzRfSmIikmakyRBAaL9BMeyFo6zVE0qi15io8E7gj5Gs9V9OmmSFa7PIjNfv/pA8/rrr6eb9zmgD9eTtUVcUlCQ8PnR/dEE4JGyLM6yM9pH9Fh1pWz+17/+FSd6Sq4Q5A4gWj/psayrpXCpVJLleJx1Ytw4pGDSx6hCH7fG45535ZVXZs2BfCOONkWtgwqNL64fRY4OGOrva7Sp1n5vo88fus6Spmjdcj27jMd1qDHWw7UYXm6s67cYS/ik88x5EKh1Aijkan0GkR8CNUjg5ptvzjxYnn766WM2Ajna1wOAnGErQla5kiyZggdjLfsrJWlJQti5+WiWqQX9X3XVVWkn7WEFXyBneKuXH0V+kyPyJBFnoz7zyu3vb6zlD9of6+29996bkoJn/vnnz1wfYe759mW9qWiehXx05ZNf9bTcNV/7yteSH/mZi4sMma/9cuXfeOONsTLKQipJkmVINKhGeOwKHJE0xSk0SwleIksfOSJPqshZZ5110haUSeVOet6TTz6ZUiAP+TcMMypmX74q991336Rdps/T/VbKr3zjl8Wd5knXRDgV8qUmi6O4FL1XRcc52gA8su6UckVKr2ib+Y4XWGCB9HJxLRtPmrS0U1G2tfw8bD2brw/la370t11M5Mmk8hR7nqxujzrqqJTGHidzUKbr8te//nVGIZu0v7G850X9gxarlA+PQR9ZgrGW+vEubBGtYCylprF6XihVrmj9qFsB/Y0nTdH7QbmfXcbyOtQY6+FaDEcZ1t+CfhdJEIBANoEmHbo/EBIEIACBcSPgfJuZ8ztjTjFkzo+MuZfrMenbWZqYe+g0F9XPnNPoMemjGht1Sj7T2N2LhLmXQXNf5c0pfcwtYzP3omTu5d/csoFqFD0tU63Lr0E4H4bmlpuYe0lO/9McuGWU5l6e0/+cgszc0j5zy7jMLeMqy1w8//zz6Xl3ypB0n+rDKQdt4YUXtrXXXtuccqQs/dBIcgL6+7v++uvNRUg0ZwVmb7zxhjmL4PScLL744uZ8OZp7YUnfo5K3OrozX3jhBXMO9M0tTc/8++CDD8wpfswtGU3/cxEh01uneDK3zN2cxZZpf7TpvffeM+cvLD12/S3MPffc6bE7S8CS2h2tPKXW09y5pef29NNPp+dS8yme+rvWXIb/OUvNNNvR9ukCsaSvG6cMtOCf8w1nzro1zU73DeeOwFyEXHMfdUbbzZjVEyNnPZz+DdLfgZJk1m+Q7kvaumX1JfXPPa8kfOnK1f5763zemvuokBmoC5SRvo9kMqpgh+uwCiYBESBQwwRQyNXw5CE6BCAAAQhAAAIQgAAEIACBeiTglt7bxRdfnB6aPhg4P5X1OEzGBAEINDABFHINPPkMHQIQgAAEIAABCEAAAhCAQDUScL6AzbmDSIsma2Lno7IaxUQmCEAAAqMmgEJu1OioCAEIQAACEIAABCAAAQhAAAJjQUDL3LX0XclFLTfnY3EsuqFNCEAAAhUjgEKuYujpGAIQgAAEIAABCEAAAhCAAASiBLQ8dcYZZ8xkH3HEEWm/w5kMdiAAAQjUAQEUcnUwiQwBAhCAAAQgAAEIQAACEIBAvRDQ8lQXgTwznHPOOcf23nvvzDE7EIAABOqBAAq5ephFxgABCEAAAhCAAAQgAAEIQKBOCEydOtW23377zGjuuOMOW2+99TLH7EAAAhCoBwIo5OphFhkDBCAAAQhAAAIQgAAEIACBOiFw4okn2pFHHpkZzRtvvGHzzz9/5pgdCEAAAvVAAIVcPcwiY4AABCAAAQhAAAIQgAAEIFAnBLQ89U9/+lN6NBMnTrSenh5rbm6uk9ExDAhAAAJfEEAhx5UAAQhAAAIQgAAEIAABCEAAAlVDQMtT77zzzrQ8Sy+9tD3zzDNVIxuCQAACECgXARRy5SJJOxCAAAQgAAEIQAACEIAABCBQMoEFFljA3nzzzXQ7W221lV1//fUlt0kDEIAABKqNAAq5apsR5IEABCAAAQhAAAIQgAAEINCgBPr6+qyzs9NSqVSawCGHHGKnnHJKg9Jg2BCAQD0TQCFXz7PL2CAAAQhAS+TV/wAAQABJREFUAAIQgAAEIAABCNQQgbffftvmnXfejMRnnnmm7b///pljdiAAAQjUCwEUcvUyk4wDAhCAAAQgAAEIQAACEIBAHRB45ZVXbHh4OD2SeeaZxyZNmlQHo2IIEIAABLIJoJDL5sERBCAAAQhAAAIQgAAEIAABCEAAAhCAAATGlAAKuTHFS+MQgAAEIAABCEAAAhCAAAQgAAEIQAACEMgmgEIumwdHEIAABCAAAQhAAAIQgAAEIAABCEAAAhAYUwIo5MYUL41DAAIQgAAEIFAJAv2PXGBti21ozTPNX4nu6RMCEIAABCAAAQhAAAKxBFDIxeKhEAIQgAAEIACBWiMw+OLfrPf2/7Omtg5rX+sIa1tis1obAvJCAAIQgAAEIAABCNQ5ARRydT7BDA8CEIAABCDQSARGPnvLuq/azVKD3ZlhSyHXvubh1jShM5PHDgQgAAEIQAACEIAABCpJAIVcJenTNwQgAAEIQAAC5SMwPGjd0/ax4Q+ezWmzecb5rXPL06x5xnlzysiAAAQgAAEIQAACEIDAeBNAITfexOkPAhCAAAQgAIExIdB372k28OQV3rabZ17Aura7KL2M1XsCmRCAAAQgAAEIQAACEBhHAijkxhE2XUEAAhCAAAQgMDYEht64z3puPtQ1nsrpoKm5zTonn2ctsy+RU0YGBCAAAQhAAAIQgAAEKkEAhVwlqNMnBCAAAQhAAAJlI5Dq/tC6r97FRno/9rapwA4Tlp3sLSMTAhCAAAQgAAEIQAAClSCAQq4S1OkTAhCAAAQgAIHyEEiNWM+NB9nQWw9722tbaC3r2PQkbxmZEIAABCAAAQhAAAIQqBQBFHKVIk+/EIAABCAAAQiUTKD/kQus/6E/ettp7prTuna41JraZ/SWkwkBCEAAAhCAAAQgAIFKEUAhVyny9AsBCEAAAhCAQEkEht990nqu289SqeHcdpqarWurs6zlKyvklpEDAQhAAAIQgAAEIACBChNAIVfhCaB7CEAAAhCAAASKJ5Dq/9z5jdvVRj5/11t54ir72sSVpnjLyIQABCAAAQhAAAIQgEClCaCQq/QM0D8EIAABCEAAAkUT6P3bUTb4yh3eeq3zrGCdW59l5qzkSBCAAAQgAAEIQAACEKhGAijkqnFWkAkCEIAABCAAgbwEBp6ean33+AM1NLXPZF3bX2rNk+bIW58CCEAAAhCAAAQgAAEIVJoACrlKzwD9QwACEIAABCCQmMDIRy9b99Q9LDXc763TuenJ1rrQmt4yMiEAAQhAAAIQgAAEIFAtBFDIVctMIAcEIAABCEAAAvEEhvqt+5opNvzRK97zJnz1O9a+xg+9ZWRCAAIQgAAEIAABCECgmgigkKum2UAWCEAAAhCAAATyEui783gbeO4Gb3nLbEtY1+TzzFravOVkQgACEIAABCAAAQhAoJoIoJCrptlAFghAAAIQgAAEvAQGX77dem/7qbesqbXd+Y272JpnXsBbTiYEIAABCEAAAhCAAASqjQAKuWqbEeSBAAQgAAEIQCCLwMjn71j3VbtaamB6Vn5w0LHe0da25BbBIVsIQAACEIAABCAAAQhUPQEUclU/RQgIAQhAAAIQaGACI8PWPW1fG37/KS+EtiU3t471/s9bRiYEIAABCEAAAhCAAASqlQAKuWqdGeSCAAQgAAEIQMD67jvTBh6/1Euiecb50ktVmyZ0esvJhAAEIAABCEAAAhCAQLUSQCFXrTODXBCAAAQgAIEGJzD0n4es58aDHIVUDomm5jbr3PZca5ljqZwyMiAAAQhAAAIQgAAEIFDtBFDIVfsMIR8EIAABCECgAQmkej627qt3sZGeD72jb1/jUJvw1W97y8iEAAQg0GgERj5+zZpnWajRhs14IQABCNQ0ARRyNT19CA8BCEAAAhCoQwKplPXcfIgNvXm/d3CtC6xhnZv/1ltGJgQgAIFGIjD81qPW//C5NvzuE9a101+secZ5G2n4jBUCEIBATRNAIVfT04fwEIAABCAAgfojMPDvi63vgd97B9bcNbvzG3epNXXM7C0nEwIQgEAjEBh++99pRdzQ249mhtu2+CbWscEvMsfsQAACEIBAdRNAIVfd84N0EIAABCAAgYYiMPz+M9YzbR9LjQx5xt1knVudaa3zruQpIwsCEIBAYxAYePIv1nfvKbmDbWq2STv+2ZpnXjC3jBwIQAACEKg6Aijkqm5KEAgCEIAABCDQmARSA93Ob9xuNvLZW14AE1fa0yausre3jEwIQAACjUJgZPr71n35du7DxWDOkNsW29g6Njw2J58MCEAAAhCoPgIo5KpvTpAIAhCAAAQg0JAEev9+tA2+dJt37C1zfc26vnW2WXOLt5xMCEAAAo1EoO/e02zgyStyhywrue9cac0zzZ9bRg4EIAABCFQVARRyVTUdCAMBCEAAAhBoTAIDz0yzvrtP8A6+aeIMzm/cJdY8w9zecjIhAAEINBqBkekfOCu5yX4ruUU3tI6Njms0JIwXAhCAQM0RQCFXc1OGwBCAAAQgAIH6IjDy8WvWPXV3Sw31eQfWufEJ1rrIut4yMiEAAQg0KoFYK7kdr3C+5BZoVDSMGwIQgEBNEEAhVxPThJAQgAAEIACBOiUwNGDd1+xhwx+95B3ghOV2sPY1D/OWkQkBCECgkQmkuj+w6fIlNzyQg6Ft0Q2cldzxOflkQAACEIBA9RBAIVc9c4EkEIAABCAAgYYj0HfXiTbw7LXecbfMuph1TT7frHWCt5xMCEAAAo1OoO9fp9vAE5d7MDQ5X3KykiPiqgcOWRCAAASqggAKuaqYBoSAAAQgAAEINB6BoVfutJ6/HekdeFNru3Vtd6E1z7KQt5xMCEAAAhAwS3X/11nJOV9yPiu5Rda3jo1/BSYIQAACEKhSAijkqnRiEAsCEIAABCBQzwRGPn/Xuq/e1VL9n3uH2bHe0da25BbeMjIhAAEIQOBLAn33nWEDj1/2ZUZmz1nJ7fhnPmxkeLADAQhAoLoIoJCrrvlAGghAAAIQgED9ExgZtu7rvm/D7z3hHWvbYhtZx4a/9JaRCQEIQAAC2QRS3R/+z0quP7vAHbUtsp6zkvt1Tj4ZEIAABCBQeQIo5Co/B0gAAQhAAAIQaCgC/Q+cbf3/vsg75uYZ57Wu7S+2pgld3nIyIQABCEAgl0DffWc6K7lLcwtMVnKXOyu5hT1lZEEAAhCAQCUJoJCrJH36hgAEIAABCDQYgaG3HrGeGw5wo07ljLypudU6tznHWuZcJqeMDAhAAAIQyE8g1fORTb9sW+dLzmMlt/C61rHJCfkrUwIBCEAAAhUhgEKuItjpFAIQgAAEINB4BFK9nzi/cbvYiHNC7kvtqx1kE76+s6+IPAhAAAIQKECg//6zrP+xSzxnOSu5b19mzbMu4ikjCwIQgAAEKkUAhVylyNMvBCAAAQhAoMEI9Nx8mA29ca931K3zr2qdm59q1tTkLScTAhCAAATiCaSt5BRxdagv58TWhdaxzk1PzMknAwIQgAAEKkcAhVzl2NMzBCAAAQhAoGEIDDx2mfXdf4Z3vM2dszm/cZdaU+cs3nIyIQABCEAgGYH++3/vrOQu9pwsK7lLnZXcop4ysiAAAQhAoBIEUMhVgjp9QgACEIAABBqIwPAHz1nPtXtbamTQM+om69zydGudbxVPGVkQgAAEIFAMgVTPxy7iqvMl57GSa1tobevY9DfFNMe5EIAABCAwhgRQyI0hXJqGAAQgAAEINDqB1ECP8xu3m4189h8viokr7m4Tv7Gft4xMCEAAAhAonkD/A39wkawv9FR0VnI7XGLNsy3mKSMLAhCAAATGmwAKufEmTn8QgAAEIACBBiLQe/vPbfDFv3pH3DLncta1zR/Nmlu85WRCAAIQgEDxBBRAZ/pl2+SxklvLWcmdVHyj1IAABCAAgbITQCFXdqQ0CAEIQAACEICACAw+d6P13nmcF0bThEnWJUuNGebxlpMJAQhAAAKjJ9D/oLOSe/RCTwNN7t57sbXMtrinjCwIQAACEBhPAijkxpM2fUEAAhCAAAQahMDIJ29Y99TvWWqw1zvijo2Ot7ZFN/CWkQkBCEAAAqURSFvJyZec5x7cuuCa1rnZyaV1QG0IQAACECiZAAq5khHSAAQgAAEIQAACWQSGB637mj1t+MMXsrKDgwnLTLb2tY8IDtlCAAIQgMAYEOh/8I/OSu4CT8vOSm77i6xl9iU8ZWRBAAIQgMB4EUAhN16k6QcCEIAABCDQIAT67jnZBp6+2jvallkXsa7J7gWxdaK3nEwIQAACECgPgVTvpy7iqvMl57OSW2AN69z8t+XpiFYgAAEIQGBUBFDIjQoblSAAAQhAAAIQ8BEYeu2f1vPXH/mKrKllonVtd741z7qot5xMCEAAAhAoL4H+h861/kfO8zbatb3zJYeVnJcNmRCAAATGgwAKufGgTB8QgAAEIACBBiAwMv0D6756F0v1feodbcc6P7G2pb/lLSMTAhCAAATKTyDV99kXEVcHe3Iab51/Nevc4tScfDIgAAEIQGB8CKCQGx/O9AIBCEAAAhCobwKpEeu5fn8beuff3nG2LbK+dWz8K28ZmRCAAAQgMHYEYq3ktrvQWuZYauw6p2UIQAACEMhLAIVcXjQUQAACEIAABCCQlEDcC1/zDHM7B+KXWNPEGZI2x3kQgAAEIFAmAvFWcqs6K7nTytQTzUAAAhCAQDEEUMgVQ4tzIQABCEAAAhDIITD8zmPWff0PzJyVXDQ1NbVY57f+YC1zfzVaxDEEIAABCIwTgf6H/2T650sKtNMy59K+IvIgAAEIQGAMCaCQG0O4NA0BCEAAAhCodwKyvOi+ahcb6X7fO9T2Vfe3Ccvv6i0jEwIQgAAExofAF1Zy27qIq905HbbOj5VcDhQyIAABCIwDARRy4wCZLiAAAQhAAAL1SqD3r4fb4Gv3eIfXOu/K1rnl6WZNzd5yMiEAAQhAYPwI9D9yvvU/dI63w67J5zsruWW8ZWRCAAIQgMDYEEAhNzZcaRUCEIAABCBQ9wQGnrjS+v7lj9DX3DGL8xt3qTV1zVb3HBggBCAAgVogkOr/3EVcdVZyA9NzxG2d7xtffEDJKSEDAhCAAATGigAKubEiS7sQgAAEIACBOiYw/N8XrOeaPZ3buEHPKJusc/NTrHWB1TxlZEEAAhCAQKUI9D9ygbOS+6O3e6zkvFjIhAAEIDBmBFDIjRlaGoYABCAAAQjUJ4HUYK91T/2ejXzyhneAE76+i7WvdoC3jEwIQAACEKgcgVT/dGclt43fSm7eVaxzqzMqJxw9QwACEGgwAijkGmzCGS4EIAABCECgVAK9dxxrgy/c7G2mZY6lrWubc81aWr3lZEIAAhCAQGUJ9D96ofU/+AevEF3b/sla5lrOW0YmBCAAAQiUlwAKufLypDUIQAACEIBAXRMYfOFW673jGO8Ym9q6rGuHi615xnm95WRCAAIQgEDlCcRbyblgPFudWXkhkQACEIBAAxBAIdcAk8wQIQABCEAAAuUgMPLZW9Z91a6WGuzxNtexwbHWtvjG3jIyIQABCECgegj0P3qRs5I72yuQrJxb5v6qt4xMCEAAAhAoHwEUcuVjSUsQgAAEIACB+iUwPGjd0/ax4Q+e9Y5xwtJbW/s6R3nLyIQABCAAgeoikBro/sKXnIu8Gk2tX1nJOrc+K5rNMQQgAAEIlJkACrkyA6U5CEAAAhCAQD0S6Lv3NBt48grv0JpnXtC6trvImtraveVkQgACEIBA9REY+PfF1vfA772CdW1zjrOS+5q3jEwIQAACECgPARRy5eFIKxCAAAQgAIG6JTD0xn3Wc/OhbnypnDE2NbdZ53YXWMtsi+WUkQEBCEAAAtVLIDXQ8z8ruc9yhGwl4moOEzIgAAEIlJsACrlyE6U9CEAAAhCAQB0RSHX/16ZftYul+j7xjqp9rSNswrKTvWVkQgACEIBAdRMYeOwS67vfvzy1a6vfW8u8K1b3AJAOAhCAQA0TQCFXw5OH6BCAAAQgAIExJZAasZ4bDrKhtx/2dtO60DrWuemJ3jIyIQABCECg+gmkreQu39Z9dPk0R9jWr7iIq1sTcTUHDBkQgAAEykQAhVyZQNIMBCAAAQhAoN4I9D9yvvU/dI53WM2T5rKu7S+xpvYZveVkQgACEIBAbRAYeOxSZyXnV7x1bnWWtc67Um0MBCkhAAEI1BgBFHI1NmGICwEIQAACEBgPAsPvPmE9133fUqnh3O6amq1r67OtZZ6v55aRAwEIQAACNUUg3kqOiKs1NZkICwEI1BQBFHI1NV0ICwEIQAACEBh7Aqn+z63b+Y0bmf6et7OJq+xrE1ea4i0jEwIQgAAEao/AwGOXOSu5M7yCd255hrXOt4q3jEwIQAACEBg9ARRyo2dHTQhAAAIQgEBdEui99UgbfPVO79hav7KidW7lljY5KzkSBCAAAQjUB4HUYK+LuCpfcrkBfFrnWcE6v3V2fQyUUUAAAhCoIgIo5KpoMhAFAhCAAAQgUGkCA09dbX3/PNkrRlP7zDZpB+c3rmsObzmZEIAABCBQuwQGHr/c+u473TsArOS8WMiEAAQgUBIBFHIl4aMyBCAAAQhAoH4IjHz4knVfs4elhge8g+rc9GRrXWhNbxmZEIAABCBQ2wSwkqvt+UN6CECg9gigkKu9OUNiCEAAAhCAQNkJpAb7rHvqFBv55FVv2xO+tpO1r36wt4xMCEAAAhCoDwIDT/zZ+v71O+9gOrc43Vrn/4a3jEwIQAACECieAAq54plRAwIQgAAEIFB3BPruPN4GnrvBO66W2Ze0rm3/ZNbS5i0nEwIQgAAE6oNA+uPM5dvaSO/HOQNqnXt569zmDzn5ZEAAAhCAwOgIoJAbHTdqQQACEIAABOqGwOBLf7fev//MO56mtg7r2v5ia55pfm85mRCAAAQgUF8EBp640lnJneodVOfmp1nrAqt6y8iEAAQgAIHiCKCQK44XZ0MAAhCAAATqisDI529b91W7WWpgundcHesfY21LbOYtIxMCEIAABOqPgKzkpl8+2VK9H+UMrmWurzmL6XNy8smAAAQgAIHiCaCQK54ZNSAAAQhAAAL1QWB4yLqv29eG33/aO562JbewjvWO9paRCQEIQAAC9Utg4Mm/WN+9p3gH2Ln5qc5KbjVvGZkQgAAEIJCcAAq55Kw4EwIQgAAEIFBXBPruO8MGHr/MO6bmGed3S1UvsqYJnd5yMiEAAQhAoI4JDPWnreRGej7MGWTLXF91VnLn5uSTAQEIQAACxRFAIVccL86GAAQgAAEI1AWBoTcfsJ6bfujGksoZT1Nzm3VOPs9aZl8ip4wMCEAAAhBoDAJYyTXGPDNKCECgcgRQyFWOPT1DAAIQgAAEKkIg1fORTb9qF69/IAnUvsahNuGr366IbHQKAQhAAAJVQiDOSm7O5axrsou+TYIABCAAgVETQCE3anRUhAAEIAABCNQggVQqbRk39J8HvMK3LrimdW52sreMTAhAAAIQaCwCA09dbX3/9P8mdG72W2tdcI3GAsJoIQABCJSRAAq5MsKkKQhAAAIQgEC1E+h/9CLrf/Bsr5jNXXM4v3GXWlPHTN5yMiEAAQhAoMEIDA04X3LbmteX3JzLOiu58xoMCMOFAAQgUD4CKOTKx5KWIAABCEAAAlVNYPi9p61n2j6WSg175Gyyrq3OspZ5V/SUkQUBCEAAAo1KYODpqdZ3z0ne4XduerK1LrSmt4xMCEAAAhCIJ4BCLp4PpRCAAAQgAIG6IJDqn27dV+9mI5+/7R3PxJX3Mv0jQQACEIAABLIIDA9+YSXX/d+sbB20zLGMdW13fk4+GRCAAAQgUJgACrnCjDgDAhCAAAQgUPMEem/7qQ2+fLt3HK1zL2+dW59l1tziLScTAhCAAAQam8DA09c4K7nfeCF0bnKStS68lreMTAhAAAIQyE8AhVx+NpRAAAIQgAAE6oLAwDPTrO/uE7xjaZo4o3XtcIk1T5rLW04mBCAAAQhAwNJWcpNtpPuDHBgtcyztrOQuyMknAwIQgAAE4gmgkIvnQykEIAABCECgpgmMfPyqdU+dYqmhPu84Ojf5jbNsWNtbRiYEIAABCEAgIDDwzLXu486JwWHWlt+SLBwcQAACEEhEAIVcIkycBAEIQAACEKhBAi46Xvc1U2z4o5e9wk9YbgdrX/MwbxmZEIAABCAAgSwCaSu57ZyV3PtZ2TpomX0pF6X7wpx8MiAAAQhAID8BFHL52VACAQhAAAIQqGkCfXedYAPPTvOOoWXWxb9wxN3S5i0nEwIQgAAEIBAlEOcCoXOTE53F9TrRKhxDAAIQgEAeAijk8oAhGwIQgAAEIFDLBAZf/of13vYT7xCaWtudJcNF1jzzgt5yMiEAAQhAAAJeArKS+/P2NjL9vZziltmXTP+25BSQAQEIQAACXgIo5LxYyIQABCAAAQjULoGRz9+x7qt2tdTAdO8gOtY72tqW3MJbRiYEIAABCEAgjsDgs9dZ712/9p7SufEJ1rrIut4yMiEAAQhAIJsACrlsHhxBAAIQgAAEapvAyLB1X7efDb/3pHccbYttbB0bHustIxMCEIAABCBQkECcldxsS7jI3RcXbIITIAABCEDADIUcVwEEIAABCECgjgj0P3C29f/7Iu+Immec1y0nusSaJnR6y8mEAAQgAAEIJCEw+Nz11nvnr7yndmz0a2tbdD1vGZkQgAAEIPAlARRyX7JgDwIQgAAEIFDTBIbeeth6bjjQjSGVM46m5lbr3OZca5lz6ZwyMiAAAQhAAAJFERgesulXOF9yn7+bUy0dNEhWck1NOWVkQAACEIDAlwRQyH3Jgj0IQAACEIBAzRJI9X7i/MZ910Z6PvSOoX21g2zC13f2lpEJAQhAAAIQKJbA4HM3OCu5473VOjb6lbOSW99bRiYEIAABCHxBAIUcVwIEIAABCECg1gmkUtZz86E29OZ93pG0zr+qdW5+KtYKXjpkQgACEIDAqAikreR2cFZy7+RUb5l1MedL7hJ+d3LIkAEBCEDgSwIo5L5kwR4EIAABCECgJgkMPHaZ9d1/hlf25s7Z3EvRZdbUMbO3nEwIQAACEIDAaAkMPn+T9f7jl97qHRseZ22LbegtIxMCEIAABAjqwDUAAQhAAAIQqGkCw+8/az3T9rbUyJBnHE3WueXp1jrfKp4ysiAAAQhAAAIlEnCRvaf/WVZyb+c01DLrou6D0KVYyeWQIQMCEIDAFwSwkONKgAAEIAABCNQogdRAj3VfvauNfPaWdwQTV9zdJn5jP28ZmRCAAAQgAIFyEMBKrhwUaQMCEGhEAijkGnHWGTMEIAABCNQFgd7bj7HBF2/1jqVlrq9a17f+YNbc4i0nEwIQgAAEIFAWArKSu+Lb3o9DLbMsYl3fvgwrubKAphEIQKDeCKCQq7cZZTwQgAAEINAQBOKi2zVNmJR2pt08wzwNwYJBQgACEIBAZQkMvnCz9d5xrFeIjg2OtbbFN/aWkQkBCECgkQmgkGvk2WfsEIAABCBQkwRGPnndLVX9nqWG+rzyd2z0a2tbdD1vGZkQgAAEIACBshOIsZJrnnlhm7SjrOSay94tDUIAAhCoZQIo5Gp59pAdAhCAAAQaj8DwoHVP3cOGP3rRO/YJy0y29rWP8JaRCQEIQAACEBgrAoMv/tV6b/+5t/mO9X9hbUts4i0jEwIQgECjEkAh16gzz7ghAAEIQKAmCfTdc7INPH21V/a0r57tLjBrnegtJxMCEIAABCAwZgTSVnLfcb7k3szpomXmhaxrx8uxksshQwYEINDIBFDINfLsM3YIQAACEKgpAkOv3m09t/qt35paJlrX9hda8ywL19SYEBYCEIAABOqHwOALtzpfcsd4B4SVnBcLmRCAQAMTQCHXwJPP0CEAAQhAoHYIjEx/37qv2sVS/Z95he5Y5yfWtvS3vGVkQgACEIAABMaFgKzkrtzJRj59I6e75pkXdL7k/oyVXA4ZMiAAgUYlgEKuUWeecUMAAhCAQO0QcC84Pdfvb0PvPuaVuW3RDaxjo+O9ZWRCAAIQgAAExpPA4It/c77k/s/bZcf6xzhfcpt5y8iEAAQg0GgEUMg12owzXghAAAIQqDkC/Q+eY/2Pnu+Vu3mGedxS1UusaeIkbzmZEIAABCAAgXElICu5vzgruU98VnILOCu5K7CSG9cJoTMIQKBaCaCQq9aZQS4IQAACEICAIzD89r+t+/ofuL1UDo+mphbr3OaP1jLXcjllZEAAAhCAAAQqRWDwxducldzR3u471v8/ZyW3ubeMTAhAAAKNRACFXCPNNmOFAAQgAIGaIpDq/dS6r97FRro/8Mrdvur+NmH5Xb1lZEIAAhCAAAQqRiA18oUvuU9ezxGheSZZyTlfcs0tOWVkQAACEGgkAijkGmm2GSsEIAABCNQUgZ5bfmRDr//TK3PrfN+wzi1+55b9NHnLyYQABCAAAQhUksDgS3+33r//zCtCx3pHW9uSW3jLyIQABCDQKARQyDXKTDNOCEAAAhCoKQIDT1xhff86zStzc8cs1rXDZdbUOau3nEwIQAACEIBAxQk4K7nuK3e24U9eyxGlecb5bdJ3nC85rORy2JABAQg0DgEUco0z14wUAhCAAARqhMDwf1+wnmv2tNTIoEfiJuvc/FRrXWBVTxlZEIAABCAAgeohMPjy7dZ720+9AnWs+zNrW2pLbxmZEIAABBqBAAq5RphlxggBCEAAAjVDIDXQY91Td7eRT3Oj02kQE53PuInOdxwJAhCAAAQgUPUE0r7kvusirr6aI2rzjPM5K7krsZLLIUMGBCDQKARQyDXKTDNOCEAAAhCoCQK9dxxrgy/c7JW1ZY5lrGubc8xaWr3lZEIAAhCAAASqjcDgy3c4K7mjvGJ1rPtTZyW3lbeMTAhAAAL1TgCFXL3PMOODAAQgAIGaITD4wi3We8cvvPI2tXVZ17cvseYZvuItJxMCEIAABCBQlQRSKeu+6rs2/NErOeI1zzivs5L7C1ZyOWTIgAAEGoEACrlGmGXGCAEIQAACVU9g5NM3rfvq3Sw12OuVtWPD46xtsQ29ZWRCAAIQgAAEqpnA4Mv/cFZyP/GK2LHOT6xt6W95y8iEAAQgUM8EUMjV8+wyNghAAAIQqA0Cw4PWfe1eNvzf573yTnAvKu3uhYUEAQhAAAIQqEkCaSu5XZyV3Ms54mMll4OEDAhAoEEIoJBrkIlmmBCAAAQgUL0E+u491QaedI6tPall5oWsc7sLramt3VNKFgQgAAEIQKA2CAy9cqf1/O1Ir7BYyXmxkAkBCNQ5ARRydT7BDA8CEIAABKqbwNDr91rPLYd5hWxqmWBdk8+35tkW85aTCQEIQAACEKgZAmkruV2dldxLOSLLP2ralxxBi3LYkAEBCNQvARRy9Tu3jAwCEIAABKqcQKr7A5t+1a6W6vvEK2n7WkfYhGUne8vIhAAEIAABCNQagaFX77KeW3/sFbt97SNtwjLbeMvIhAAEIFCPBFDI1eOsMiYIQAACEKh+AqkR67nhQBt6+xGvrG0Lr2Mdm5zoLSMTAhCAAAQgUJME0lZyuzkruRdzxG+eYR5nJXeVGVZyOWzIgAAE6pMACrn6nFdGBQEIQAACVU6g/5Hzrf+hc7xSNk+ay7q2v8Sa2mf0lpMJAQhAAAIQqFUCQ6/e46zkDveKj2W4FwuZEIBAnRJAIVenE8uwIAABCECgegkMv/O4dV//fTNnJZeTmpqt61t/sJa5v5ZTRAYEIAABCECg5gnISu7q79nwhy/kDKV5hrmdldzVWMnlkCEDAhCoRwIo5OpxVhkTBCAAAQhULYFU32fuRWRXG5n+nlfGid/YzyauuLu3jEwIQAACEIBAPRAYeu2f1vPXH3mHgpWcFwuZEIBAHRJAIVeHk8qQIAABCECgegn0OmfWg86ptS+1fmUl69zqDDNnJUeCAAQgAAEI1DOBtJXcf5/PGaLcNkzaSVZybTllZEAAAhCoJwIo5OppNhkLBCAAAQhUNYGBp662vn+e7JWxqX1mm7SD8xvXNYe3nEwIQAACEIBAPRGIt5I73EUZ366ehstYIAABCOQQQCGXg4QMCEAAAhCAQPkJDH/4kvVcs4elhgc8jTdZ52a/tdYFV/eUkQUBCEAAAhCoTwLdV+9uw/99LmdwWMnlICEDAhCoQwIo5OpwUhkSBCAAAQhUF4HUYJ/1THUvHZ+85hVswtd2svbVD/aWkQkBCEAAAhCoVwJDr//Lem451Du89jUPswnL7eAtIxMCEIBAPRBAIVcPs8gYIAABCECgqgn03XmcDTx3o1fGltmXsq5tz8VXjpcOmRCAAAQgUO8EuqdOseEPns0ZZnPXnDZp56n8PuaQIQMCEKgXAijk6mUmGQcEIAABCFQlgcEXb7Pe24/2ytbU1mld219szTPN5y0nEwIQgAAEIFDvBIbeuM96bj7EO8z2NQ61CV/9treMTAhAAAK1TgCFXK3PIPJDAAIQgEDVEhj57C3rvmo3Sw12e2XsWP8X1rbEJt4yMiEAAQhAAAKNQiC/ldwczkruGqzkGuVCYJwQaDACKOQabMIZLgQgAAEIjBOB4SHrnra3dxmOJGhbcgvrWM9vOTdOEtINBCAAAQhAoCoIxFrJrX6ITfjajlUhJ0JAAAIQKCcBFHLlpElbEIAABCAAgf8R6LvvDBt4/DIvj+aZFnBLVS+yprYObzmZEIAABCAAgUYj0H3Nnjb8/tM5w27umt0m7eSs5Fon5JSRAQEIQKCWCaCQq+XZQ3YIQAACEKhKAkNv3P8/fzipHPmamtusc/J51jL7EjllZEAAAhCAAAQalcDQmw9Yz03+iOPtq//QWcl9p1HRMG4IQKBOCaCQq9OJZVgQgAAEIFAZAqnuD6376l1spPdjrwA4qPZiIRMCEIAABCBg3dfs5azknsoh0dw5m/Mldy1WcjlkyIAABGqZAAq5Wp49ZIcABCAAgeoikEpZz40H2dBbD3nlaltoLevY9CRvGZkQgAAEIACBRicw9OaDzkruIC+G9tUPdlZyO3nLyIQABCBQiwRQyNXirCEzBCAAAQhUJYH+Ry+y/gfP9srW3DWn8xt3iTV1zOQtJxMCEIAABCAAAbPua11ApPeezEHxhZWcfMlNzCkjAwIQgEAtEkAhV4uzhswQgAAEIFB1BIbfe8p6pu1rqdRwrmxNzda11VnW8pUVcsvIgQAEIAABCEAgQ2DoPw85a/MDM8fhnfbVDrIJX985nMU+BCAAgZolgEKuZqcOwSEAAQhAoFoIpPqnO79xu9rI5+94RZq48l6mfyQIQAACEIAABAoT6L52H2cl90TOiU0dszpfcte4KOXtOWVkQAACEKg1Aijkam3GkBcCEIAABKqOQO9tP7XBl2/3ytU6zwrWudWZZs0t3nIyIQABCEAAAhDIJjD01sPWc8MB2Zn/O2pf9UCbsPx3vWVkQgACEKglAijkamm2kBUCEIAABKqOwMAz11rf3Sd65WpqnyntN6550pzecjIhAAEIQAACEPATkBuIoXcfzyls7pjFulzEVazkctCQAQEI1BgBFHI1NmGICwEIQAAC1UNg5KNXrHvqFEsN93uF6tzkJGtdeC1vGZkQgAAEIAABCOQnMPTWI85Kbn/vCe2rHuCs5HbxlpEJAQhAoFYIoJCrlZlCTghAAAIQqC4CQ/3Wfc0UG3ZKOV+asNy3rX3NQ31F5EEAAhCAAAQgkIBAz3Xft6F3/p1zZlP7zDbpu7KS68gpIwMCEIBArRBAIVcrM4WcEIAABCBQVQT67jrBBp6d5pWpZbYlrGvyeWYtbd5yMiEAAQhAAAIQKEwgzkpu4jf3t4kr7Fq4Ec6AAAQgUKUEUMhV6cQgFgQgAAEIVC+BwZfvsN7bjvIK2NTa7vzGXWTNMy/oLScTAhCAAAQgAIHkBHqu/4ENvf1oTgWs5HKQkAEBCNQYARRyNTZhiAsBCEAAApUlMPL5O9Z91a6WGpjuFaRjvaOtbcktvGVkQgACEIAABCBQHIHht/9t3dd/31up/Zs/sAkr7OYtIxMCEIBAtRNAIVftM4R8EIAABCBQPQRGhq37uv1s+L0nvTK1Lb6JdWzwC28ZmRCAAAQgAAEIjI5Az/UHOCu5h3Mqp63kdr7GmiZ05pSRAQEIQKDaCaCQq/YZQj4IQAACEKgaAv33/976H7vYK0/zjPO5paoX81LgpUMmBCAAAQhAYPQE4qzkJn5jP5u44u6jb5yaEIAABCpEAIVchcDTLQQgAAEI1BaBof88ZD03HuSETuUI3tTcZp3bnmstcyyVU0YGBCAAAQhAAAKlE+i5wVnJveWzkpvJJu3sIq5iJVc6ZFqAAATGlQAKuXHFTWcQgAAEIFCLBFI9H1v31bvYSM+HXvHbVz/YJnxtJ28ZmRCAAAQgAAEIlE5g+J3HnduIfb0NTVxlX5u40hRvGZkQgAAEqpUACrlqnRnkggAEIACB6iCQSlnPzYfY0Jv3e+VpXWB169zst2ZNTd5yMiEAAQhAAAIQKA+BnhsOdFZyD+U01jRxRpv03WlYyeWQIQMCEKhmAijkqnl2kA0CEIAABCpOYOCxS63v/jO9cjR3zmZdO1xmTR0ze8vJhAAEIAABCECgfASG333Cuqft420QKzkvFjIhAIEqJoBCroonB9EgAAEIQKCyBIbff8Z63IN/amTII0iTdW51hrXOu7KnjCwIQAACEIAABMaCQM+NB9vQfx7Iabpp4gz/s5LryikjAwIQgEA1EkAhV42zgkwQgAAEIFBxAqmBbuc3bjcb+ewtrywTV5xiE7/h92XjrUAmBCAAAQhAAAIlE4i1klt5b5u48p4l90EDEIAABMaDAAq58aBMHxCAAAQgUHMEev/+fzb40t+8crfM9TXr+tbZZs0t3nIyIQABCEAAAhAYOwI9N/3Q69s1bSWniKsTJ41d57QMAQhAoEwEUMiVCSTNQAACEIBA/RAYfO4G673zeO+A9LDftf0l1jzD3N5yMiEAAQhAAAIQGFsCw+89Zd3X7uXtZOLKezkrOX+ZtwKZEIAABCpEAIVchcDTLQQgAAEIVCeBkU9ed0tVv2epoT6vgJ0bn2Cti6zrLSMTAhCAAAQgAIHxIdBzkyKg35fTWdOESV/4ksNKLocNGRCAQHURQCFXXfOBNBCAAAQgUEkCQwPWfc0eNvzRS14pJiwz2drXPsJbRiYEIAABCEAAAuNHYPi9p52VnN9f3MSV9rSJq+w9fsLQEwQgAIFREEAhNwpoVIEABCAAgfok0Hf3STbwzFTv4FpmXdS6Jl9g1jrBW04mBCAAAQhAAALjS6Dn5kNt6I1/5XT6hZWcfMnNkFNGBgQgAIFqIYBCrlpmAjkgAAEIQKCiBIZevct6bv2xV4am1nbr2u5Ca55lIW85mRCAAAQgAAEIjD+B4fefSVu2+3qeuOIeLhr6Pr4i8iAAAQhUBQEUclUxDQgBAQhAAAKVJDAy/T3rvmoXS/V/7hWjY92jrG2prb1lZEIAAhCAAAQgUDkCPTcf5qzk7s0RoKmty/mSc1Zy7TPmlJEBAQhAoBoIoJCrhllABghAAAIQqByBkWHruf4HNvTu414Z2hbd0Do2Os5bRiYEIAABCEAAApUlMPz+s85KbopXiIkrTnFWcvt6y/6fvfMAk6JI3/i3pF2SCVAxoIAoICKiAqJ3KKKnCJgDoniIgAkxnaIimBXuVAyYE4b7K5jFUzHnHAADIIgJs4KSd9ntf719Vl11T3dPz2zPbu/uW88DHSp99evemZ63v/qKJ0mABEiguglQkKvuK8D+SYAESIAEqpXAmndukTUf3BFoQ73mm0jTQ++RokZNA/N5kgRIgARIgARIoPoJrHzqTFn71WsZhtBLLgMJT5AACaSIAAW5FF0MmkICJEACJFC1BMoXfyArnjhJdepkdFxUr4E0OeAWqb9h54w8niABEiABEiABEkgPgfKf58qKh/4eaFDxDn+X4p7HB+bxJAmQAAlUJwEKctVJn32TAAmQAAlUGwFn1VJZ8eBRUrHil0AbSnqdLI26HRWYx5MkQAIkQAIkQALpIrDq6X9I2ZevZhhV1LCJiiX3KGPJZZDhCRIggeomQEGuuq8A+ycBEiABEqgWAmFBoGFMg816SpP9JosUFVWLbeyUBEiABEiABEggNwLlP89TXnLHBFYq3uEY5SV3QmAeT5IACZBAdRGgIFdd5NkvCZAACZBAQQmUzv4/adh+Lylq2jKjn9LZ98vqN5TgFpCKGm8gzQ69V4qabBCQy1MkQAIkQAIkQAJpJbDq6bOUl9wrGeYVNWwszY5UXnKN183I4wkSIAESqC4CFOSqizz7JQESIAESKCiB5f8+RJzS5dK47wRp0GYX0xfeoK985DhxKsrMuf/tFCnPuGukweY9/neKeyRAAiRAAiRAAjWCQPkv81U4CnjJZcaGLe42VIp7nVgjxkEjSYAE6gYBCnJ14zpzlCRAAiRQtwisXSN/3La7GjMeyIukUdfBUtLzRHHKS90H9Yo/vgnkUdztaPWwjkUemEiABEiABEiABGoigVXPnC1li17OMP2/XnKPKC+59TLyeIIESIAEqoMABbnqoM4+SYAESIAECkogKI5M/Vad1PTVDWXtl5kP6TCm/obbSlO1qqrUq19Q29g4CZAACZAACZBA4QiU//q5rJg+VHWQ6SXXaPujpGSXkwvXOVsmARIggRwIUJDLARaLkgAJkAAJ1AwCZfOfklUvXBjb2KJGzaTpofdIveatY9dhQRIgARIgARIggXQSWPXMWOUl91KGcUUNSv674iq95DLY8AQJkEDVE6AgV/XM2SMJkAAJkECBCax+83opnXVv7F4a97tEGm7VL3Z5FiQBEiABEiABEkgvgfJfFygvuaOVgfSSS+9VomUkQAIU5HgPkAAJkAAJ1DoCK/9zhqz9+vVY42rUaX8p6XNOrLIsRAIkQAIkQAIkUDMIrJp5rpR98UKGsa6X3JEqllyT9TPyeIIESIAEqpIABbmqpM2+SIAESIAEqoTA8vsOkIplP8Tqq6TXydKo21GxyrIQCZAACZAACZBAzSBQobzklod6yQ1RseRG14yB0EoSIIFaS4CCXK29tBwYCZAACdRNAk7pSll2x55q8JnTVMKINNzyr1Ky+zgpKlknrAjPkwAJkAAJkAAJ1DACq549T8oWPp9hdaSXXEU5F3jKIMYTJEAChSBAQa4QVNkmCZAACZBAtREo//ETWfHI8Jz7r6dWYG3c7yKp37pbznVZgQRIgARIgARIIH0EKn77QpZPG6IMy3xJ16jrYCnpPeZ/RpevlbLP/yOls++Xpof9+3/nuUcCJEACBSJAQa5AYNksCZAACZBA9RAom/uErHrp0vw6L6qnprCMkUZdD8+vPmuRAAmQAAmQAAmkikCol1z9YrXiqoolV7KulM3/j6x5/04V7uI7gfdc8+NeStUYaAwJkEDtJEBBrnZeV46KBEiABOosgdWvT5bSOffnPP6ihk2k0XaHS6Ptj5Si4uY512cFEiABEiABEiCB9BGoWLJIlj9wpDIs00uuwWY9pOKPxe4/bXmREuqaj3hZH3JLAiRAAgUjQEGuYGjZMAmQAAmQQHUQWDljjKz99u3YXeNNeMNtD5FitbBDUeP1YtdjQRIgARIgARIggZpBYNVz50vZgmdjGUtBLhYmFiIBEkiAAAW5BCCyCRIgARIggfQQWH7PAKlY8UtWg4rqNZSGnQ+U4h2OkaKmLbKWZwESIAESIAESIIGaSaBiyZfKS26wMj7TS84/oqL6jZSH3Cv+0zwmARIggcQJUJBLHCkbJAESIAESqC4CzpplsuzOvSK7L6rXQBpuM0Aa7Xis1Gu2YWRZZpIACZAACZAACdRwAmrV1LIFM2X1q/8Sp2xF1sFQkMuKiAVIgAQSIkBBLiGQbIYESIAESKD6CZR/P0tWPDYq2BC1YEPDrfeR4h2HS711Ng0uw7MkQAIkQAIkQAK1g4BToVZNfVYt1nC7VPz+dewxwYO++chXY5dnQRIgARLIlwAFuXzJsR4JkAAJkEDqCJR++oisfmWiz64iadh+TyneeYTUW28LXx4PSYAESIAESIAEahUBLcR9oIS4pfGFOM2AgpwmwS0JkEChCVCQKzRhtk8CJEACJFBlBFa/eqWUfjLd9Ndgyz5SAiGuxVbmHHdIgARIgARIgARqN4HSOdNkzZvXiVNRlvNAKcjljIwVSIAE8iRAQS5PcKxGAiRAAiSQPgIrHz9J1n73vjTYfBcp7jFK6rfqmD4jaREJkAAJkAAJkEDBCZT/9Jmsem6cVPyxOKe+EGu2+cjXcqrDwiRAAiSQDwEKcvlQYx0SIAESIIFUElj17DhptN1hUn/jrqm0j0aRAAmQAAmQAAlUHQFnzXJZ/fJlUvbFC7E7pSAXGxULkgAJVJIABblKAmR1EiABEiABEiABEiABEiABEiCB9BIo/eQhWfP65FhTWIuK6kvzUa+ndzC0jARIoNYQoCBXay4lB0ICJEACJEACJEACJEACJEACJBBEoPyX+bJq5rlqCuu3QdnmHAU5g4I7JEACBSZAQa7AgNk8CZAACZAACZAACZAACZAACZBA9RNwSleq1dgvl7IFz4YbU1RP1hn1Rng+c0iABEggIQIU5BICyWZIgARIgARIgARIgARIgARIgATST6D000fUFNarxSkvzTSWglwmE54hARIoCAEKcgXBykZJgARIgARIgARIgARIgARIgATSSqD81wWy6lk1hXXp1z4Ti2Sd49/0neMhCZAACSRPgIJc8kzZIgmQAAmQAAmQAAmQAAmQAAmQQMoJuFNYX50oZZ8/Y1lKQc6CwV0SIIECEqAgV0C4bJoESIAESIAESIAESIAESIAESCDdBMrmPi6rX71STWFdowylIJfuq0XrSKD2EKAgV3uuJUdCAiRAAiRAAiRAAiRAAiRAAiSQB4GK3xaqVVjPk/KlX6opq2/l0QKrkAAJkEBuBCjI5caLpUmABEiABEiABEiABEiABEiABGohAadslax+7Z/SeI/xtXB0HBIJkEDaCFCQS9sVoT0kQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAK1mgAFuVp9eTk4EiABEiABEiABEiABEiABEiABEiABEiCBtBGgIJe2K0J7SIAESIAESIAESIAESIAESIAESIAESIAEajUBCnK1+vJycCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAmkjQEEubVeE9pAACZAACZAACZAACZAACZAACZAACZAACdRqAhTkavXl5eBIgARIgARIgARIgARIgARIgARIgARIgATSRoCCXNquCO0hARIgARIgARIgARIgARIgARIgARIgARKo1QQoyNXqy8vBkQAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJpI0ABbm0XRHaQwIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkUKsJUJCr1Ze3Zgxu8eLFcvPNN8sXX3whw4cPlz322KNmGJ4iK++8804pLS3NsGjPPfeUrbbaypyPW85UqKadBQsWyPPPP5/Re6NGjWTYsGEZ53M9UV5eLgcddJB8+OGHcuCBB8o111yTaxMsTwI5ETjppJPkiSeekM6dO8vjjz8uuJeZSIAESIAESIAESIAESIAE6i4BCnJ199qnYuSrVq2STTfdVJYsWWLsmTp1qgwdOtQc17SdiooKefDBB6Vv377SsmXLKjG/fv36gn79CULnyJEjzem45UyFatq55ZZbZNSoURm9w/61a9dmnM/1xAUXXCAXXnihNGzYUD744APp0qVLrk3U+PIrVqyQpk2b1vhx1JQBvP3227LLLruI4zhy3HHHya233lpTTKedJEACJEACJEACJEACJEACBSBAQa4AUNlkfAJ33XVXhsfTbrvtJq+++mr8RlJUEqIYPLjuvvtuefrpp+Vvf/tblVgXV2iLW65KjI7opJCCHO4teGHCS27s2LFy+eWXR1hS+7LKyspk0KBBMnPmTOnRo4fMmDFDWrRoUfsGmsIRHXPMMe5nA0y78cYb5fjjj0+hlTSJBEiABEiABEiABEiABEigKghQkKsKyuwjlMD1118vo0eP9uTXVEFOe77ccccd7ngoyHkua04HhRLk4Im5/fbbyzfffCObbbaZzJ8/Xxo3bpyTbTW98PTp0+Wwww4zw7jqqqvktNNOM8fcKRyB7777TrbZZhtZvny5lJSUyPvvv+9OYS1cj2yZBEiABEiABEiABEiABEggrQQoyKX1ytQRu7788kvZYYcdZOnSpWbENXHKKsQ4eLtASNKJgpwmkfu2UIIcpkLfc889rkE33HCDnHDCCbkbV8NrvPTSS544jfBShecWU9UQgEfmueee63a24447yrvvvitFRUVV0zl7IQESIAESIAESIAESIAESSA0BCnKpuRR115DZs2e78ZT++OMPd7rn7rvvXuNgnHzyyTJlyhSP3RTkPDhyOiiEIDdnzhzp1q2bG2tv8803FywcUVcD61900UUybdo0V5ibNGlSrfMShMA/YcKEVC7WsWbNGjdu5q+//ur+Tdx7770yZMiQnP4+WJgESIAESIAESIAESIAESKDmE6AgV/OvYY0ewbPPPhu4Omj37t2ldevW7tjw4/r111/PGGe9evVk7733Fh3of+7cue6qmbNmzZLi4mI3UD8EmE6dOmXU9Z/4/fff3boI8L9w4UJp0qSJbLHFFq733k477eS256+jj0899dTAH/7nn3++9OzZUxdztxCA9tprL885HCDA/iuvvCKLFi2Sn376SX755Rd3XBtssIE7xRKxvjbZZJOMevpE3Nhwccvpdv1bePNA2Prhhx/cf+CMqZ9g1Lt375w8fTBdD6ucwkvyt99+k+bNm7vT9/bZZx957LHHEl/UAaupPvroo+6QcM2uvvpq//B4XAsIYFoyPhd+/vln995K45BsT82OHTvKp59+mtPfThrHRJtIgARIgARIgARIgARIgARyJKCm2jGRQLURUKKao27ZjH9qdVBj03/+85+MfF1HiVfObbfd5rRq1Sq0jJqW6KxcudK0Z+/MmzfPUWKSo6aMhdZff/31HbShPKrsqu7+GWecEVpP22hvMV6d1AIQjvJScvbcc09HCXWR7cC+Qw891Pn88891dc82DkdUiFvOblythOuoVUmddu3aRdqovM4c+7rZbdj7asqko+IEhralYro5bdu2DcxXgqLdVOx9JSR62lPei7HrsmDNIaCEbEdNgXevtRLUU2u4iuPnuR+feeaZ1NpKw0iABEiABEiABEiABEiABApDgB5ySi1hqj4CcTy2nnrqKenfv3+gkfvvv7/rTRWYaZ3ENNgXX3zROiNuPXiqYKpsnOQPfn/OOefIFVdcEaeqKaMEMXd1T3iZDR8+3I0fZTJj7MBLDl5qfm+5OBzRfNxy2pSPPvpIjjzySPnss8/0qaxbeLg98sgjbtB6f2GsPotxr1271p8V6xj251N38ODBcv/997t9YBEHeOQhqH5YUh+3gthq8FqEF98XX3wh7du3F3hu7rvvvnLIIYeEVY11Hh6QSmh2PSLhIQgPyaAEu+HZl2RCX8cee6xgjEhnn322IJaZTvBIHTlypD4UeHput9125ljvwMNx4sSJ7iFioGExk6ZNm7oLZYwbN04XM9uGDRuKElpdjltttZXsvPPOnmuAKZx2TD/wjloBF4vB/Pjjj277uC/g8YhVjTEFHgn94fPBTr169RIlotunXA75XGu0g8VBkAYOHChHH320LF682F25FyvYwssWnztggbHYadmyZdKyZUvjHYzr/O9//9suwn0SIAESIAESIAESIAESIIHaTnLjglYAAEAASURBVKAwOh9bJYF4BOJ4bEV5yKm/T4+nSdSx+pFsjFKrHDrwxIoq789Tgpypf+GFF+ZUV7eF8SIp8SCv+minT58+bhv2f3E4onzcciirhCKnRYsWedmpxAk04UnPPfdcpCeiZhS1VcKLp804B2VlZc56661nxqEEw8hqaiVMp1+/fqZ8kD1qEQRHCVuR7QRloo4Scp1mzZpFtq/7VGJYUDOVOqdELE/fDz74oKc9XHfdP7YzZszw5OsD1LPLoV2kF154wXPeLmPvw4Ptvvvuc+ApioStmmLuqaumkLt5/v9efvllTzklfDnbbrut55zdl97fb7/9PE1V5lorUdH0p2JIut6rSig353SfuNYPPPCAp18cqGm1pqyamu4ooTmjDE+QAAmQAAmQAAmQAAmQAAnUXgLwDmAigWojEEcgSkqQww9gnfxigv7xjC1+VCvPGmeXXXZxlJeN+dFsC3Lbb7+9OW/XzbYfJchtuOGG7vTVww47zJ1GG9YW2lAeNnoo7jYORxSMWw7iiPJgCh2j8kRyTjvtNAfbMDuVl5yxUQWyd1SsrNCyYW34z+cjyPnFG4ihYUl5aTkbb7xxLDsx9lwSBBflSRWrbT1utTBBLl3EKpsWQU6PEdOXMS0a6aGHHvLwUZ6IgWMaMGCApxzEPd1e1NYW5Cp7rW1BbtiwYc7WW28dasOll16aMY4rr7zSU/61117LKMMTJEACJEACJEACJJAWAuXl5e5LRhWnNy0m0Q4SqPEEKMjV+EtYswcQRyCKI8hBHDnvvPMiBaJNN93UwFJT8jw/hvWPeMRyKy0tNeXU1EZn8uTJzkYbbeTYgpyayukgFpqaZhbYDtrDj3CUsf+pxSnctm0Pua5duzpPPvlkhocMvG60Xf6tmkppbMROHI65lHv++edD+4ZXk52uv/76wLK2AKoWUwgso8d1yimnOO+8846jphU7xx9/vEcI1WWwzUeQU1MyPX3jfgpLJ554oqds3759HbBQU1YdePjtuuuunvxcYn/57xW1GIYzfvx45+9//7uDOIV6nA0aNHDgGYb4iKtXrw4zNe/zVS3IQVRDrMURI0Y4hx9+eGB8QFx/JAjBXbp0MSxwX6vFWjxjVQsgeDwt1XRbB+IW/tnXB55p+rzePvHEE6atyl5rW5DT127LLbd04y3CsxH7OI9yQdfRH0fu3HPPNbZxhwRIgARIgARIgATSRABinAr14z7bMBZzmq4MbanpBCjI1fQrWMPtjyMkRQlyWOwAizrohB/0WCRB/0C2t+hLi22YNmjn2fsQSCDA2EnF1XLUyqL2KXdfxZAKbSfqy0oLcioOVuCPdTSOqYK2Xfa+LSygbByOuZTDlFO7P70/aNAgNONJYK5Wss0oj2vz/fffu2XhQaTb8G//8Y9/eNrDwbXXXhtYPh9BTsV887T13nvvZfSHEx9//LEr+Gn7gsYKLzcIQLoM9uMmeF3qelggQ60GaqpCsLHvW3hJFipVtSCnRWh7PFOnTvWwxr0C4RMJ0zs1J2wxPdhOKgahJ//hhx822biXdF14zYWlJK61X5BTcfYcPW0X/aqVm13bwz4HXn31VWMrbA6638Ls53kSIAESIAESIAESqCoCeNZX8YfNc0vYs01V2cN+SKA2EaAgV5uuZg0cSxwhKUqQC5rSB880/aPcv0V8LCR4pPnz7GPYdcQRR7giTRTWfAU5eFv5RTX0g6mdGC9Eh3XWWSfURn/dOBzRftxyEBdsHnof8dfgIef/17lz58DyEB2QwuJ7IY4fvBD9Cau16j7tbT6CnF51U7fz9ddf+7tzj6+55hrTJwQiFaA/sJzt7QeeQd5PQRV79uxp2r/uuusyiiDGobaxdevWGflJnUiDIIex+D3ERo0a5Q4Rb2BtgRceg/rvFgJvcXGx4YRp0HhI1CmuIJfEtfYLcrlOOcWKyfp6Y9ujRw89DG5JgARIgARIgARIIBUE8Jzln1lEQS4Vl4ZG1BICFORqyYWsqcOIIxBFCXKY1udPUZ5l9vQ3fxwq+8ex3ocwgyl3EN6CUr6CnL8t/JjHl529+IC2IWhbaEEurh1Bttnn1Mqm7lARtN4+r/cRiy8oJSnIQdzS/WEbJqCpFT495TBlOOjfoYce6imHKZRxUu/evU09CEL+ZN+3ECptoclftjLHaRHkMAa16qphgummOkHwta/ZSSed5Gb5PVvVyq66iruNK8glca1tQS7sPvYY5ztAHEh7jFEefb6qPCQBEiABEiABEiCBKiGAZzD7eQX7FOSqBD07qSMEijBO9YfFRALVQkB5PIkSHjL6VoKMKIHKPf/UU09J//79M8rghBLkpFWrVp68t956S9SCDJ5z+kAJcrLNNtu4h8obTcaNGycqNlygDboOth06dBAVt01UwH/7tHz77bey+eabe87pA/VlJSq2nT4M3KKMip8lylsmMD/spBLkRAmKJjsORxSOW055JYnyVDLt57ujhCd3fGH9qhVjRcXYy2j+lltuEeUxlXEe7ahpoxnnw07g3lJxAc1YlNehqKmEgcXVlFFRK4QG5kWdfPzxx0Ut1hBVxM3DeDAuJBU/TtQUTVELAbjHuI/QhopN6B7jfldenO5+0v/hb0bFRDTNqgVO5OCDDzbHX331laj4Z+ZYCYWiFkMwx3pHLcAgSqzWh6KEPlELk4iKAygq9p45r6asihIjzbG9o6aHi5q+6p5ad911RU0Nd/dx7ymvS5k/f757XFJSInPmzBHlRSZqqq97DgzV1HJRC6+4x/jvrLPOkn/+85/usRK4RHnWufv+/5K41vhMWLBggdu08qaV//u///N3k/W4adOmsnLlSrec8vwTJRZnrcMCJEACJEACJEAC6SSAn9VvvPGGzJo1y30u+uWXX9znlObNm0ubNm3kL3/5iyjvfmP8woULBb9N/Em9GBf1otJ/WtQsD/d5yJ+B5wm14rz/tDnGs++HH34oKkaxoM8mTZoInpPULBLZaaedBM8gQenUU08VPMv7k4qVK2rmh+c0nrf32msvzzn7QK1sL/PmzXPHi989eLbDbzLwwBZjjkp4RsQzpT8p5w5RcavNbxz08f7777vjVY4V7jM3nqv1M7ddH9cLv0NUOBtRM2PcNlTMcdljjz1cNnZZ7pNAwQhAkGMigeoiUAgPuTfffDPjTY76A3LP2R5yeszqg9hBTC9dJmzbq1cvXcVsozzk/F5sppLagffTRRddFDqFVH0RuNNCw2zxtx2HI/qPW06JjKE84DUY99+UKVPcYWMF2aCxwMsoKCXlIQfOmPKo+27ZsmVQd+45rKqry6kHFTf+F+KXZfv37rvvhrZpZ6iHswz+uKdwrf3X5cYbb7SrJrqfJg85JYgb5kpsdcrKysxYEWdOXw9s1cOm59heZEVXiushl8S1tj3k4EmZT7Kn38IrkokESIAESIAESKDmEcBq8RdccIHjn5VhP8fofTw/YCEzJPUi0fNso8tg5fagdMUVVwSWD3u+VeKUowQ3z2JYug+9xcJimDmgXjJ6usSCXLpMnC2eZYMSZstgIbNsbcBO5VQR1IR7LtuMKfVSPZK/evnsICa4Tgiro15Ah9qFMD0IJcREAoUmwCmrhSbM9iMJ+IUI/WENQUanbB/Aupze5irIoR6EAKyCialn2gb/FiKUHYgf9aIEudtvvx1FAhO+nPzt4xg/yh955BG3jnqrFlgG5QotyNmrVdp24kFDL4wROLCQk/44brpNXH8IVf6UlCCHdjfZZBPDEeJcWMIXtbYLDw6FSDfddFPkQxH6x4IaiKNWqJRNkEOMPc0B22nTpgWaojzrPOXQLhIeiOz6QYs66Aax6qou63/4xN9k+/btTb4uhy2mQGPKpz/FFeSSuNaVFeSw6IM9Jk5Z9V9NHpMACZAACZBA+gngZb/9TGB/t4ft6wWpCinIIeZxVDxqv232i86xY8d6nlH8ZYOO/YIcxMB+/frl1A5+ax133HHOr7/+mnHho34PqtlGnsXCguzDOYh+WKDt1ltvdZRHX1bbhgwZkmEHT5BA0gQoyCVNlO3lRKC6BDnlsu2u5Ik3WnbCh3TYlyM+yPUiBbrOihUrQgUWBKZftGiRWxRCHuJdQZi64YYbnMGDBwd+CUyaNEk37UQJcgiIb6c4HFE+brlLLrkk0D4wwCq08DyLSmrqpXPxxRebIkcddVRoewhmb69gi8UUdtttt8Dy8KLKNXXv3t3TFoSQoOR/+FBTL4OKVfocHsKCHhTUdAYHbyMLKcbB+GyCnF8o0l6O/oFXVpDDw1aLFi0Mi4MOOsjfhQNRO4iVmiqRURYnzj77bFNeTS8PLIOTSVxr++E7Hw85LuoQenmYQQIkQAIkQAI1ggBezNsvfoOeWYLOFVqQW758ufuSP6jvsHNakLvwwgvNs1RY2aDztiAHT7Q4XnFB7eCcmvqacf2jBLmwdoLOhy00F1QW5zSXDIN4ggQSIkBBLiGQbCY/AnEEoqgP4KBFHeJ4yOmg8fgSxQctBDq4JcPrBi7nYR/KsMWfsn0RQ0Sy27v66qtDF2+wBTkVY8xTz24DwoOKr+XaDXvicMylHB4wwtqEHX/9618d2AdBE+IcRDQVY8+ZOHGiY6/QqoU7rCpr2+/fh7v8oEGDHBVbw8F0UX++Ps5HkFNxIzztaZHUfx0hIup+sIXHki0U+svDrR422+7vKINFI/S4/XXg2ajHh7GoWGYuM7DEw1NUCmoXfUP4VHFDYj8wZBPkYLt97TFl159Q5thjj/XwytVDDisJ27zte1/3B29M/3QC8Pv55591Ec92/Pjxnjb90y904SSudWUFOXgO2uPHvcREAiRAAiRAAiRQcwiMGDHC811uf69H7RdakPO/NLVtwe+W/fff30H4DhWH19ivhaeo2UJ2O/59PDvq5H9G9JeNcwwvNjtF/R6M016+ZYLEQdsu7pNAZQlQkKssQdavFAH7h7/9QVnoKatakLP7hC1wlbbP2fuY7vjbb79ljDdXd2wIcmEiHmKtYdodvND8Qp5ti96H5xlSHI65lEPZ8847L5SF7j+KF8poYQrbLl26ZG1Ptxu2zUeQs+OUoV0Ih2EJ8SLsviEUXnfddQ5WUoX4pYLEOvfee69zwAEHmHJaiIJ4dOCBB7ou8Jh+iamb/mQ/5GDaZJwU1a49RRN2Y6XWbCmbIIf69hRjCGAQuXWCaD106FAzfs1Lc/BPWZ0wYYInLsn333/v4Yf6ELf83qq6v3PPPdfT1+jRo3VWxhbXStuDLVYuhmgPr0PE+lOLL7geqqhYmWuN+pUV5PyekvBKZSIBEiABEiABEqgZBNTCUR5By37+wD6ekfGiediwYe5zD2bO6DKFFuTw/KP7sreYpmmHnsHvmsmTJztqsS/zYhcvLRFfG6F87Lr2/qWXXuqWQTn9T4coiXIoQBt4/r3rrrvc57Edd9wxtA+12JfnBWwcQU4tpuf+fkF8ZtvesH3EzMZ0V9QLKxM146Jm3Km0Mu0EKMil/QrVcvviCElRH8CV9ZAL+/ANOq9WlAy8GpjaGFQ+7BwEObQVlp/L+UIKcpi+q1YtqpSdWpADOIhazZo1q1R7+QhyarUrT5/w4gtLf/zxh9OtWzdP+WzXQwtRiLVml0UcPn867LDDTBkIvHhggogWNo0W9aPa9cdYO/HEE/1dZhzHEeT8QYNhq1pNy8EiFNi3x6n3NQe/IKfzEX9QrTAWWBcPb0EJ9489tQB9q1Vgg4q65z7++OOM9lEHsRm1HXoqdWWuNTqrrCDnF7zjLg4SOnhmkAAJkAAJkAAJVBmB008/3Txb6GcMvW3btq2jVu7MsEWtNOrcc889DuL1IoWFyfHH1dUN+Z/PdH/+RR3OOeecUNvw0l+tUq+bdLd46eyfFRIVJ/vpp5/21LcP/C+LtY3YqlVb7aLurJKwMDUo//jjj5vyUb8HUfaWW24xZfEiFs+sdt/+fSyuZ4eJwUwFfxl9HDYzw3TIHRKoBAEKcpWAx6qVJ1BTBDmIAv6pifboR40aFfohrj/M9RaC3OzZsx18eepzQVuwyVamkIIcxofA+hAOwq5TkN04B+Fsv/32Mx5ymhXeooV5B4a1ZZ/PR5DDl60dqyzb1EA8LGG6qt1v2D7i3+nFBbBgg12uQ4cOethmO2fOHE8ZXR58IQSOGTPGmTlzpodbVLsDBgzwtAcPvGwpjiAHISzIC07bi+vgj82XTZDTde0t7m9/PETb/scee8wzPix4kS1F2Y2+tSCHdvK91qhbWUFu5513NmMDB1u8RvtMJEACJEACJEAC6SXQp08f8z1uP9vAMy5IjAsaSaEEuSeffDLQNm0nnjsxawAvMsNSvoKcfwaC7hMvR4MWa3j22WdDbYUnnk5RghxiA/tTlCiJZ0V/wqJ62lb/9u233/YX5zEJJEaAglxiKNlQPgSKi4sDP/ywAIJOYR/U+MILmkKKL0H/B6k+XrhwodssvL8gDuHtFoQTne/fIiAppsHFWfYaH+QdO3YMnPaKqY9w0X7ggQccLASBBJdweFH5V/lp2rSp07dvX3eKHext1apVhn3wNDrzzDOdTz75xG0rDkcUjFvObdT6D0IS+tt0000zbNHMEIcCIg2m3n377bdWbe8u3jKBO6bn6rp6C+HsX//6l7v6kT5nb0tKSryNxTyCkKPbAc9sCW8uIY4FrU613nrruatGQTizE7zctEgDO+17GPcb3sTZnlranqAt4lUgLh9SVLvwOuzatasZG2LSZUtxBDm0AZvxllOPCXbiXsU9+9prrzm2WIa/RSxCguT3kMNbYj9H3EdYucr/NtZtwPoP8U00H/QR9eCoq0FERhwUXCddF1t4ymGqMf7u7JTPtUZ9e+rJaaedZjeZdR8PpLbIjRXFmEiABEiABEiABGoOAUzztJ8z9D5mL8RNhRLk0L//pa22z97i2QqzdiC++VO+glzYYg548RyUEEPZtsneh2ioU5QgFzRjyh8axG4XK+P6E8LS2GXsfTt0i78ej0mgsgSK0IC64ZhIoE4TUIKbqNhWojxmRAkgory4RAWTFxW/IGcuSnCTzz77TJTwIWqanrRr106UOBDajgrWL0pEFBVDS5SLuiixTdQXpCmP82o6m6Bd2IV8JfCZ/KrcwceFEhNcTkpMcbtWq4O6nJRwI0qwiW2OEnxEucyLEkTcOkqgE+WJKMr7KnYbcQuquHGi3mSa4moRD1Fx0sxx2A7Gq77kRYlNojyYXPZR9wTK41rhOtrXXL2lEzXNwNONEl5FPbSJ8uBzOeA620lNERUVj8PlEdYuyquFIkR5/blVlYAmanEEu5mMfYxHPUSa8yrwryix2BwH7ag4Ke7fB5gpsTGoiDmnpnCLEpTNMcaghDVRq4q6fxOdO3cWJbya/LAd/zUbOHCgqKkLYcUzzuN6LVq0SNQDpeDewt+zioeXUU6fyPVa63r5bNU0ZDn88MPdqkqYEyWsyjbbbJNPU6xDAiRAAiRAAiRQDQTUi2jBs6w/qdkKol7++U8HHqtF2gKf25SzgKjF2zLqqLArorzBMs4rT3tRL7w95/HbRsVRFvWS0n2G9WT6DtAfnrtUvDSTo16ui4qxZo7tHTVlVVTcNfuU2VcvQN1nW3Pizx21AJngGTEo2b977Px9991XlBDnnnrqqadELdRmZ5t9PNuqF+7mGDtq8TX3+dNz8s8DJchlPHeBd9izmBLkRE2BDWqK50ig8gTUjxAmEiABEqj1BOzYcJhiXFUJq6Paq1hhWumrr77qCaq7cuVKd+qmP8YavEOzJbjpq28C1/tOe9VF1cGiCiiv/2ElriST30NOB/nNtQ/1EGZshK35tpNrv1VR3l5hNiw2ZVXYwT5IgARIgARIgATyI4A4b/pZyt7CAz5oamZQL2EecphRgZkK/uRf6Er3i9AXYQkzgpRzQKCtuj62iLlmpygPOfUy2C7q2Q+bTRMUygUV/TM3bJsq4yEHrza7LXs/yENu3rx5oeXpIee5xDxImACnrCYMlM2RAAmkkwACw+ovYywuEbWQQpIjwLRi3S+2UXEosGCBXRbxBqOS8vRzp5FiKjKCBMdJeIiy+4CAlmRKQpD78MMPPTZilbLakjDNXnmVuuPDVBFMkWAiARIgARIgARKoWQSU577nWcV+tjr//PNjDQZxbe169r4OS6MbeueddxyERLHL6P0oQQ71Ec4Dq6bipbCu49/imWTJkiW6O3caq7+MPr799ttNOf9O1GIK/rAhqHvjjTeG2oTQKTrlOmWVgpwmx23aCVCQS/sVon0kQAKJEUCMNf0wcf311yfWblRDiK2Ghxzdb5RHmnKXN+VQ/uabb45q2o3Xp6Y+OmpqZmQ5O/P44483fcCuqMVK7Hpx95MQ5PBGVPPCFsGJa0saP368GRuuBRMJkAAJkAAJkEDNIxAlJOHZBd/xejVTeLvhGe+GG25w49litVQkxBu2n3fsfb0iKcQ0iFFRi6L5BTm8sL322msdFQ7FAxZ2hHnloW/M4NAJMa/t51fbNsTR1c+eEPEwDhXWxB2f/Zxj18E+YgPrmMPoBzGqo8ZlzxShIKevDLe1jQAFudp2RTkeEiCBUALwQFMx4NyHHwSd/eOPP0LLJpmx3XbbmQcuLNqBhS/wlhALXMBjSsUcdK655hpHxRw05fAQFGcRg7h2YhVP9GsvJoDpFkmnygpyCxYscFfp1Q9xWLSitiRcb73YBFbyrar7r7bw4zhIgARIgARIIC0EsOBb2AIG+hkGW3zv26FLcO7EE090h/Hcc8+Z5z67jt7HswLENn0ctvULcvfdd59bB2IXFrqCQAd7ly1b5lxwwQWh7UH0slOUWAZbVNxnT1uY2QGhLkzIQx0shqFiwbn/VGxfT317fCr2r2cFegpy9pXhfm0iQEGuNl1NjoUESCArAXio6S/8k08+OWv5JApgVVIIcbrfONtTTjklia5NGyqQbkb/eEuadKqsIIf4fjYfPFTWljRs2DB3bFjxtTbFxKst14fjIAESIAESIIFcCEyZMsXzzGI/v0Tta0EOcYb1i7qo8tnywgQ5ux5eyEYJZXg2wUtiO/Xr1y+n8elQK6effnpO9Ww79f4jjzxim+J6Ceo8/zZolVVOWfXg40GKCVCQS/HFoWkkQAKFIaAXQsDDiVqFqTCd+Fp9+eWXne7du2d9QFErgjpqpVRf7WQO4YWnH2IwtcA/lSGJXiojyGHBCcTD0za2bds2MKhxEnZWdRtq9TLzIHzllVdWdffsjwRIgARIgARIIGECmAKKxZn0c0vcrRbkYI690FPc+v5ycQQ5fx3/cdAiU0Evc/317GMtyGGabZ8+fXLmotvC4hX+RA85PxEe1xYCRRiIuvmZSIAESKDOEFDTN0V5KwmWPVdv/+TSSy+tsrErbzmZOXOmqNWr5Ouvv5bS0lJRUwLcpeWVSCaHHnqoNGrUqGD23HTTTTJr1ixRopCoqQKJ9zN79mwZMWKEaXfq1KnSsWNHcxy1oxbe8FyLk046SYYOHRpVpcbknXPOOaLEStlpp51EvVGvMXbTUBIgARIgARIggXACSnySs88+W1TMNikvLw8vaOUoQc48C+B5UC1eJSqsilUic/eAAw5wn6dU/LmMTCXIiQqLYc6rBRxkyJAh5jjbzrbbbivKc19UWJeMoioWnqiYxhnng04oQU5U7Ds3C8+35513nvu8GVduUIteiYrxHPjs99RTT4ma6hrUrSgPOWnVqpUnT71wFxWzznNOH+D5f5ttttGH7lbF+Ms4pwsobztRi1XoQ25JIFECFOQSxcnGSIAESIAESIAESIAESIAESIAE6hIBFRtY8NJTTbV0BaKgsbdr10723Xdf98WlWvHUFFHxc0WtKCpqEQNRiymY82pVVUG5wYMHy5gxY0TFg5MzzjjD5OsdFYNYvvvuO33oCoN4AYwXnU888YR8/vnnJs/eUTHwXDFx5MiRkS+DH330UcGLxXnz5mF2nd2ErL/++tK3b1857LDDZMCAARkve8EFL2cfeOABUTMhPHX1QZcuXVwBES9h8ZI6KKl4e7LXXntlZKlpuPLrr7+6dtiZahV79yWofU7vL1y4UHAt7KRi32Wc0/nvvfee7LjjjvqQWxJIlAAFuURxsjESIAESIAESIAESIAESIAESIIG6SABecosXL5Yff/xR1IqiAlFtgw02cGdCYBuVVEw5UQswyNKlS11hCkKViu0WVSVWnlrQwRXDINr9/vvvbttq0YRAj7ioBiEWqoXI3LFBBISopWLgRVUxeRDywAOi3A8//CBqoQtBG/gX5JlnKnKHBGo5AQpytfwCc3gkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQALpIkBBLl3Xg9aQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAnUcgIU5Gr5BebwSIAESIAESIAESIAESIAESIAESIAESIAE0kWAgly6rgetIQESIAESIAESIAESIAESIAESIAESIAESqOUEKMjV8gvM4ZEACZAACZAACZAACZAACZAACZAACZAACaSLAAW5dF0PWkMCJEACJEACJEACJEACJEACJEACJEACJFDLCVCQq+UXmMMjgdpE4LTTThMs3Y40ZMgQ2XXXXc3wovJMIe6QAAmQAAmQAAmQAAmQAAmQAAmQQAoIUJBLwUWgCTWLwMqVK6VJkybVbnRa7KhKEPXr15eKigq3y2uvvVZGjx5tuo/KM4Wy7KxYsUKaNm2apRSzSYAESIAESIAESIAESIAESIAESKByBCjIVY4fa9cxAieffLLceuut0rZtW5k+fbpst9121UIgLXZU9eCjRLeovGx2lpWVyaBBg2TmzJnSo0cPmTFjhrRo0SJbNeaTAAmQAAmQAAmQAAmQAAmQAAmQQF4EKMjlhY2V6iKBuXPnSqdOnczQhw8fLrfddps5rqqdtNhRVeO1+4kS3aLy7DaC9iGuHnbYYSbrqquuEkyBZSIBEiABEiABEiABEiABEiABEiCBQhCgIFcIqmyzVhL44YcfpE2bNgJvKqSxY8fK5ZdfXuVjTYsdVT5w1WGU6BaVl83Wl156SfbYYw9T7K677pJjjjnGHHOHBEiABEiABEiABEiABEiABEiABJIkQEEuSZpsq9YTgFAzefJk2WGHHeSKK66QjTbaqFrGnBY7/INfunSpTJgwQa655hp/ViLHUaJbVF6czi+66CKZNm2aK8xNmjRJGjduHKdajSpT6OtTo2DQWBIgARIgARIgARIgARIgARKoRgIU5KoRPrsmgdpEYMmSJbL33nvLzz//LF9++WVBhhYlukXlFcSYGtZoVVyfGoaE5pIACZAACZAACZAACZAACZBAtRGgIFdt6NkxCdQeAr/++qvstdde8uGHH8oWW2xBQS5ll7aqrk/Khk1zSIAESIAESIAESIAESIAESCC1BCjIpfbS0LCkCcyfP1/GjRuX0WzDhg3dVVPbt28vW221ley8885SUlKSUQ4njjrqKCktLXXzRo4cKf369TPlkmjfNJZlJ8wOTEmEXTqdfvrp7kIU1113nbz22mvyzjvvyOabby777LOPHHTQQdKzZ09dNGOLlUanTJki8+bNc73eNttsM+nQoYO7GungwYOladOmbh14xIHD7Nmz3WPw3H///T3t9erVS8444wz33C+//CKwZ9asWTJnzhzBMVat7dy5s/zjH/9wpwN7KlsHUV5wUXlWE4G7K1askGOPPVYcx3Hzzz77bNlxxx1NWT/X888/P3CF3ffff18mTpzo1isqKpI77rjD5ZTvvQEh7YQTTjB24B6Nils4evRo+fHHH93y4HHPPfcIPONyuT66M7DA1OhXXnnFFVq/+OILQf/du3eXfffdVw455BBd1LPFdf7mm2/ccwMHDpSjjz5aFi9e7MZcxCq2TZo0kd133939W0R7TCRAAiRAAiRAAiRAAiRAAiRQJwmoH11MJFAnCLzwwgtQW7L+Ux5ezn333edUVFRkcKlXr56pf+2113ryk2jf02DEQZgdaqqosQ9jVQKXo0RGzznNoEGDBs7jjz8e2Mtxxx0XWEfXXXfddZ0FCxY4aoEJZ9ttt40sizr77bef289zzz3ntG7dOrS8ErGc8847L9AmnAwbd7a80Ab/zFAilsemBx980FPFz1WJlZ58fYB6mhG2aBcp33sD92C3bt08bX7wwQe6O8/25Zdf9pRT04dzvj66we+++85RIp6nPXtc2FeLXjhKyNRVzNa+304++WTn888/dzbZZJOMtpo1a+Y88MADph53SIAESIAESIAESIAESKC2E1i7dq0zaNAgRzlJOKeccooZ7qmnnuqoF/HuP+VIYc7nu4Pfabo9bL/++ut8m6rSevj9g981sPmvf/2r065dO0c5hjh9+vRx8Bs17PfDiSee6DL929/+5qxZs6ZKba5MZ/AIYSKBOkEgriiihYfddtvNWbVqlYdNlCCURPueziIOwuzwC0d6LGFb5QnovP76656e/KJSWF3lOeV06tQpQ2gJKq8FOdvuoHL6XJhQaNf3C6JReZ4BBhykRZDT47fvvYceesjDWHmmBYzAcQYMGOApB4a5Xh80rLzynI033tjTlrbLv8UXnj/ZgtywYcOcrbfeOrStSy+91F+dxyRAAiRAAiRAAiRAAnWAwPLly+vAKDOHqBbAc5+N1awiR80WMgUq81vGNGLtqFBCnmdw/28+q2iqdvEbz/+bw3/cu3dvDzsM4K233nLg3IGyEO5qSqIgV1OuFO2sNAG/YAZhQ02vc0aMGOEcfvjhjpo2mfHHb7+1gAFRH5RJtB93kGF2BAlyW265pXPvvfe63lLffvut+ybG/lA74IADPN0ef/zxHg4XXnih8/HHHztPP/20ywueTagPQe7KK690/+26666mDvL1eb194okn3D50v2raoqOmMzpXX321c/HFFztdu3Y19VGmf//+Hpv0Qdi4kR+Vp+uHbatakMvl3sNboi5duhg+GOfcuXM9Q/n000/NFxD49ejRw/ntt9/MdYh7fdAo3i7p64Rt3759neeff95RU1YdeDjabSH/mWee8dhiC3K6HdyDF1xwgaOm+jrYx3mUW716tacuD0iABEiABEiABEiABGo3ARX+x1Hhc9xndxXWxlHha2r3gK3R4feTCivjPguPHTvWyqncbxlPQ38e1FRB7qqrrvL8FtG/J/zbNm3aOCqskGfoQ4cONXVvvPFGT15aDyjIpfXK0K7ECfgFs6C3BFOnTjUfkvijh8oOMUKnKNEnifZ1P9m2YXb4BTmIHkHuySpOnvmwwvRTOx188MEmDwwwfdFOKmacK6zY5zE1Vn9IYspvWEIZeEx99tlnniJw3W7ZsqVpAx+wQSls3CgblRfUln2uqgW5XO89uGZrvthiuqidhg8f7sl/+OGH7Wx36rKuH3V9ILzqhwSUhzu9P+Faqfh6pj/s28kvyG233XZm6i7K/f777679EHiZSIAESIAESIAESIAE6haBadOmmedIPG9CgKkLCS/LMU0VY8YUzJUrV3qGXZnfMp6G/jyo6YIcZnKpuNMOfucceuihgTN4/L+JVNxqRzuPoP4nn3wShCZV5yjIpepy0JhCEogjmKH/6dOne74kRo0aZcyK+qBMon3TUZadMDv8gtzdd98d2NIVV1zhGaPtqaTdqLWAA0ElLG6ZbjyuIIcpjP43GboNtRiFsQniXFAKGzfKRuUFtWWfS4MgB3vC7r3y8nLP9FPE/8O1Rvr++++d4uJiw65jx44Z8Q/jXp9rrrnGtAMxGl9qQenRRx815cDdvn/8glwSMTCCbOA5EiABEiABEiABEiCBmkfgxRdfNM+R+L2hFhGreYPIw2K10JkZ9w033JDRQmV+y2Q0pk7UVEEO9wdmUSGMjp0wxdn+vYh7Z5111rGLuPuXXXaZ4QzHgaC48BmVqvEEBblqhF/Xu548ebLruQOxIOifP0ZYZXnFFczQjz19FVP0dIr6oEyifd1Ptm2YHX5BTq2yGdgUhDotuGELUUcnBABt3ry5Jx8xDs4880znjz/+0MU827iCj6eSdaBWf3UXftA2bbjhhlbu/3bDxo0SUXn/ayF4Ly2CHKwLu/ew0Ijmg+1JJ53kDuacc87xnFcru2YMMu71QfBUuw8syhD0D2+p7HKYMquTLchtv/32+jS3JEACJEACJEACJEACJOASQEgcLAyH50y/p1gaEC1ZssSz4EJlbZo9e7b5rQIvuaBFByrzWybIvpoqyAWNRZ/DTJ3u3bt7focsWrRIZ7tbOAq0aNHClEHopjQnCnJpvjq13LZsAecRNyvJlItgBvdXLTjYUzqjPiiTaD/ueMPsiCvIIaabHh+22ttK9//mm286rVq18pRBOcT/wqo3/hRX8LHrzZ8/3zn33HOd9u3bZ/Sz3nrr2UXNfti4USAqzzQQspMmQS7s3sMXkL1AAtywsYLp+uuvb/jB/R1xOfwp7vVBvDj7voi7by/CYQtyRxxxhN8UHpMACZAACZAACZAACZBAaglgaulOO+3kRIV5ydV4xOzWz9VYTTUoVea3TFB7tVGQwzjPOusswxJM8Rvcn2xvxKDZQ/7y1XlMQa466dfxvtMsyI0bN878oSOmVllZmXu1oj4ocxHkwtqPe0uE2RFXkJsxY4YZHz7I/IIc7FiwYEFGAH+URd9+1/K4gg/axcISWEjDjlXWtGlTT1yAuizIRd0biHGov8yxBTf7OCwGR9zrs8suu5j2sPAGxME4/959911cWjfZghzeejKRAAmQAAmQAAmQAAmQQE0ggAUmdthhB/d5OClBDs/J9vN6WBzlsN93+XKrrYKcfwFCzO7yJ38YIP8idP7y1XlMQa466dfxvuGmbH84+fcRuyzJlItghlVXtT3wStIp6oMyifZ1P9m2YXYkKcjBBsy5v/322z0LLoBLo0aNHHjR6RRX8Hnvvffcuf6aLTy6ELcMU2Ht+GVpE+SwMIa2GVsEow1KDz74oKccPO+Qkro3IAwHeRTCpg022MBZtmxZkFmxF3WwF/TAtcknUZDLhxrrkAAJkAAJkAAJkEB8AvPmzXMD3SOMiP3vyCOPdM477zwHIUywoueqVatCGz399NNNXR13Gi/OjzrqKAfhYzAz5u9//7v7kj6oEczeeOONN5xbbrnFGT16tIO+zzjjDPfFfVT8acQCO+yww0zf+H0QlPA7BOOADQiDgpA63bp1c4499lg37nJQHf85LHSGMC+77babO6sEz8tdu3Z1FwpArDI7vthPP/3k5ulnfoTsOeSQQzz//vWvf/m7yHqMGSO6zcaNG4dek7Dfd+gAi+qNHz/e2X///Z127dq5v6fAZPDgwaGxvv2CHBYqhP1YKAEzoWALXsafcsopzty5c7OOI1uBJO7JbH1gUUHcm5qn/Tvdrovflvi9qsuBU1oTBbm0Xpk6YBeC+0PRDvsXFvw/XzRxRREEkLTnnR900EGmy6gPyiTaNx1l2QmzI2lBTpsBJvgg0x9q2NrTEc8++2yTt/HGG+tqni0WJrBF2B49ergrbupCaRbksDKoPfYpU6Zosz3bygpyUfee7ggCqW2L3j///PN1kYxtnOuDSlh+XbeHLR5Uck0U5HIlxvIkQAIkQAIkQAIkkBsB/+8O+/nN3oeXF+IQ28KT7sn/zIZQKJtssonnWRBtYdXKBx54QFdztxB7MK3T7su/379/fwcvtf0pW6gYlIfw0q9fv8j2MYtjxYoV/ubdYwhY9otmv236WAts+D1q/07R+f7tfvvtF9hf2Em8TIejgW5nn332CSsaGn7nueeec1q3bm3a0G3pLRZigwjrT35BTpcP2kLsxO+YyqQk7smw/iGwwSEC009t+0888cSwKs7ee+9tykKIhYCcxkRBLo1XhTYVhID/Q+L1118P7AdLK9t/6JMmTTLlwoQwFEiifdNRlp0wO5IQ5DBlEm/U/Alf5PCa0mxsDyq8sdHnscV0V39C4H+7jD/AZpoFOYzdZo4HAH9CGbyxs8eYq4dc1L2n+0OMOLyxtPvB9FI8eISlONcHdT/66CNPu3iIC3ID1/289dZbzqBBgzwr5/of7nRZbkmABEiABEiABEiABJIh4P/dYT8XBu3DQ8zvLWc/sw0bNswTq9jfxqWXXmoMxyqWDRo08Dwz+svrYwg9mD5op2yCHF5Q4wW/biNq+7e//c1u2t1HDLhNN900Vv2JEyc6KJ8tlJK2IVdBDrG3dV1ssXpoWLJ/a9iLG9rn7bb8+3ZMZ/SRiyCn24JHYb4piXvS3zeEOIhp2j57CwEVThNh6corr/TUe+2118KKVut5CnLVip+dVyUB/4fEhAkTPMIRVhq1A27iDx5fVPaXl/2BaH9QYhxJtB+XR5gdSQhyeuUasIAbuo6f9/bbb3tcf9u0aWPMve666zwfeFiSGm7f8IpD3AR4040ZM8ZTxhY60RDijekPWT1l1b8CUdi4UT8qD/lRKduDAerqeBKwEQKYPWUXU0WHDh1q7NfjCBPk8rn3bPuxGIbuA1tME4hKca6PXn4db+7strFoBOpDUIXX6vvvv+9ATLX/VvQ4YYP9cMcYclFXhXkkQAIkQAIkQAIkkB8B/+8OTK3EdFHEaUbonbZt23qe5/Bsh6mJdrKf2fSzH176XnDBBQ5mXugXwCiHlSuR/HGoUQ8L8SGuF2ZaYCqqX0CBh93ChQtN19meu+H1pO3BFouOYbrlF1984cBbbNddd/Xk++OD+WOMIW41Xprfeuutzp133ulgqq72ONOCHMQb/LPbht36vN5iYbxckj1LBWP5z3/+E1o97LeMZoHfHwMHDnRFvYsvvtgzvRZl4JFopyBBrkOHDs7ll1/uPPTQQ+4WK77q9rHFtQ5aIM5uN2w/iXvS3zbEWds+vY/fihBSo5I/jhx+P6UxUZBL41WhTQUh4P+Q0H/Q+ECGuKSP7S0+/O0U9kGJMkm0b/cVtR9mR5KCnOaAhQP8H9bIs12EP/744wx+eHOG2AS6Hf+KOPggRew5xIbAl4sup7ejRo1y3ebRtk5h40Z+VJ6uH7bN9mCAeldccYXHRoyvZ8+eTq9evULfEmqhKsl7A554tks97Pjqq6/Chuaej3N98MWOhDdRiM+hr0OcrR4n6tsPdxTkQISJBEiABEiABEiABJIl4H+2DJr5g8XA7EXUMLXR/m1jP7PheQ/xu+1nOngfYVaIXoQAeXb8LsToglCFF/B2wkt5CHP2M+Rf/vIXUw7t2Hn2VEk8s9o2YyaGP2Hq4Y477mjawL5OiEdn/yZo2bKl8+qrr+pss4XDBZwr/AJZ3LjYpqEsO/vuu6+xE2MOi5eHZmy7bccP1EOstM8++8zTGzhgfJql7SyBgn5BDmKt7WiCMphhg98zug1sw0LzoHxUSuKe9LcfJsjBTsTQwzTrsITrbo8r6F4Kq1uV5ynIVSVt9lWtBPwfEvYfqH8fH25+92oYH/ZBibwk2kc7cVKYHUkIcr179/Z8ePnZ4Bhv3ewvbNgc5CFm14Xgg3h89rk4+7NnzzZIwsaNAlF5poGQnagHA10FQljUGPHwoL0L9bg0oyTvjccee8zDEMt6x0lRtsNeLcihLcTtwHRVPY6oLWIB2otJ2A93FOTiXBmWIQESIAESIAESIIHcCPifLYMEObTo9xLCC2+d7Gc2POtlm9IHkch+JrSfHXWbeovnZni22eUxfRMp6rnbDmEDAXHx4sW6Sc/20UcfNW3jN4D24LvwwgvNefQNUTKXlLQgZ8+wgT1BMfW0fWG/ZTCdOCy2OmYlacb4/WonvyAXdo/gt5bdN8TUfFIS96S/X8QIxG8d/N6wF2nQY4bgGZYg1uly2KKNNCYKcmm8KrSpIAT8HxIQldZZZx3PHyriDQwZMiQ0blZxcbEpf+ONN3rsTKJ9T4MRB2F2YGUk+4Pn/vvvD2wFrt12OdTTacmSJe4bI7if22Wwjw9CrFSkhSZdB1tMbb3qqqs8gUtRBx5cmN6I+GRwLbZXGkI+POXOPPNM15Xd9lSEm/hFF11kvmDRR9i4s+UhPypFPRjY9fAmCl/U9gMMmMC9HQ8xtliGhwgsnY6U5L2B1ZD0dUEftgehbat/P871sevggQHTjP1/I/qaIdDuzJkz7Sruvh2D47TTTsvI5wkSIAESIAESIAESqOkEJk+e7D4T4rkw6J/t4VSIsfqfLcPEFvRtT1/FM6tO9vMsvI2yJfxG0s+giCWdbWojpqnq8thCbEOKeu4+4YQTPHXwcjfoH1aWtdtGaBUk+3cGViPNNSUtyOmpsdpWLRwG2WWLYnHun3feecdBTDvdNrwX7RRXkEOdvfbay7QDEVGnXO7zJO5J3W/QFrN4sOKuHq/eIpxOUILDgC6DLZwN0pgoyKXxqtCmghAI+pDA2xss0YxFDLR4km/nhW4/X7sqUw8ffHhrgrGBU7YvXvQFt3Us6vDiiy86n3zySeDqRxD95syZk/GWCHXhyo0vmJUrV1bG9JzqRj0YhDW0aNEiN8ae3/U7qHxS94Y/MCym+uaa4lwfu038jWBhBwh/uBfC3tDZdbhPAiRAAiRAAiRAArWZgP0C0v7Rr/fxYruQKejZMqw/TDvVdq277rqmmC3IQcjKluzyBx54YLbibr69aiviuCFFPXf7veq03dm2ekEDCIu6LBwCck1JCnJ45ran3+Ild1SKI8jNnz/fQSy09u3bm3Hq8eoY3LqPXAQ5xBfU7cApQqdc7vMk7kndb9S2T58+xlbYfNNNN4UWR9w9PS44dqQxUZBL41WhTQUhkMuHRD4GFLr9fGxinXgEsKCH/rDG1o5lEa+F6FJJ3Rv+OBRRb0OjLWIuCZAACZAACZAACZBAvgRyESry7SOqXi7PluPGjTPPuRCI9IJttsAWJ8yIHT/uuOOOizLP5HXt2tX0DbENKUqQs2eCQEyBmBjnHxaRQ7JDrgwePNg9l8t/SQpyeKmNmUL6N4Z/SqnfrihBDrOZEAPOFvgQ59tejbYygpw9brSrUy73eRL3pO43anvfffcZpmCLRTrCkj27CrHN05goyKXxqtCmghDI5UMiHwMK3X4+NrFOPAJYMUl/WWKLa5lkSuLe8L/lQnBcJhIgARIgARIgARIggaonYC+wZT9D6n0skFDIlMuzJVZd1XZhcQCdchXkdt99d9OOPfVVt+ffItRLSUmJqYPphkhRgtzBBx9symNabK4pbOpl3HZsYSqJKY62hyDEuagUJshh9pAdQgZcMP0XM5nsmHuVEeT2339/w33nnXc2ZuZynydxT5qOI3bsEEG4r0ePHh1YGouS6Pse2ySuZ2BHlTxJQa6SAFm95hDI5UMin1EVuv18bGKdeATs5dERly3paZlJ3Bt2TAx8qTz55JPxBsdSJEACJEACJEACJEACiRLAsyJCeoT9S/pZ0m983GdLrFLZokULI0xggTWdchXkTj31VNNOw4YNnVmzZummArd33nmnKY9n19tuu80tFyXIjR071lMHIXBySbaN8CZDGJxc0tlnn236h/dZZZN/wTeIRGEpSJDDtFdbFMPCBHYbSQhy8L6DV5wWr7RwCjtzuc+TuCfD2NjnbdHUvq/sMtjnog5+IjwmgWomEPdDIl8zC91+vnaxXjgBuJJfcsklnpWF7DeH4TVzy6nsvYGYfLaLOtz/mUiABEiABEiABEiABOomgbjPlsOHDzdCC8SLSZMmGWC5CnIPP/ywp61u3bo5EPyCEhZZgMeWFnmwhUCCFCXIYRE4uw68miB6hqW33nrLGTRokHmZfvfdd3vqQ8zKJU74+PHjPfXxDF6Z1L9/f097iEEdloIEOXC0edx7772e6pUV5BAfHPEA7T6iYrJ5OvcdJHFPYjo1vAqxsB9ijvsT4r77V1v94IMP/MXcY4T2sceF+ySNiR5yabwqtKkgBOJ+SOTbeaHbz9cu1gsngLdu9gc19s8666zwCnnmVPbewBL1tp2IncBEAiRAAiRAAiRAAiRQNwn4ny0nTJjgLqqmaSA+MhY1sJ8fIcDZi5HlKsih7QEDBnjaRFy522+/3e0bbWPRtosvvtgzVRU2jBkzRpsWKcih0D777OPpY/3113euu+46B+IUPLawqiaEKXt8EPmQIOhglVB73IjdduONNzqfffaZu0Ddd99958yYMcPBqrG2xyDqox+77siRI52ffvrJXbQOceowY+WGG25A0VjJjt+HdiEohaUgQe65557z2GMLqmgHsf+0vXrK6po1a9wu/OFuIDZCpMJ44GWH30E9e/Y09dEORFYdYzDMzrDzSdyTEAj1eJo3b+6uIguPuMsvv9zB1GubEcrBfkyNDkp+ARlOGGlMFOTSeFVoU0EI+D8kkg6IX+j2CwKFjXpiL+AL3H5QSQpPZe4NPFDZAUmxdH3YF09S9rIdEiABEiABEiABEiCB9BLwP1tqEaN169ZOmzZtjKihz2P7/PPPewaUjyAHb7VWrVoFtm/3Ze/j+VqLRDAgykMO+YiNBmHIbiPbvhbkUB+CnT2zJKruTjvthComffzxxxn9IvYbFgTQ7UBwjJveeOMNUw/1J06cGFrVFpuuvfZatxymk+p+sYXoBoHqjjvucAYOHOjJQz5e4sPDDOPwC3J2O0H7GGeYt1mo0VZGEvekLcgF2WifwzWZO3euZYF397zzzvPw0Qt/eEtV/xEFueq/BrSgigggzgHm3et/eEuSZCp0+0nayra8BPDWDHHkVqxY4c1I6Kgy9wYCl+p7FtupU6cmZBWbIQESIAESIAESIAESqIkEwsQPW7DQ+/AQmz59esYw8xHk0Ah+Q2FRB91+1BZeaLZYhvrZBDmUgRcbpqtGta3z8Hy8bNkyVDPp8ccfdzbddNOs9f2CHBoYOnRoZL1cBDnEgLNj+EVNmwwS5GAPvPj0WONuZ8+enZMghxf+M2fORHd5pyTuybiCHDwzcY2jEhan0LzwN4BQRWlMFOTSeFVoEwmQAAmQAAmQAAmQAAmQAAmQAAkEEPCLHxBU7JU4IURAkIIgFhaDrVOnTkawOO200wJ6CT8FcWPKlCnu9FB7JVX0iymmffr0cTDdMijFEeRQ7+uvv3anuvrHhT7gKdavX79IEQnTMrECZ/v27TOmOkL8wnRHTFH1J0zZvOqqqzJi4MGDDNNkEecul3T00UcbzvAuDEv2jBg4C+j022+/uVNltbikx3/mmWc6Cxcu9HhENmvWzI2/tnr1amflypXOLbfc4nLC9E+7PvYxno4dO7rhepJwSkjinsSY4dmH6wYRzW/zRhtt5Bx77LGhsQs1M8Q2tAXO4447TmelblsEi9RAmUiABEiABEiABEiABEiABEiABEiABFJOQMX/kr59+xorVSge2WWXXUQtnCBK8JLOnTuL8swy+YXcUV5gbr8qNpl06NBB1LTZyO5QTgkrpsyDDz4oBx98sDn270CuQB21OIMoIVDUlFxZd911/cUij1VIGpk3b56o6bBu30qsFCVeRdZBX2oRBvnmm29EeWTJlltuKU2aNImsE5Sp4saJEihNlpoWKmoarzmOu6Pi54mawuqOffPNNzfVYKcSsVw2Xbp0ETWV0+TZO0qgFOV56N4fShgUtZCdqNVy7SKV2i/EPanESPnqq69k+fLlosRDgd1x0rRp00TFnHOLKmFOVPxB2WabbeJUrfIyFOSqHDk7JAESIAESIAESIAESIAESIAESIIH8CASJH717986vsSqupTz2PKJdNkGuis0rSHcQ4JRnndu2ivMmaiXTgvRTnY2m6Z5UHnGiFhtxcRxyyCGipmxXJ5rIvinIReJhJgmQAAmQAAmQAAmQAAmQAAmQAAmkh0CaxI9cqagVTkUtSGCqqamOsscee5jj2rjzxBNPiIof5w4NnnmLFy8WNRW3Vg01LffkkiWYGZq4AAA8GklEQVRLRMUfFBVXUIqKiuS9996T7t27p5Y1BbnUXhoaRgIkQAIkQAIkQAIkQAIkQAIkQAJeAmkRP7xWxTs64YQTjIcYBBMIKLlOQY3XU7pKqZh3ola6dY26/vrr5aSTTkqXgZW0Ji335IQJE+Siiy5yR6MW7RMVj6+SIytsdQpyheXL1kmABEiABEiABEiABEiABEiABEggMQJpET9yGRBiwV122WUyfvx4N94Z6iKOGWK71YWkFqmQrl27CmK5bbbZZm5cM7XYQq0ZehruScQZRBxDxNuDl9ycOXMk7YwpyNWaPwEOhARIgARIgARIgARIgARIgARIoLYTSIP4kSvjl156KWNq6llnnSUTJ07MtakaW/6hhx4SxDRDOvnkk0Wt8lpjx+I3PA33pFqBVe68805RK8jKyy+/LDUhriIFOf+dxGMSIAESIAESIAESIAESIAESIAESSCmBNIgf+aC59tprZcyYMW5VLHTwxhtvSElJST5N1dg655xzjlxxxRWC1T8x/p49e9bYsdiGV/c9+eqrr7qr2cIT88orr5TTTz/dNi+1+xTkUntpaBgJkICfwGmnnSZr1qxxTw8ZMkR23XVXUyQqzxTiDgmQAAmQAAmQAAmQAAnUcAKzZ8+WESNGmFFMnTpVOnbsaI7TvIMVRmfNmuWKJk2aNEmzqQWxraKiQoYNGyZz584VxJW79NJLC9JPVTda3fckhE4sELLTTjvJlClTqnr4efdHQS5vdKxYVwmsXLlS0vDlkRY7qvI+qF+/vok5gTdso0ePNt1H5ZlC3CEBEiABEiABEiABEiABEiABEiCBFBCgIJeCi0ATag4BzPW/9dZbpW3btjJ9+nTZbrvtqsX4tNhR1YOPEt2i8qraTvZHAiRAAiRAAiRAAiRAAiRAAiRAAlEEKMhF0WEeCVgE4FbcqVMnc2b48OFy2223meOq2kmLHVU1XrufKNEtKs9ug/skQAIkQAIkQAIkQAIkQAIkQAIkUN0EKMhV9xVg/zWGwA8//CBt2rSRsrIy1+axY8fK5ZdfXuX2p8WOKh+46jBKdIvKqw5b2ScJkAAJkAAJkAAJkAAJkAAJkAAJhBGgIBdGhudJIIDAXXfdJZMnTxasCoTVcTbaaKOAUoU/lRY7/CNdunSpTJgwQa655hp/ViLHUaJbVF4inbMREiABEiABEiABEiABEiABEiABEkiIAAW5hECyGRKo6wSWLFkie++9t/z888/y5ZdfFgRHlOgWlVcQY9goCZAACZAACZAACZAACZAACZAACeRJgIJcnuBYjQRI4H8Efv31V9lrr73kww8/lC222IKC3P/QcI8ESIAESIAESIAESIAESIAESIAEMghQkMtAwhO1lcD8+fNl3LhxGcNr2LChu2pq+/btZauttpKdd95ZSkpKMsrhxFFHHSWlpaVu3siRI6Vfv36mXBLtm8ay7ITZgSmjsEun008/3V2I4rrrrpPXXntN3nnnHdl8881ln332kYMOOkh69uypi2ZsZ8yYIVOmTJF58+a5Xm+bbbaZdOjQQQYNGiSDBw+Wpk2bunXgEQcOs2fPdo/Bc//99/e016tXLznjjDPcc7/88ovAnlmzZsmcOXMEx1i1tnPnzvKPf/zDnQ7sqWwdRHnBReVZTXCXBEiABEiABEiABEiABEiABEiABKqdAAW5ar8ENKCqCLz44ovSt2/frN3Bw+uyyy5zRaeioiJP+SjRJ4n2PZ1FHITZ8dVXX8mWW25pakLgeuSRR2TBggXmnN5p0KCBPPzwwzJw4EB9ymxHjBgRuYLsuuuuK++//740a9ZM9txzT/nkk09M3aCd/fbbTyDwPf/883L00UfL999/H1RMwPvcc8+VSy65JDA/bNwoHJUX2BhPkgAJkAAJkAAJkAAJkAAJkAAJkEA1EaAgV03g2W3VE4grmGnLdtttN3n22Wc93nJRok8S7eu+s23D7PALctnagScgRLLevXubog899JAccsgh5jhs55VXXpFRo0bJZ599FlbEnNeCnG23yQzYefzxxwOFQrv+tddeK6NHjza1o/JMIe6QAAmQAAmQAAmQAAmQAAmQAAmQQAoIUJBLwUWgCVVDwC+YQXSCN9wff/zh/sN0zkWLFnmMOeWUUzwrhkaJPkm07+k84iDMjiBBDh5z8DjDtNK1a9fKpEmTBGKWTgcccIDrRaePTzjhBLnpppv0oVx44YVy8MEHy7fffusKlDfffLMsX75cIMi9++67bjl42r3++uvuPrzmUMdOW2+9tQwYMMD1gMP5Jk2auJ518FhEW9OnTzdTXpHfv39/efLJJ7HrSWHjRqGoPE8jPCABEiABEiABEiABEiABEiABEiCB6ibgMJFAHSHwwgsvOOrvzfxTAlLGyKdOneooYceUUVMoHeVBZsrVq1fP5ClRy5zHThLtexqMOAizQ61uauzDWFVMPOfrr7/OaEnFyTPl1PRTT74S30we2vjuu+88+SpmnHP++ed7zqupsaaOEjk95e0DtKfEOUd51dmnHSUUOi1btjRttGnTxpOvD8LGjfyoPF2fWxIgARIgARIgARIgARIgARIgARJIAwFJgxG0gQSqgkAcwQx2KG8tIwxBQFLTMo15UaJPEu2bjrLshNnhF+TuvvvuwJauuOIKzxhXr15tyk2YMMGTt9122zkffPCByQ/aiSvIDRs2zFELTwQ14ajFKEy/EOeCUti4UTYqL6gtniMBEiABEiABEiABEiABEiABEiCB6iLAKatKcWGqHgLXXHONLF68OLRzrAZqxwgLLRgzwz+lFFMs7dhpdjPt2rUz01d33XVXd4VS5EdNi0yifduGqP0wO/xTVu+55x53ZVh/Wzg/dOhQcxqLLGy88cbu8Y8//uiuprps2TKTj5VTx4wZI+PHj5fmzZub83rnrLPOkn/+85/uIaYBK2FQZ8XaYuorprnqaaobbrihwA5/Chs3ykXl+dvhMQmQAAmQAAmQAAmQAAmQAAmQAAlUK4HqUgLZLwl06tTJeESpP4KM/S5duiQKKa4HGzo95phjjD32lM4oL6wk2o874DA7/B5ySngLbPKJJ54w4wN71LPTm2++6bRq1cpTBuVUPDrn5Zdftou6+3E95OyK8+fPd9SKqk779u0z+llvvfXsomY/bNwoEJVnGuAOCZAACZAACZAACZAACZAACZAACaSAAKespuAi1FUT0izIjRs3zohEyvPKKSsrcy9TlOiTiyAX1n7ceyHMjriC3IwZM8z4ggQ52LFgwQJHeQd6yqEs+r7rrrs8puYiyKnFIZwRI0Z4YvU1bdrUUR56pi8Kch68PCABEiABEiABEiABEiABEiABEqhlBCjI1bILWpOGs+222xoBBkKP/x9ilyWZchHMDj/8cGMPFiHQKUwIQ34S7et+sm3D7EhSkIMNFRUVzu233+5ZcAHXqVGjRg686HSKK8i99957zjrrrGPYbrbZZo6auuyolW7drb4HKMhpstySAAmQAAmQAAmQAAmQAAmQAAnURgKMIacUAKbqIfD777+LWkwgtPOSkhJR00VD83PNiBvj7bfffhMlwsmvv/7qdnHQQQfJQw895O5HxSlLov24YwqzI24MOcRqGzBggOlOCXmC2G9hCUxOPvlk+b//+z9T5IgjjjDHY8eOlYkTJ7p5iEWHmHT+pMQ96dq1q3zyySduVo8ePeTZZ58VJdC5x2rVWjdOHQ6UICdLlixxz9v/hY0bZaLy7Da4TwIkQAIkQAIkQAIkQAIkQAIkQALVTqA2qowcEwkEEYjrwTZ8+HDjwaX+QJ1JkyaZ5sI801AgifZNR1l2wuxIwkMO02lfeeWVDAvgLQePNjDBP+zrpBZ7MOeRh+mu/vTpp596ytx7772eIvCU021Xl4ccVpvFOMNSVH5UXlh7PE8CJEACJEACJEACJEACJEACJFA3CXDKat287nVy1H7BbMKECR7hSHl1OQcccIARhSAObbXVVs6qVasMrzAhDAWSaN90lGUnzI4kBLnu3bu7DMDijTfeMPHz3n77bXeqqhbN2rRpY6y87rrrPNxGjhzp/PTTT055ebmjVlB1lDedo1Zp9ZSxhU40pDzwTL4W5NasWWP6wE7YuLPleRoJOCgtLXUOPPBAd3yYooxraaeo/Kg8uw3ukwAJkAAJkAAJkAAJkAAJkAAJkIAmQEFOk+C21hPwC2ZaWGrdurUDcUkf29vnn3/ewyVKEEqifU9nEQdhdiQpyGkOWHBh8803z+Bz4oknGgs//vjjjPwGDRo4jRs3NufPOusss4+2Iboh9twdd9zhDBw40JOH/FGjRjmbbLKJg7Z1Chs38qPydP2w7bRp0zz9YzELO0XlR+XZbXCfBEiABEiABEiABEiABEiABEiABDQBCnKaBLe1nkCYYKaFJ3vbsmVLZ/r06RlMokSfJNrP6DDkRJgdSQhyvXv39ohTNhe937ZtW+fHH3/0WDd06NDIehdffLGj4vFFltHt29vZs2ebfsLGjQJReaaBkJ2bbrrJY1eHDh08JaPyo/I8jfCABEiABEiABEiABEiABEiABEiABP4kQEGOt0KdIeAXzCAq2St+QgTadNNNnSFDhjg//PBDIJfi4mIj3Nx4442eMkm072kw4iDMjm+//dbYh/Hcf//9ga0888wznnKop5NaTMFRCyw4Xbp08ZRBe1hd9aSTTsoQ41C3rKzMueqqq1zPN1tQg6ccpr9+9NFHjlocwp2+aufDU+7MM890Fi5c6PFUbNasmXPRRRc5iM2mU9i4kR+Vp+uHbdUCI+70ZNilFhNxvfbsslH5UXl2G9wnARIgARIgARIgARIgARIgARIgAU2Aq6yqX+BMdYNA0Cqou+yyi3z++eeivL2kc+fO0qJFi7xhFLr9vA2rRMVly5aJ8rqTX375RZRYKUrElIYNG0a2iNVUFy1aJN98841suOGGsuWWW0qTJk08dZYuXSpKBHRX0VXTYU0e6n744YeCrRIERU15NXmF3lEfiqLi3bkr7CqRMKO7qPyovIyGeIIESIAESIAESIAESIAESIAESKDOE6AgV+dvgboDIEgwU9MzEwNQ6PYTM5QNkQAJkAAJkAAJkAAJkAAJkAAJkAAJVCsBCnLVip+dVyWBQgtmhW6/KlmxLxIgARIgARIgARIgARIgARIgARIggcIRoCBXOLZsOWUECi2YFbr9lOGkOSRAAiRAAiRAAiRAAiRAAiRAAiRAAnkSoCCXJzhWq3kECi2YFbr9mkecFpMACZAACZAACZAACZAACZAACZAACQQRoCAXRIXnaiWBQgtmhW6/Vl4UDooESIAESIAESIAESIAESIAESIAE6iABCnJ18KLX1SEXWjArdPt19bpx3CRAAiRAAiRAAiRAAiRAAiRAAiRQ2whQkKttV5TjCSUwe/ZsGTFihMmfOnWqdOzY0RxXdqfQ7VfWPtYnARIgARIgARIgARIgARIgARIgARJIBwEKcum4DrSCBEiABEiABEiABEiABEiABEiABEiABEigjhCgIFdHLjSHSQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkA4CFOTScR1oBQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQB0hQEGujlxoDpMESIAESIAESIAESIAESIAESIAESIAESCAdBCjIpeM60AoSIAESIAESIAESIAESIAESIAESIAESIIE6QoCCXB250BwmCZAACZAACZAACZAACZAACZAACZAACZBAOghQkEvHdaAVJEACJEACJEACJEACJEACJEACJEACJEACdYQABbk6cqE5TBIgARIgARIgARIgARIgARIgARIgARIggXQQoCCXjutAK0iABEiABEiABEiABEiABEiABEiABEiABOoIAQpydeRCc5gkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQALpIEBBLh3XgVaQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAnUEQIU5OrIheYwSYAESIAESIAESIAESIAESIAESIAESIAE0kGAglw6rgOtIAESIAESIAESIAESIAESIAESIAESIAESqCMEKMjVkQvNYZIACZAACZAACZAACZAACZAACZAACZAACaSDAAW5dFwHWkECJEACJEACJEACJEACJEACJEACJEACJFBHCFCQqyMXmsMkARIgARIgARIgARIgARIgARIgARIgARJIBwEKcum4DrSCBEiABEiABEiABEiABEiABEiABEiABEigjhCgIFdHLjSHSQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkA4CFOTScR1oBQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQB0hQEGujlxoDpMESIAESIAESIAESIAESIAESIAESIAESCAdBCjIpeM60AoSIAESIAESIAESIAESIAESIAESIAESIIE6QoCCXB250BwmCZAACZAACZAACZAACZAACZAACZAACZBAOghQkEvHdaAVJEACJEACJEACJEACJEACJEACJEACJEACdYRAjRfkVr9xrZR/81Zil6vx3peK1G8kq54dZ9os7nGCNNi8hznmjpdAxR+LycuLpE4eOaUrZeWTY0Qq1rrjb9TlEGm4zX6GRbZ8U5A7HgLZuGXL9zRWwIOVT54qzurf3R4abPkXKd7x2AL2xqZrBAGnQlY8MlLEKXfNbdT1SGnYYa8aYTqNJAESIAESIAESIAESIIFCE6jxghx+BK5NUJBrMnCKFClBbsWjIwz7xn3OkYad9jfHsXcqyqX857lS1LSV1Gu2YexqlSpYDX2W/zAnGV6VGjgrVzcBZ+Vvsuzu/saM4m5DpbjXieY4W74pGLVTDfd3lDlVkZeNW7b8qrARfSy79a9Kdyl1u2vYbg9pvPflVdU1+0kpgYplP8jy+w4w1pX0Gi2Nug0xx9whARIgARIgARIgARIggbpMgIKc7+onJsiVl8nyBwZLxR/fihTVk8Z7nC8Nt97X11vCh9XRpxoCBbmEr2MNbS6bMJQtP+uwq+n+zmpXgQtk45Ytv8DmmeYpyBkU3PmTwNpv35WVM0YbHk3+NlEatO1jjrlDAiRAAiRAAiRAAiRAAnWZQI0X5NZ8cJeUf/dB6DUsX/y+OH9Ol0Gh+i22lqLG64WWL/nLWeKsWlJpj6+1i16Rlc+cZfppsPH20uSAm81xIXaqo0+Mg4JcIa5mzWszmzCULT/biKvr/s5mV6Hzs3HLll9o+3T7FOQ0CW41gdJPH5HVr0zUh9LssPuk3gbtzTF3SIAESIAESIAESIAESKAuE6jxgly2i4f4NeU/zjbFmh32b/WDoJ05DtpJQmCq+HWBLJ9+lGm+4db9pXHf8ea4EDvV0SfGkQSvQvBgm1VLIJswlC0/m7XVdX9ns6vQ+dm4ZcsvtH26fQpymgS3mgBivJbO/vefh0WyznEviTQo1tnckgAJkAAJkAAJkAAJkECdJkBBLuDyJyUwlc76t5R+9oTyymsnxT1PlHrrbBrQW7KnqqPPpHglS4KtVTWBbMJQtvw49lbH/R3HLpQp//FjWfXiRdLsiGlxq8Qql41btvxYnSRQKA2C3LI7+olTusIdTfFOw6V4p+MyRhanTEYlnsiLwKqnz5KyL19x69ZruqE0O/rxvNphJRIgARIgARIgARIgARKojQQoyAVcVQpMAVAiTpFXBJw6lJVNGMqWX5NRlf8wW60we6pS5Uql+cjXEh1KNm7Z8hM1JqKx6hbkKpZ9pxYQOMhYWLLLKdJo+yPNMXbilPFU4EGlCCy/X8VRXbrIbaPBJt2lyaAbKtUeK5MACZAACZAACZAACZBAbSJAQS7gakYKTE6FWqhhsRQVrytFJesE1M79lLNyiThrlkpRk1aq3Wa5N5BnjaT6rWpeeQ63VldzSleKs+JHKWq2sRQ1bJzoWCH4OKXL1GrBG6m2S0LbziYMZcsPbbiyGfpvtmQ99ffVPHZrcf8+yr/7UFY+dbo4ZavU+i0NYgty1cm10veLWu3WXbBG0ay3zmbqv/rJr7LqOErM+VKc1b+r4J+NlKdxB7VtGHr91i56WcXtPNvkN+l/tTRos4s5xk6cMp4KOIAdSxZJ+ZIvXS/n+oiBVr9BRrGoE+CtGpKiRk2jihU0L64N8DCsUGOtWK4+T0rWVWPeRODdhmscJ1Us/0kqflvoeiquVh6jeuXdRp0GSUmfczObSIAvGo07vkwDeIYESIAESIAESIAESIAEqocABbkA7kECU1HzTaT0o3ul/KdP1IP/clWrSOpvuK002KK3FHcbmvEDDT9qVjz4d/UrYa3bQ6NuR0mjbQ82vTmrlgoWpCib+4T6If/fKVbILFKiQYPNe6myB0n9jbua8nF2svWJNgrRbxK87PFVLPte1n7xopSrH3UVv32h3FrK3bhD+CFcr8VW0nCrvTPE0IqlX/3XQ+nPhuq37qZi9k2wmzX7+FG++o3J7nG95psqr43rTZ69U/79LFn1wgXmVIO2u0tJ7zHm2L9T1TY4K36R1e/coGL4fSwVv3+jzHHUvyKpt14babDJjlLc66S8BQCsjlj64VT3h3WFWuTkv0m13WxDabTd4dKoy6EZ4kg2wS1b/p+dhG6i7u//sj9N1QUDkeIeowTXds37t7sxDt2/MbXaMf5mG7b9q/KcUvEdi4rcsvZ/uf59rFWLxqx66gz1Z77aNFOveWuzj536G3aWxntd6p6rTq6Vvl/K18oadU+sVVMQ8XfpVJS5YyqqX6z+Ljuoz8aP3WP817DdHtJ478vNcS47zpplsvq1f8nab976rxj3Z+WiovrScJv+UrzrGUYYtv9GcX84a/4wXRUpEa+oSQspatBYXZ9V7vnQMk1aStMDbzV1seOs+l3ZMUnZ8fafn/n/zUa7jXY8Vop3OCbjHlpx/xFKgFrjFmy0/RCp33JrdQ/eIeXq7wmLCxXvPEqKdxz234Zi/F/+02dSNv8pKf/5M3FW/hJZo+SvY9V3R0/Jx4ayeU+qfp52BdCKFT9n9AP2uIcbtNs9I09wX3x0j5QrTuW/LfCwsgsX9zxJMTvanMqHLyrnMz7TKXdIgARIgARIgARIgARIIEUEKMgFXAy/wFRvvbZK8PhK/UKrCCgt0qjjQCnZ/TxPXpT4gDf5Kx4ebqbyeCpaB036T1YeHr2sM9G7UX2iZqH6TYKXOzIlvJV+PE3WvHOzR+Dwj7qo8QZSstuZ0rB93/9lrV0jy+4dZP2AL5JmRz4YGLdv5WMnyNrvPzR1m+5/s9Rvvb051jurZp4jZUoY1KnkL//wiKr6vNlWoQ1rv3xNVr10iRrvUtO9fweeS433vswVBfx5ocfqGqx++0b5//bOBN6J6t7jJ+u9N7kXKkVRcUHEBbcWtW5oEVAUrFZAUNGnQnF53Z5V63uvtvq01qpdtO2rraKgqCyCggtaFXGrK3azVqtIfaJWBaniTXKTm+39/2dyTmYmk8kkueGG+DufDzczZ845c853ZsJnfvkvvX+5k5oY4pZT28DWI0X0q7+1BGiveP+RpV33/El6OBay2w75ut6vtOE2fsk9SBY9+R6y7MsZgrh97PDeJ4n2Iy6yVFf7fLC41vPQRVqAsQxm2uHMztGp8/qVa733S5aS1CQfu5wElzWmlZXfrFWQY5fSnhUXiCxZxpUrga2Gi8jkm0lsjojev90tkk//pFxTWc9WkSzyuRV7fDO28EpQ/DO2hi5XQrseRSLVlfqw/f4MkYCfeZfEPLKcVKVt1FkUT/Q8tVv+k6zGWPzsXT3HkiW8fAchIhOulj/imJ+xSnPgHz+ST14t5+k2Nh/rGH+FCO02wdKMOfXwfbHxDUu9007H0T+m7+yx8lAtfLljnzJ2miTqQAAEQAAEQAAEQAAEQGAzEoAg5wDb/nJvbaKsaqxiRfth54vwfqfopvYXB7P4kHrxJrKOm6vbltuITPy5tMArd9xe73ZObtuo8/YFL5FNi8T93xKZD/5sX1bZ/fDIyeQCVXRRS73wW3qJvVW3Z0uu9tFsNVUs/CIYu+u0YgVthUYcLTqO+qGlLtf9gYgtoHhUBRHWF+6kgOT3V3QH3Rxz4Gyj8eXn0Hyt96BlAYUdP1kIRU9dWnHeqm+erGO6bz9e7bp+tu0/U1qiqUaV7r9Kx9U45T7d+rvfg84j2mOMVfN8sKVg/J5Z2h3P+QxGLQtykUk/6zeuBpva75d8fKOILT65rOWT09prEeTYRTi+ZIYoWmSSAVooQpaO21Mdiav0T5W2/UnYOug8yuC5UCSf+5VRbfnBhL6nCxaQLEwr91r1PBsdim1kfLPjDUvZXGyDiC8mSzeT5bI/OpjmsYMUnvJpdj81SuTYn4rgsMPlDscRNJ5LdbT006sgl37tXtHzpEcLQ7L85BKdeiv55CY9zyHzjyekBbDZulMORH/4e4P8r4XZWi46Za609lRteI7Jp39KSNOqSn6yNZ1v4A7kQr/eIkZGp90u3Y5r5cuD9yVjy6SxAwIgAAIgAAIgAAIgAAL9QACCnAN0p5d7X9sAsqi5mCzWDpMvesmnr5WuRKo7uwmasyu6iQeJ+78pMu+9pLqS+990Edr9WPnrf2bds4YbK73k9LUg16jz9gWv5HP/S1ZZd2gmvMEv0sHtR9FGmF7GPzJcx0yugdym4+irtKWcfNFbMFlbRflCUUNEI0saVZJPXSt6X71H7cpPjvvVedq9FCONXkILJfX8r6UbltoP7zeD3FW/rXbLfjZ8Dod+U8TvniWyH/1dz4Gt1doOPFv4Bw4lS853RYos3Nh1TBU+xhknvRSzIOfzh0SAArHzP0EiBFsL5jat08OwUNJ5WpGl2z3PnSod1wOX2XDr73gPkvt3O1ngBYYexGcnQfq3Ir3mYT26/Zmt5vkIbL0njfU7OVbvq8stXFjoMxfmFBiytxbkNitXEqnqvV96HvkeXftVpiX5pKVoaPg4+cxkN7wusv/8I2WUvpfaGCJxLYJc8pnrRO9fF+vzBLbei9zJfy3FZI5nliABNJfYKI+zm2zn6fcJX8dAuZ9PxUT3vKN037Yv/pt02dYVtOGlDbdPkuVp798f0F1l7LPDvytdtNmiLLFstp6HtH6cNl+2Tb/xIAlcV+h+asP/uZ1JiBpBLr2v0o82p5LL93R1yPGT7/PYoulFAZSEMX6Gg0MPFGlytzdbr7JrOjNSxesc2LpPiqwmy0F/5xD6AeMisrLbl7h+jlxt54nU6hvV0KLrrEd0qIDsR2/I62G2QA18bphoO/xCEaRwARzrL/X8DfQdarDhQbpmrZJWjbXy5TG8rq8SYx4LBQRAAARAAARAAARAAAT6mwAEOYcrYH+555hQkeOup9hcOxdbU9wcfqExuzSZX1jcxIP4srNF9sO/6rHMVhZcyUGx068sEaG9J0vrEN2wwobbOblro85bLy+OAxW/l924lMWXT0gLmANIRDIFT3dyr+KYe53TF1KsqK0knZ7HLrOILu2jL9AvwPxCHrvjeIvVhuxEf9q+dA7Fdppl7LLrKVmJFeNRkfsrCU/22GCqr/2zkXNg1j2PXapPGdhmHxE9kV6aTQHX82mykrmLshuSeMCFkzzwvekWEF8NqAQ5tgjqmHCNFJL0MeZ320STRYxPDDj3WW2JVOn+q3Rcnafcp1v/knuQLHwiX/mV8A8aXhyOxKnYnZNlsHpVaX5ma30+zNe7XFKH/uKaXvNIXfdL9v0/F55NRcxHPxSQVdjOo1WF/qwnyypbpMYXTtP3Ft9/0anz6bkepMe3i+nR428gsZXEYip2y6mO8f9D7pXH6r6e27D16RKOc2Z8F0nXbHKPNT9fySevIfFxmTE2iWUDZj9pCFAOls/hkV8VHNtNWetZJlRmxy462V3lE/d9XWRIAC1MQHR97XF6xtvlrpOVp9Mceh69RKTXPqZnENz5cNEx9lItuPEBFhd5Llz4B6mumfQdwoWsmeN3z7SI/hwvr50sFs3fMeZzsMVd5xkryMKwdr58aq/r47YoIAACIAACIAACIAACINDsBCDIOVwh+8s9v1CF9zqxpKXdqost5NjqhoubeJAkVyTDmsQYkoOEs/uVdHktuB8ZR6r763ZOHqlR562XV/L3P6PYcUv0YsN7TaGX2Iv1vmVDvgyeRS+Da3V1B8XvC1EcPy5shcKuhKr4B+woOk+9S74Q9/71LpF85ufqkOXTH92aRLfl8sXb7i4W2mWM6DjmGkt7t51GzsFuRcRuamytZS+9f7uH3Mmu1dWdpyy2Csr6iHWDg95zv/ZDvkXWT4OtB2kv8eCFIrPuGV0/4Oyn9Ut4pfuv0nE9aJkNt/72e9AeQF4Nab/XOk9dQpaFO8rDtT4fngS5fuJa7/3CiWySzxuunAxJijtj/lvhtHzWI8jZv0vbKSFJmKzczMUuLnbQPEIkeHFhK8XkU1fr5so9Uld4bJN89pfkBrtAd+MfYULDxuh93si8/XvKuPoPXdd58iLh32qY6Fl5qUi/WRCt6GhgyL5GnEWTWK47uWywC65hBUdfR/QMsiUgu4+qYo+b1zl9gRaevcwhs+55eo7PV8ORJTJ9R55Ma7ZlsDUL1GytGJ06V/bhZERsbapKcKfR0iVb7avP+NIzyZL3dbkb3PYLIkI/HNTDlwfysj51fnyCAAiAAAiAAAiAAAiAQLMTgCDncIXsL/fmFz9z895XlspsgKrOHGPHTTzgjJDxxTNKgnUHBu8h3WLZva2W4nZOHq9R562Xl7S2oCyCXHzBdmlJ4QtH5b7TH8Oi7lx9iDN+tpOrlCrmF0muk66/Ox1qWDR+sk41kwkfzBaOKuh4nGLMmQW/6Am/IbdNcp2tojRqDokV58vsk2oqnDWUqKld/ZmjoPhGNmCjKjLxZ45WTbpDhQ222sysXSmD6CtmLCR3zSIrm8KLfKX7r9LxClNwFbnt96CToMPjp/50O7n0Fl38zM9src+HF0Gu3NoazbXe+8VsJcVrcBN26xHkEg9eQEIvWVtSYZdeGa+R3CbNha9PbNHJuqqNLGjbvnS23DcLrRzDrGv2E/q+VB28tElQkg4W3KopnNyEszpbvsdoDdHpd+ofaNR4PAeO81au+Ol5zr63WluvsRtp5+nsClws6bWrRM+j39MVnact09a7nubwzPXkGrxI97dbaKsD3bdSGIVC0pjQbsdQUofL5aH4snPIwvtluc0JMzqnL7K4++v+c4/S30GhPY4jC7wfiHr48rhe1qfOj08QAAEQAAEQAAEQAAEQaHYCEOQcrpD95b6cIGe3pIqeOEfG3+EhK4kPmbefpV/7Lyl1n+R4QaPOJPdJq7umwzRLqiqdkzs04rx18dpmL9F985HaVc1siVGyQFVB7sLdt3AfI4NmcLtRIvLV36ij8mWW3aVUCe54iGCXqsQD31JVFOdolAiTi6qljuIxybr7v6HbBQbtRi/Wt+t9rxvsDtaIOcTunEKuqP/0Og3dzuy6qysrbVDGVXZZS1M8rQwFrKe7uqSHdNcLtsn6SvdfpeMlg9sq3Prb78FyglyJJZXpmeXT1fJ8VC3IbUau9d4vnA06u/5v8kpId1x2zyxj8VWPIBdbOF3H4QsM2U9EJ99ku/pk/Uqx6uJ3n6nrzfd04r5vkBvnH+QxzordecpC3U5teGkTWzCNwhC8o7p4+lTWeN3zjtbZXO0ZWHkgzuLbPXec65jBHQ6WmVJTL83R7aJT5lEyhZF63yyysngpxcfCNak0Bx5EJs8h0Y+LfyDFPmULYlspibenxE+6d7tvGaczC3MW145jipaJaph8zybRfdsxapeE03Pp/7SZlCindr48mJf16ZNiAwRAAARAAARAAARAAASanAAEOYcLZH+5b4Qgx6flwNhJitNjDsCvpmO2SFB1lT7dBAtz374+b128yN3y0zlfpukZYo/MdnjCDebpOm5LEa9gaWIPzi/opZEzpHIgeKP4yFJlR7IQLFrHdRz9I0oGMV7EF50ismRNpgqPZWl35PfJHfYr6rD3zwbNIXbniSTIfaDnEdpjkt522wjvcXxVVn6cgTH5wg1aJOGx+eXfnlFxSxPk3ER0xa/a56MaQW5zc633fjGLWDIWIcUrK1e654wpCjXDx1IMwh+Xa2qtZ5HnZupbENhDIyZQ1uMrrG1oL/PWUyLxcNGV3Wz1abHmouean2978dTmxtHacjlMbvDs+lypsJUYu3p33zpBN3VKpML3VXzpGbqN00Z4r6mU4OcYS6ZU/vGgnazTONMzC3WcWVYVs/CXT35acQ7cLzb/OJ2Ugr8DnVhl179Grv8z1WlEx7hLaV6TKAbcmkKMPeOQEtp0w8KG3W2/46grKZv1UaK7Rr4sAntdn30u2AcBEAABEAABEAABEACBZiUAQc7hytQlMFGGOi5exTEWjzizIL9o5dM9ltm0H3a+EVfOUlt+x/M5eYg+PG+9vMzWMb6OQaLrzAfLL5KnTskKODi/KsEdDqIA/r9Uu/LTHvvKfFDGZaKsqvySZ4+1Zm7HCSO6OH5TMGyu9rzdiDlYXGHJmpJdRlko6ctid8VmDhzfMDxyssyEm/pz0WKwFQU5ybKK58OrINcfXOu9X+yJFLrOfFhnNrXfc7VayOUp0ydbPqlSLoak2eWU2yr32XziY9E9f6Lqrq2xdAVteGnD7btvGU/fw3HZlcWx9i9/V25X+lMiQI27XApr5n65j9+i2G0XmKtKtjmZTWj34wpJE9aUHDdXsMs4u1z7KYMrFy9zsItaMhnDoUXLYTV+es2jlAzkB2qXEsfcJC337N/1nFE4/IUZup3asMf7U7Eua+XL43pZnzo/PkEABEAABEAABEAABEBgSyAAQc7hKtlfOhplIWc+dS62gZIu/MgaH+zzu4votPnmZq7bVQlyhZH64rz18jJn4+NplUuioRaffOJK0UtulKpYMqQWKvklP8aZUh3iNbUdOFvwPy7sRha74wQd66jQXX607T+Tkm2ca66qarsRc7DH9CpnoWKZaJ6sD32lceYsbQo7LFzEFkzW3PwDdpAv4yrbZer5X4vPhCBX4OHl+ehZdTm59j5U6OEzRNJwxIK3v7jWe7/0vryYAvFfp9fi9iNBrYIcD262eJWZTafO0+fkDf6xQj7PvTFZL91n2VqP4hdm3l1tcT2PHHOtCO7CVrfF4qUNt7YkInAQ+osjWrdKBKjJt1gyFFtbV97j76X4PWdZrHXNvViE7xh7mQgOP1JXe5mD3RWVrd7Y+s1eUn+YK1Kri27DXWc8JDNZlwh6ZD3cTlbE9lLSf+ZKytTaWTNfHt/L+uzzwD4IgAAIgAAIgAAIgAAINDMBCHIOV6degYmHdBPH2A2Mg4CHv3i61bopkxKfkutWsfjEgLOfKglOXjxu3XI7J7ds1Hnr5cUZVmWw88JyfKGI6Dj2JyI49ADrAmnPbmXEDcolXUg+/VOygFtqGYNf5DmbqjmDaNIW5Jw7GO2WUbutLf2r3enrOXCMs8RDRSsbnmfHhKtFcNjhJVPLp5PS+jJNAdwjFHieM0ZWKpm3nia3wKJVkFm85L6tLMjV+nwkn7yKsiaTJWWhlATJp5iHnJk28fB/qiZSEFaiMFc2imu994vhZnkmzTAv587umZzEwD9oV7lv/lOPIMdJboqZS0nUnPkICThdevjUizeK1B/n6X2zFZ09e7KynNONacNLG27f8+j3KQblSqMrWaBKy67Buxv7tr/pN1fKmKGceKFEgHKxJLQNU7LLYhz/OMNxKK3FJwJb7SLFxtDIEymRw7aWw17n0H3bJJHv+Zfsy26wnDTCnESHXeIT952nXeP5+7jra6v0uVgYzcU3GP05ecW022WWWd2ANnoev0KkXzcsnaWl8Vm/k4dr5cudva5Pngh/QAAEQAAEQAAEQAAEQGALIABBzuEi1Ssw8ZBu4piK4eOPfF66AgaGfoncwAZR8Pz7peuqmpIM2H02BVGnF0Mvxe2c3L9R562bF7kHxpfNpqDtr1mWGdrtWBHc8WCyzBgs8pTIgC0kVOB21TA0fBwJUlepXcsnx4IzsjIaYgIfdIqZlNv0rogtnEZHTe1MWQUtg1a50+dzYFb3zKL4g69bZsLB1YM7HyZ87YModt4HIvfx/1HMrcd1rKjoSfNFoIywYB6IkziwVZUq5liGbPGXeODbluvUNfNRutc3yoy10vVwfjGmXdsXzxBth3xdDeX6TOhGLhtu97f9HiyX1MEthlytz0fqhd9Q9tbb9MzZwosTs/hIMMn+848U82sRCVjDLdavm41rH9wvdsGRv5f4x4TAtvsZohm5TnIxx0cLVRNDjvqa3X55LJnY5uB/580SEZ7PH51xt/B3biOP28XMCAvUw4+kBAsxEoXup+3xIk2iv9my06kNj2e3wuIEEZFjr7aI2SxSpihbauaDv8jszpzl2WyJyCJX16yCqCdnWN2f1Oo5JD7dIjuxW2porynyezAwZB+LSGkf1escEiu+Q/fic7q7/A7lDKpsbbjuOcEWyDl6plUJ2Cy1zWIbt+G4mx1jvid/ZFJ9EsvPlXx4PzBkX0rSYSSpqJUvj+N1fdwWBQRAAARAAARAAARAAAS2BAIQ5Byukv3lvq9dVtWLv8OpLVXBYWPoZfAaS53bjptgwf0add6+4MXCFb/QO7mYlluzv2s7ET3pdukKVa5N4sELpXWSOl7Omo5jO2XWPauaydhMAcoA2xelr+eQj2+koO+zZSw9r/PzKsjZ4zTx+Jz51j9oF5F953nLizofYyudXPeHIjLpOin4dbeAIMfrciv25zLzzosiseLbbl1EaJexIk0CqblsLq713i/5nk9EbPEpFFT/E/P0XberFeRy/1orYnedTmMWRfEAWeHlU59qayx1wvC+p4j20eerXYpruEAkn/ul3ucfMPxdQ0U+vl4mmWg/giw+yfq4Upvw3lPp9HkSvCmz7IZX9Xi+QJsIkLUuu4lmN/ydsrC+p49FjvuFFMvMsfoCg/ek76VbdZtqN8wWbPwdF6EkN/xZqXidQ/bDV+QPIObxfO0Dae05nSXWfMz+Iwa7rcaXzKDr8pG5GX1PjJTCJVvEpV9/QIcBCO0+kdxiLzPa1siXO3tdn2VS2AEBEAABEAABEAABEACBJiYAQc7h4vSFwOQmjpmTGDicXlbxS010yi3S8qhcG3u92zm5baPO2xe8eH4sBiWfuMox6ywfN5fgTqNF+xEXVXxRNceO4hf86PQ7zcPobbYMSTz4Hbkf2GYfYn+zPlbvRiPmkPvkbZmJMZ/cVHF6AbLO6pj0C7Io8uB+Sy/l8eXnieyHL1cc19wgMun6LV+QWzjdklXWvD617fhcksiQWPEfFMvsRdWs5JNdLLMb3+w3rvXeL5yxOPn4lSLz3uqStTlVVCvI8Rj2BBJO40pX1cMvlAlZ1PEcceXYb/l8VlVZPjsowULg87tWbMPZTbmw6JZYcX5FAVImVTh1qbTUi902UeR6Ppb9zZlPZUWVf+JLzqB75Q1TL3JVHTRCBHY4kMS/Q0Vw+1GOYQyqmYOTK706Ia8rn+1VuxZrRVWZee8l0fPwf2nRTdU7fdozztbCl8etZn1O80AdCIAACIAACIAACIAACDQbgZYX5PjFKkOWPUbxic4Z9AI1YKjrdeAX5/gSttYwCr/QqZc1Vcef6TceJjeawi//tB+ddge9+BkZ79iKIEbWQvlcRnYxB9/nAO+9ZEGQfm25xdpCNhQ+we5snEzAi1WE0cf463ZObtGo8/YFL70OireV+ssdIkPuqblN6zQ/Ps6xjDh2VXi/GeR6OlZ3qbSh4lO1H3GxCO89xbk5iSpSsPz0HdEx/od0DY52bldjbSPmwFaFvcSK78N8NkUz89E/w8LIYDVCtFEGxOAuY+gQH/NW8hQfqucJlWDEGJOtg0IjJojQnsfLTJFsucSF3QdD+04XbQfMkpl7y93z3LbS/clt3Ipbf/s92H74RSK8z0klw7k+s/U8l2SBlfrTfMGZdY1roU7tE5wFuO2AmfJ7pz+51n2/0DOSXvMQiXIvidzGteQW/ZZFuFEr5s/QHpQsYGxpsgBzm5JtGr/35YUi9cINluee27EbKN9jThk9+Xjvq8tF6tnrSyxsfcF2EZn4c7Jw299TGx6Li0zm8dilIvP+n4wK01+fL0DPwVdE+ICvGW6zNO/uWydo67K2UWeJtoPPM/WobpMF/J6HLizLlkXhtoPOo6zHJ/ADaAxewxz4Xk29dHORGY0V3PYL5Gb+TZFYfo4WODuOvEQ+9/ZV8PPIcf3Sryyl65W2H9b7Tv9/VsWXR6phfXoC2AABEAABEAABEAABEACBJiXQ8oJck3LX0+K4W7nu96VrFseU46yWHMun0aW/zlvVukicy216mwKQbxK+gUPp5XdIVd1V49ymd2SA8iDFvBLBNlVd8slWRLnYehHcji1QgiXH66lo6BzY1YxiPkl332xaxtzztQ+oZ7qyL7sq5tNxKeb5O8llTol6FJeMrVzoLVwKpD5bRtG6T9wEA9T8fBCb3KfvSjdLPwknvq7tSUyKWFbU71wbdL9YFlnHDgs9bCHGlm+CkpZwrDr5Q4cSn8qMza65LKDlY++TG/sA4SeX88BWwy3WdF7amIdny8DchtdFlr4bOMlEYKthlMBgOMVqrP/5Mp/HvM3fFRyXMP2PYiIF83G1LWOzkTtrXf9f8LO8cY0Uy9lFnzOhVls4eUy++z36rqZ/FOtT0I9Qvs5t6Qcl+kffGzJDs/rusA3eH3xtU8AuCIAACIAACIAACIAACPQbAQhy/YYeJwYBEAABEACBAgESx5LP/UqkKTO0sqzmI5wd2T9wR5Elyzmr9aUQ5ZKXgCkIgAAIgAAIgAAIgAAIgEDzE4Ag1/zXCDMEARAAARBocQLpN1eKnpXf16sMDj1Quo8Gtt5T1kkX0ZfmyKyzyjWdY8pFjrtO98EGCIAACIAACIAACIAACIDAlkMAgtyWc60wUxAAARAAgRYl0PPoJSK99jG5Oo4B2TWLth1cPc1JHzjmW+TEG1uUCJYFAiAAAiAAAiAAAiAAAq1NAIJca19frA4EQAAEQGALIJB68SZKkjBXzzS063hKQnOS8A/eQwpzufWvifTbv5eJL5SFnMw6++WLdR9sgAAIgAAIgAAIgAAIgAAIbDkEIMhtOdcKMwUBEAABEGhRArlN74r4stmUYOETTyvkzLPRafMpecL2ntqjEQiAAAiAAAiAAAiAAAiAQHMRgCDXXNcDswEBEAABEPiMEuCMp8lVP5RZZt0QBAaNEO3jLhWBwbu7NcMxEAABEAABEAABEAABEACBJiYAQa6JLw6mBgIgAAIg8NkjkF3/KsWTWyXysfdFLrZeiExK+Dq3Ef7O7URw58NEcKdDP3tQsGIQAAEQAAEQAAEQAAEQaDECEORa7IJiOSAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAs1NAIJcc18fzA4EQAAEQAAEQAAEQAAEQAAEQAAEQAAEQKDFCECQa7ELiuWAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAg0NwEIcs19fTA7EAABEAABEAABEAABEAABEAABEAABEACBFiMAQa7FLiiWAwIgAAIgAAIgAAIgAAIgAAIgAAIgAAIg0NwEIMg19/XB7EAABEAABEAABEAABEAABEAABEAABEAABFqMAAS5FrugWA4IgAAIgAAIgAAIgAAIgAAIgAAIgAAIgEBzE4Ag19zXB7MDARAAARAAARAAARAAARAAARAAARAAARBoMQIQ5FrsgmI5IAACIAACIAACIAACIAACIAACIAACIAACzU0AglxzXx/MDgRAAARAAARAAARAAARAAARAAARAAARAoMUIQJBrsQuK5YAACIAACIAACIAACIAACIAACIAACIAACDQ3AQhyzX19MDsQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAIEWIwBBrsUuKJYDAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiDQ3AQgyDX39cHsQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEWowABLkWu6BYDgiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAQHMTgCDX3NcHswMBEAABEAABEAABEAABEAABEAABEAABEGgxAhDkWuyCYjkgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAALNTQCCXHNfH8wOBEAABEAABEAABEAABEAABEAABEAABECgxQhAkGuxC4rlgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAINDcBCHLNfX0wOxAAARAAARAAARAAARAAARAAARAAARAAgRYjAEGuxS4olgMCIAACIAACIAACIAACIAACIAACIAACINDcBCDINff1wexAAARAAARAAARAAARAAARAAARAAARAAARajAAEuRa7oFgOCIAACIAACIAACIAACIAACIAACIAACIBAcxOAINfc1wezAwEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQaDECEORa7IJiOSAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAs1NAIJcc18fzA4EQAAEQAAEQAAEQAAEQAAEQAAEQAAEQKDFCPw/acX/isfTKBUAAAAASUVORK5CYII="
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Modules.png\" width=\"800\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives\n",
+    "\n",
+    "- Explain the syntax of a function header:\n",
+    "    - def, ( ), :, tabbing, return\n",
+    "- Write a function with:\n",
+    "    - correct header and indentation\n",
+    "    - a return value (fruitful function) or without (void function)\n",
+    "    - parameters that have default values\n",
+    "- Write a function knowing the difference in outcomes of print and return statements\n",
+    "- Explain how positional, keyword, and default arguments are copied into parameters\n",
+    "- Make function calls using positional, keyword, and default arguments and determine the result.\n",
+    "- Trace function invocations to determine control flow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Function syntax\n",
+    "\n",
+    "- Let's compare math function definition to Python function definition\n",
+    "    1. Square of a number:\n",
+    "        - Math: $f(x) = x^{2}$\n",
+    "        - Python: \n",
+    "        ```\n",
+    "        def f(x):\n",
+    "            return x ** 2\n",
+    "        ```\n",
+    "        \n",
+    "- Python function defintion syntax:\n",
+    "    - start a function definition with `def` (short for definition), always followed by a pair of parenthesis `( )`\n",
+    "    - inside the parenthesis specify **parameters** separated by `,`\n",
+    "    - use a colon (`:`) instead of an equal sign (“=”)\n",
+    "    - type the `return` keyword before the expression associated with the function\n",
+    "    - indent (tab space) before the statement(s)\n",
+    "    - it is common to have longer names for functions and arguments\n",
+    "    - it is also common to have more than one line of code (all indented)\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "- Let's compare math function definition to Python function definition\n",
+    "    2. Radius of a circle\n",
+    "        - Math: $g(r) = \\pi r^{2}$\n",
+    "        - Python (literal equivalent): \n",
+    "        ```\n",
+    "        def g(r):\n",
+    "            return 3.14 * r ** 2\n",
+    "        ```\n",
+    "        - Python (better version 1):\n",
+    "        ```\n",
+    "        def get_area(radius):\n",
+    "           return 3.14 * radius ** 2\n",
+    "        ```\n",
+    "        - Python (better version 2):\n",
+    "        ```\n",
+    "        def get_area(diameter):\n",
+    "           radius = diameter / 2\n",
+    "           return 3.14 * radius ** 2\n",
+    "        ```\n",
+    "       "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 1: Cube of a number\n",
+    "- Input: number (to be cubed)\n",
+    "- Output: cubed number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Let's define the cube function\n",
+    "def cube(side):\n",
+    "    return side ** 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "125\n",
+      "512\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's call cube function to compute cube of 5\n",
+    "print(cube(5))\n",
+    "# TODO: discuss what is different about the below line of code\n",
+    "print(cube(cube(2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "37\n",
+      "37\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: compute cube of 4 + cube of -3\n",
+    "# version 1\n",
+    "print(cube(4) + cube(-3))\n",
+    "\n",
+    "# version 2\n",
+    "cube_of_4 = cube(4)\n",
+    "cube_of_minus_3 = cube(-3)\n",
+    "print(cube_of_4 + cube_of_minus_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1728\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: compute cube of 4 * cube of -3\n",
+    "# Now which one of the above two versions is better?\n",
+    "\n",
+    "print(cube_of_4 * cube_of_minus_3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Whenever you think you are going to reuse a function call's output, save it in a variable\n",
+    "\n",
+    "Rookie programmer mistake: calling the same function with the same arguments will always give the same return value. Why is this a problem? Running the same function call twice takes twice the time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`return` vs `print`\n",
+    "- `return` enables us to send output from a function to the calling place\n",
+    "    - default `return` value is `None`\n",
+    "    - that means, when you don't have a `return` statement, `None` will be returned\n",
+    "- `print` function simply displays / prints something\n",
+    "    - it cannot enable you to produce output from a function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Change the return to a print function call and run this cell\n",
+    "def cube_no_return(side):\n",
+    "    print(side ** 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "125\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cube_no_return(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for ** or pow(): 'NoneType' and 'int'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mcube_no_return\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcube_no_return\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36mcube_no_return\u001b[0;34m(side)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcube_no_return\u001b[39m(side):\n\u001b[0;32m----> 3\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[43mside\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m)\n",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for ** or pow(): 'NoneType' and 'int'"
+     ]
+    }
+   ],
+   "source": [
+    "print(cube_no_return(cube_no_return(2))) \n",
+    "# TODO: discuss the root cause of this TypeError\n",
+    "# TypeError: cannot pass None as argument to the outer cube_no_return function call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "64\n",
+      "-27\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for +: 'NoneType' and 'NoneType'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [12]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mcube_no_return\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcube_no_return\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m)\n",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'NoneType' and 'NoneType'"
+     ]
+    }
+   ],
+   "source": [
+    "print(cube_no_return(4) + cube_no_return(-3)) \n",
+    "# TODO: discuss the root cause of this TypeError\n",
+    "# TypeError: cannot use + between None values"
+   ]
+  },
+  {
+   "attachments": {
+    "return_print.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## fruitful function versus void function\n",
+    "- fruitful function: returns something\n",
+    "    - ex: cube\n",
+    "- void function: doesn't return anything\n",
+    "    - ex: cube_no_return\n",
+    "    - may produce output with `print` function calls\n",
+    "    - may change values of certain variables\n",
+    "    \n",
+    "<div>\n",
+    "<img src=\"attachment:return_print.png\" width=\"800\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tracing function invocations\n",
+    "- PythonTutor is a great tool to learn control flow\n",
+    "- Let's use PythonTutor to trace cube function invocation\n",
+    "- TODO: Copy-paste cube function defintion into PythonTutor (course website > tools > PythonTutor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2: is_between(lower, num, upper)\n",
+    "- Purpose: check whether number is within the range of lower and upper (inclusive)\n",
+    "- Input: lower bound, number, upper bound\n",
+    "- Output: boolean value (`True` or `False`)\n",
+    "- Keyword: `pass`:\n",
+    "    - placeholder statement\n",
+    "    - you cannot run a cell with an empty function definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "False\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "def is_between(lower, num, upper):\n",
+    "    #pass # TODO: remove this and try to run this cell\n",
+    "    # version 1\n",
+    "    return lower <= num <= upper\n",
+    "    # version 2\n",
+    "    #return lower <= num and num <= upper\n",
+    "    \n",
+    "# you can call a function in the same cell that you defined it\n",
+    "print(is_between(3, 7, 21))\n",
+    "print(is_between(2, 14, 5))\n",
+    "print(is_between(100, cube(5), 200))"
+   ]
+  },
+  {
+   "attachments": {
+    "argument_types.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Types of arguments\n",
+    "<div>\n",
+    "<img src=\"attachment:argument_types.png\" width=\"800\"/>\n",
+    "</div>\n",
+    "\n",
+    "- positional: order of arguments must match exactly with order of parameters\n",
+    "- keyword: order of arguments doesn't matter\n",
+    "- default: included as part of the function definition line\n",
+    "\n",
+    "Python fills arguments in this order: positional, keyword, default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = 100\n",
+      "y = 10\n",
+      "z = 5\n",
+      "x = 100\n",
+      "y = 10\n",
+      "z = 5\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "115"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def add3(x, y = 100, z = 100): \n",
+    "    \"\"\"adds three numbers\"\"\"       #documentation string\n",
+    "    print (\"x = \" + str(x))\n",
+    "    print (\"y = \" + str(y))\n",
+    "    print (\"z = \" + str(z))\n",
+    "    return x + y + z\n",
+    "\n",
+    "sum = add3(100, 10, 5) \n",
+    "# TODO: 1. sum is a bad variable, discuss: why. What would be a better variable name?\n",
+    "# TODO: 2. what type of arguments are 100, 10, and 5? Positional\n",
+    "\n",
+    "total = add3(100, 10, 5)\n",
+    "total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = 1\n",
+      "y = 5\n",
+      "z = 2\n",
+      "8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(add3(x = 1, z = 2, y = 5)) #TODO: what type of arguments are these? Keyword"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = 5\n",
+      "y = 6\n",
+      "z = 100\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "111"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add3(5, 6) # TODO: what type of argument gets filled for the parameter z? Default value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Positional arguments need to be specified before keyword arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "positional argument follows keyword argument (1597961864.py, line 2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Input \u001b[0;32mIn [17]\u001b[0;36m\u001b[0m\n\u001b[0;31m    add3(z = 5, 2, 7)\u001b[0m\n\u001b[0m                    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Incorrect function call\n",
+    "add3(z = 5, 2, 7) \n",
+    "# TODO: what category of error is this? Syntax error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, parameters with default values should be defined after parameters without default values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "non-default argument follows default argument (424418737.py, line 2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Input \u001b[0;32mIn [18]\u001b[0;36m\u001b[0m\n\u001b[0;31m    def bad_add3_v1(x = 10, y, z):\u001b[0m\n\u001b[0m                               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m non-default argument follows default argument\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Incorrect function definition\n",
+    "def bad_add3_v1(x = 10, y, z): \n",
+    "    \"\"\"adds three numbers\"\"\"              #documentation string\n",
+    "    return x + y + z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python expects exactly same number of arguments as parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "add3() got multiple values for argument 'x'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [19]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Incorrect function call\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43madd3\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mTypeError\u001b[0m: add3() got multiple values for argument 'x'"
+     ]
+    }
+   ],
+   "source": [
+    "# Incorrect function call\n",
+    "add3(5, 3, 10, x = 4)\n",
+    "# TODO: what category of error is this? Runtime error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "add3() missing 1 required positional argument: 'x'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [20]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# TODO: will this function call work?\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43madd3\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mTypeError\u001b[0m: add3() missing 1 required positional argument: 'x'"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: will this function call work?\n",
+    "add3(y = 5, z = 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "add3() missing 1 required positional argument: 'x'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [21]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# TODO: will this function call work?\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43madd3\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mTypeError\u001b[0m: add3() missing 1 required positional argument: 'x'"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: will this function call work?\n",
+    "add3()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 3: Generate a height x width grid\n",
+    "- Input: width, height, grid symbol, title of the grid\n",
+    "- Output: string containing title, a newline, and the grid\n",
+    "- Pseudocode steps:\n",
+    "    1. Generate a single row of symb (width dimension). What string operator do you need?\n",
+    "    2. Capture single row into a variable\n",
+    "    3. Add newline to single row variable.\n",
+    "    4. Generate multiple rows (height dimension). What string operator do you need?\n",
+    "    5. Generate the output string to be returned by adding title with a newline with the output from step 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: how many parameters have default values in the below function? 2\n",
+    "def get_grid(width, height, symb = '#', title = 'My Grid:'):\n",
+    "    row = symb * width\n",
+    "    grid = (row + '\\n') * height\n",
+    "    return title + '\\n' + grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My Grid:\n",
+      "##########\n",
+      "##########\n",
+      "##########\n",
+      "##########\n",
+      "##########\n",
+      "##########\n",
+      "##########\n",
+      "##########\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: generate various sized grids, by exploring\n",
+    "# three types of arguments\n",
+    "# Here is one example\n",
+    "print(get_grid(10, 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: use PythonTutor to trace get_grid function call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My Grid:\n",
+      "@@@@@\n",
+      "@@@@@\n",
+      "@@@@@\n",
+      "@@@@@\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: make your 2nd grid\n",
+    "print(get_grid(5, 4, symb = \"@\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Some Grid:\n",
+      "..\n",
+      "..\n",
+      "..\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: make your 3rd grid\n",
+    "print(get_grid(2, 3, \".\", \"Some Grid:\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you use keyword arguments, the order of the arguments need not match with the parameters.\n",
+    "This is because we tie the arguments to the parameters, by explicitly saying parameter = argument"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Some other grid:\n",
+      "^^^^\n",
+      "^^^^\n",
+      "^^^^\n",
+      "^^^^\n",
+      "^^^^\n",
+      "^^^^\n",
+      "^^^^\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: Try using all keyword arguments and use different order than the order of the parameters.\n",
+    "print(get_grid(symb = \"^\", title = \"Some other grid:\", width = 4, height = 7))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Revisiting `print` function\n",
+    "- Let's look at `help(print)` to learn about print's parameters\n",
+    "    - Default value for `sep` is space, that is: \" \"\n",
+    "    - Default value for `end` is newline, that is: \"\\n\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello world\n"
+     ]
+    }
+   ],
+   "source": [
+    "# sep doesn't work if you have a single argument\n",
+    "print(\"hello\" + \" world\", sep = \"---\") # `+` concatenates and produces a single string as argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7 True 34....\n",
+      "7\tTrue\t34\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: predict output, then run to validate your prediction\n",
+    "print(3 + 4, 3 < 4, \"3\" + \"4\", end = \"....\\n\" )     # sep default is \" \"\n",
+    "print(3 + 4, 3 < 4, \"3\" + \"4\", sep = \"\\t\" )         # end default is \"\\n\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## void function (one more example)\n",
+    "- fruitful function: returns something\n",
+    "    - ex: add3\n",
+    "- void function: doesn't return anything\n",
+    "    - ex: bad_add3_v2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example of void function\n",
+    "def bad_add3_v2(x, y, z):\n",
+    "    \"\"\"prints x + y + z, instead of returning\"\"\"\n",
+    "    print(x + y + z)\n",
+    "\n",
+    "print(bad_add3_v2(4, 2, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for ** or pow(): 'NoneType' and 'int'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [32]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mbad_add3_v2\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m)\n",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for ** or pow(): 'NoneType' and 'int'"
+     ]
+    }
+   ],
+   "source": [
+    "print(bad_add3_v2(4, 2, 1) ** 2) # Cannot apply mathematical operator to None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `return` statement is final\n",
+    "- exactly *one* `return` statement gets executed for a function call\n",
+    "- immediately after encountering `return`, function execution terminates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def bad_add3_v3(x, y, z): \n",
+    "    return x\n",
+    "    return x + y + z      # will never execute\n",
+    "\n",
+    "bad_add3_v3(50, 60, 70)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Default return type from a function is None. \n",
+    "None is a special type in Python (similar to null in Java)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Trace this example\n",
+    "- manually\n",
+    "- then use PythonTutor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A1\n",
+      "B1\n",
+      "C\n",
+      "B2\n",
+      "A2\n"
+     ]
+    }
+   ],
+   "source": [
+    "def func_c():\n",
+    "    print(\"C\")\n",
+    "\n",
+    "def func_b():\n",
+    "    print(\"B1\")\n",
+    "    func_c()\n",
+    "    print(\"B2\")\n",
+    "\n",
+    "def func_a():\n",
+    "    print(\"A1\")\n",
+    "    func_b()\n",
+    "    print(\"A2\")\n",
+    "\n",
+    "func_a()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/f22/meena_lec_notes/lec-10/.ipynb_checkpoints/lec_10_Iteration_1-checkpoint.ipynb b/f22/meena_lec_notes/lec-10/.ipynb_checkpoints/lec_10_Iteration_1-checkpoint.ipynb
index f145c506d3cc260c280cca71e62996b6e970d069..9d8dab9725c14a6f2ebf2bede3a882708008a89a 100644
--- a/f22/meena_lec_notes/lec-10/.ipynb_checkpoints/lec_10_Iteration_1-checkpoint.ipynb
+++ b/f22/meena_lec_notes/lec-10/.ipynb_checkpoints/lec_10_Iteration_1-checkpoint.ipynb
@@ -1,11 +1,17 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "dd1c3a1b",
    "metadata": {},
    "source": [
-    "# Iteration 1"
+    "# Iteration 1\n",
+    "\n",
+    "## Readings:\n",
+    "\n",
+    "- Chapter 7 of Think Python\n",
+    "- Chapter 6.1 to 6.3 of Python for Everybody"
    ]
   },
   {
@@ -135,17 +141,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "How many seconds? 10\n",
-      "10 seconds left\n",
-      "9 seconds left\n",
-      "8 seconds left\n",
-      "7 seconds left\n",
-      "6 seconds left\n",
+      "How many seconds? 5\n",
       "5 seconds left\n",
       "4 seconds left\n",
       "3 seconds left\n",
       "2 seconds left\n",
-      "1 seconds left\n"
+      "1 seconds left\n",
+      "BEEP BEEP BEEP BEEP BEEP BEEP BEEP BEEP BEEP BEEP \n"
      ]
     }
    ],
@@ -157,19 +159,24 @@
     "remaining = start\n",
     "while remaining >= 1:    # TODO: iterate from start to 1\n",
     "    print(remaining, \"seconds left\")\n",
-    "    # TODO: update loop control variable's value to make progress towards terminating the loop, \n",
-    "    # that is turning loop condition to False\n",
+    "    # TODO: update loop control variable's value to make progress towards terminating \n",
+    "    # the loop, that is turning loop condition to False\n",
     "    remaining -= 1\n",
     "    # TODO: now run the cell to see the output. Didn't it go too fast?\n",
     "    # TODO: call time module sleep function, by passing 1 as argument\n",
     "    time.sleep(1)\n",
     "\n",
+    "# TODO: print \"BEEP BEEP BEEP ...\" (10 BEEPS) without typing BEEP 10 times\n",
+    "# What string operator can you use here?\n",
+    "print(\"BEEP \" * 10)\n",
+    "\n",
+    "# wake up call\n",
     "beeper.beep(10) # Only works on MAC laptops, sorry Windows users :("
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fa91a67f",
+   "id": "062eb9a7",
    "metadata": {},
    "source": [
     "## `for` loop\n",
@@ -187,7 +194,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "f81bc886",
+   "id": "92053fd6",
    "metadata": {},
    "outputs": [
     {
@@ -210,7 +217,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "95e21183",
+   "id": "25df6abb",
    "metadata": {},
    "outputs": [
     {
@@ -233,7 +240,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "5b96ced3",
+   "id": "ed75ffbc",
    "metadata": {},
    "outputs": [
     {
@@ -319,6 +326,7 @@
     "\n",
     "# Let's try the values from -5 to 5\n",
     "x = -5\n",
+    "\n",
     "# Goal: after the loop, best_x and best_y should contain just that\n",
     "best_x = x\n",
     "best_y = f(x)  # at any time, this is the BEST SO FAR\n",
@@ -383,7 +391,7 @@
     "# delta_x = 0.001\n",
     "\n",
     "while current_x <= end_x:\n",
-    "    y = f(current_x)                 # use f(x) defined previously\n",
+    "    y = f(current_x)                 # TODO: use f(x) defined previously\n",
     "    rect_area = delta_x * y\n",
     "    total_area += rect_area\n",
     "    current_x += delta_x\n",
@@ -453,7 +461,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "5b449f66",
+   "id": "56d538aa",
    "metadata": {},
    "outputs": [
     {
@@ -516,13 +524,23 @@
    "source": [
     "print(\"Prime numbers:\")\n",
     "number = 2\n",
-    "while number <= 50:       # TODO: comment out this while loop and write equivalent for loop using range\n",
+    "# TODO: comment out this while loop and write equivalent for loop using range\n",
+    "#while number <= 50:      \n",
+    "for number in range(2, 51):\n",
     "    if is_prime(number):\n",
     "        print(number, \"is prime\")\n",
     "    else:\n",
     "        print(number, \"is not prime\")\n",
     "    number += 1"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa37a65c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -541,7 +559,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/f22/meena_lec_notes/lec-10/.ipynb_checkpoints/lec_10_Iteration_1_template-checkpoint.ipynb b/f22/meena_lec_notes/lec-10/.ipynb_checkpoints/lec_10_Iteration_1_template-checkpoint.ipynb
index 25e3ce581b81173b2c06a458f4299428c90655c5..e64eca32afb7dd374c7077c46a611a69a71c784e 100644
--- a/f22/meena_lec_notes/lec-10/.ipynb_checkpoints/lec_10_Iteration_1_template-checkpoint.ipynb
+++ b/f22/meena_lec_notes/lec-10/.ipynb_checkpoints/lec_10_Iteration_1_template-checkpoint.ipynb
@@ -5,7 +5,12 @@
    "id": "dd1c3a1b",
    "metadata": {},
    "source": [
-    "# Iteration 1"
+    "# Iteration 1\n",
+    "\n",
+    "## Readings:\n",
+    "\n",
+    "- Chapter 7 of Think Python\n",
+    "- Chapter 6.1 to 6.3 of Python for Everybody"
    ]
   },
   {
@@ -116,12 +121,16 @@
     "remaining = ???\n",
     "while ???:     # TODO: iterate from start to 1\n",
     "    print(remaining, \"seconds left\")\n",
-    "    # TODO: update loop control variable's value to make progress towards terminating the loop, \n",
-    "    # that is turning loop condition to False\n",
+    "    # TODO: update loop control variable's value to make progress towards terminating \n",
+    "    # the loop, that is turning loop condition to False\n",
     "    remaining -= ???\n",
     "    # TODO: now run the cell to see the output. Didn't it go too fast?\n",
     "    # TODO: call time module sleep function, by passing 1 as argument\n",
     "\n",
+    "# TODO: print \"BEEP BEEP BEEP ...\" (10 BEEPS) without typing BEEP 10 times\n",
+    "# What string operator can you use here?\n",
+    "\n",
+    "\n",
     "# wake up call"
    ]
   },
@@ -338,7 +347,8 @@
    "source": [
     "print(\"Prime numbers:\")\n",
     "number = 2\n",
-    "while number <= 50:       # TODO: comment out this while loop and write equivalent for loop using range\n",
+    "# TODO: comment out this while loop and write equivalent for loop using range\n",
+    "while number <= 50:       \n",
     "    if is_prime(number):\n",
     "        print(number, \"is prime\")\n",
     "    else:\n",
@@ -363,7 +373,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/f22/meena_lec_notes/lec-10/__pycache__/beeper.cpython-39.pyc b/f22/meena_lec_notes/lec-10/__pycache__/beeper.cpython-39.pyc
deleted file mode 100644
index b9f001427a20444d72a1913259600da8a6182a56..0000000000000000000000000000000000000000
Binary files a/f22/meena_lec_notes/lec-10/__pycache__/beeper.cpython-39.pyc and /dev/null differ
diff --git a/f22/meena_lec_notes/lec-12/.ipynb_checkpoints/lec_12_Iteration_Practice_template-checkpoint.ipynb b/f22/meena_lec_notes/lec-12/.ipynb_checkpoints/lec_12_Iteration_Practice_template-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..50400f6ccb611c50312add7bfec0f1813e9b50ef
--- /dev/null
+++ b/f22/meena_lec_notes/lec-12/.ipynb_checkpoints/lec_12_Iteration_Practice_template-checkpoint.ipynb
@@ -0,0 +1,326 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6a76ef95",
+   "metadata": {},
+   "source": [
+    "# Iteration Practice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "103da70b",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives\n",
+    "\n",
+    "- Iterate through a dataset using for idx in range(project.count())\n",
+    "- Compute the frequency of data that meets a certain criteria\n",
+    "- Find the maximum or minimum value of a numeric column in a dataset\n",
+    "    - Handle missing numeric values when computing a maximum / minimum\n",
+    "    - Use the index of a maximum or minimum to access other information about that data item\n",
+    "- Use break and continue in for loops when processing a dataset\n",
+    "- Trace the output of a nested loop algorithm that prints out a game grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28961628",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9341253",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: inspect the functions inside project module\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1dca7ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: inspect the project module's documentation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fb78f6b",
+   "metadata": {},
+   "source": [
+    "### How many students does the dataset have?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d67a080f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c97d494",
+   "metadata": {},
+   "source": [
+    "### What is the age of the student at index 10?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bde8dc35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "id_10_age = project.???\n",
+    "id_10_age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0f87a2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: inspect return value type of get_age function\n",
+    "print(type(id_10_age))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37898141",
+   "metadata": {},
+   "source": [
+    "### What is the lecture number of the student at index 20?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba993090",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60730da8",
+   "metadata": {},
+   "source": [
+    "### What is the sleep habit of the student at the last index?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d92e499",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.???(???)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af3d9d8c",
+   "metadata": {},
+   "source": [
+    "### How many current lecture (example: LEC001) students are in the dataset? \n",
+    "\n",
+    "- use `for` loop to iterate over the dataset:\n",
+    "    - `count` function gives you total number of students\n",
+    "    - use `range` built-in function to generate sequence of integers from `0` to `count - 1`\n",
+    "- use `get_lecture` to retrieve lecture column value\n",
+    "- use `if` condition, to determine whether current student is part of `LEC001`\n",
+    "    - `True` evaluation: increment count\n",
+    "    - `False` evaluation: nothing to do"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e024c488",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9ff6434",
+   "metadata": {},
+   "source": [
+    "### What is the age of the oldest student in current lecture (example: LEC001)?\n",
+    "\n",
+    "- use `for` loop to iterate over the dataset just like last problem\n",
+    "- use `get_age` to retrieve lecture column value\n",
+    "    - if: age is '' (empty), move on to next student using `continue`\n",
+    "    - make sure to typecast return value to an integer\n",
+    "- use `get_lecture` to retrieve lecture column value\n",
+    "- use `if` condition, to determine whether current student is part of `LEC001`\n",
+    "    - use `if` condition to determine whether current student's age is greater than previously known max age\n",
+    "        - `True` evaluation: replace previously known max age with current age\n",
+    "        - `False` evaluation: nothing to do"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38bd778a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b40a32fb",
+   "metadata": {},
+   "source": [
+    "### What is the age of the youngest student in current lecture (example: LEC001)?\n",
+    "- use similar algorithm as above question"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea77e0cd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48f1c791",
+   "metadata": {},
+   "source": [
+    "### What major is the youngest student in current lecture (example: LEC001) planning to declare?\n",
+    "- now, we need to find some other detail about the youngest student\n",
+    "- often, you'll have to keep track of ID of the max or min, so that you can retrive other details about that data entry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a524873b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5294702a",
+   "metadata": {},
+   "source": [
+    "### Considering current lecture students (example: LEC001), what is the age of the first student residing at zip code 53715?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fada2a40",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68793d99",
+   "metadata": {},
+   "source": [
+    "## Self-practice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2eeed867",
+   "metadata": {},
+   "source": [
+    "### How many current lecture (example: LEC001) students are runners? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ea57e12",
+   "metadata": {},
+   "source": [
+    "### How many current lecture (example: LEC001) students are procrastinators? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf0ac7c8",
+   "metadata": {},
+   "source": [
+    "### How many current lecture (example: LEC001) students own or have owned a pet?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffd5e10f",
+   "metadata": {},
+   "source": [
+    "### What sleep habit does the youngest student in current lecture (example: LEC001) have?\n",
+    "- try to solve this from scratch, instead of copy-pasting code to find mimimum age"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f255b95a",
+   "metadata": {},
+   "source": [
+    "### What sleep habit does the oldest student in current lecture (example: LEC001) have?\n",
+    "- try to solve this from scratch, instead of copy-pasting code to find mimimum age"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db60812c",
+   "metadata": {},
+   "source": [
+    "### Considering current lecture students (example: LEC001), is the first student with age 18 a runner?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d55fa983",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/f22/meena_lec_notes/lec-12/.ipynb_checkpoints/project-checkpoint.py b/f22/meena_lec_notes/lec-12/.ipynb_checkpoints/project-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..1689e4461f9cd30e83c6084431577d9ea99e3dae
--- /dev/null
+++ b/f22/meena_lec_notes/lec-12/.ipynb_checkpoints/project-checkpoint.py
@@ -0,0 +1,67 @@
+__student__ = []
+
+
+def __init__():
+    import csv
+    """This function will read in the csv_file and store it in a list of dictionaries"""
+    __student__.clear()
+    with open('cs220_survey_data.csv', mode='r') as csv_file:
+        csv_reader = csv.DictReader(csv_file)
+        for row in csv_reader:
+            __student__.append(row)
+
+
+def count():
+    """This function will return the number of records in the dataset"""
+    return len(__student__)
+
+
+def get_lecture(idx):
+    """get_lecture(idx) returns the lecture of the student in row idx"""
+    return __student__[int(idx)]['Lecture']
+
+
+def get_age(idx):
+    """get_age(idx) returns the age of the student in row idx"""
+    return __student__[int(idx)]['Age']
+
+
+def get_major(idx):
+    """get_major(idx) returns the major of the student in row idx"""
+    return __student__[int(idx)]['Major']
+
+
+def get_zip_code(idx):
+    """get_zip_code(idx) returns the residential zip code of the student in row idx"""
+    return __student__[int(idx)]['Zip Code']
+
+def get_latitude(idx):
+    """get_latitude(idx) returns the latitude of the student's favourite place in row idx"""
+    return __student__[int(idx)]['Latitude']
+
+def get_longitude(idx):
+    """get_longitude(idx) returns the longitude of the student's favourite place in row idx"""
+    return __student__[int(idx)]['Longitude']
+
+def get_piazza_topping(idx):
+    """get_piazza_topping(idx) returns the preferred pizza toppings of the student in row idx"""
+    return __student__[int(idx)]['Pizza topping']
+
+def get_pet_owner(idx):
+    """get_pet_owner(idx) returns the pet preference of student in row idx"""
+    return __student__[int(idx)]['Pet preference']
+
+def get_runner(idx):
+    """get_runner(idx) returns whether student in row idx is a runner"""
+    return __student__[int(idx)]['Runner']
+
+def get_sleep_habit(idx):
+    """get_sleep_habit(idx) returns the sleep habit of the student in row idx"""
+    return __student__[int(idx)]['Sleep habit']
+
+def get_procrastinator(idx):
+    """get_procrastinator(idx) returns whether student in row idx is a procrastinator"""
+    return __student__[int(idx)]['Procrastinator']
+
+
+__init__()
diff --git a/f22/meena_lec_notes/lec-13/.ipynb_checkpoints/lec_13_Strings-checkpoint.ipynb b/f22/meena_lec_notes/lec-13/.ipynb_checkpoints/lec_13_Strings-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b54f13a59f65dadf3a53d99c544c32e7c17dca05
--- /dev/null
+++ b/f22/meena_lec_notes/lec-13/.ipynb_checkpoints/lec_13_Strings-checkpoint.ipynb
@@ -0,0 +1,966 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Strings\n",
+    "\n",
+    "## Readings\n",
+    "\n",
+    "- Chapter 8 ( + 9) of Think Python\n",
+    "- Chapter 7 of Python for Everybody"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Review1: Build a string using `+=`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I have a dog named Blacky, a cat named Tommy, and a chicken named Bling\n"
+     ]
+    }
+   ],
+   "source": [
+    "dog = \"Blacky\"\n",
+    "cat = \"Tommy\"\n",
+    "chicken = \"Bling\"\n",
+    "\n",
+    "sentence = \"\"\n",
+    "sentence += \"I have a dog named \" + dog\n",
+    "sentence += \", a cat named \" + cat\n",
+    "sentence += \", and a chicken named \" + chicken\n",
+    "print(sentence)\n",
+    "\n",
+    "# TODO: print the length of sentence using len\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning Objectives:\n",
+    "- Compare two strings by hand using < or > \n",
+    "- Recognize common string methods, explain what they do, and use them in Python code\n",
+    "- Define the term sequence, name common sequence operations, and explain how a string is a sequence\n",
+    "- Index and slice strings using correct syntax, including positive and negative indices\n",
+    "- Read and Write code that uses a for loop to iterate over a string"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare two strings by hand using `<`, `>`, `==`, or `!=` "
+   ]
+  },
+  {
+   "attachments": {
+    "string%20comparison.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:string%20comparison.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "False\n",
+      "True\n",
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"cat\" != \"dog\") # use !=\n",
+    "print(\"cat\" == \"dog\") # TODO: use ==\n",
+    "print(\"cat\" < \"dog\") # TODO: use <\n",
+    "print(\"cat\" > \"dog\") # TODO: use >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### String comparison:\n",
+    "\n",
+    "Strings are compared one char at a time, using the ASCII table: https://simple.wikipedia.org/wiki/ASCII\n",
+    "\n",
+    "#### Exceptions\n",
+    "\n",
+    "1. upper case comes before lower case\n",
+    "2. string of digits are compared one character at a time\n",
+    "3. prefixes come before any word containing that prefix (because space comes before any alphabet in the ASCII table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "True\n",
+      "True\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"H\" < \"h\")                 # upper case comes before lower case\n",
+    "print(\"dorm room\" < \"dormroom\")  # space comes before 'r' in the ASCII table\n",
+    "print(\"base\" < \"baseball\")       # strings that end come before strings that continue, \n",
+    "                                 # that is no character comes before some character\n",
+    "print(\"11\" < \"2\")                # strings of digits are compared one character at a time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You keep the comparison going until you find the first non-matching character."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"doo doo\" < \"dog\") # \"o\" comes after \"g\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### String methods\n",
+    "\n",
+    "- Strings have special functions that are part of the definition of a string\n",
+    "- These are called methods and are called with a '.', similar to modules"
+   ]
+  },
+  {
+   "attachments": {
+    "string%20methods.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:string%20methods.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BLACKY\n",
+      "blacky\n",
+      "Blacky\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dog.upper())     \n",
+    "print(dog.lower())\n",
+    "print(dog) # calling a method on a string does not change the original variable's value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, how do you update the original variable?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dog = dog.lower()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`dog.upper()` is equivalent to `str.upper(dog)`. Programmers don't prefer the latter usage as `str` is redundant (it is obvious that dog variable stores a data type of string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'BLACKY'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str.upper(dog)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Stripping removes whitespace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'       A       B\\nC      '"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_word = \"       A       B\\nC      \"\n",
+    "some_word"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       A       B\n",
+      "C      \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(some_word)  # recall that print function formats the string and only \n",
+    "                  # displays the formatted output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A       B\\nC'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TODO: call strip method\n",
+    "some_word.strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A       B\\nC      '"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TODO: call lstrip method\n",
+    "some_word.lstrip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'       A       B\\nC'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TODO: call rstrip method\n",
+    "some_word.rstrip()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "find method returns index of first matching character of the search string or -1, if there is no match.\n",
+    "\n",
+    "- `find` requires a search string as argument. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "220 is Awesome!\n"
+     ]
+    }
+   ],
+   "source": [
+    "some_str = \"220 is Awesome!\"\n",
+    "print(some_str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-1\n",
+      "0\n",
+      "2\n",
+      "7\n",
+      "10\n",
+      "-1\n",
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(some_str.find(\"1\"))   \n",
+    "print(some_str.find(\"2\"))   \n",
+    "print(some_str.find(\"0\")) \n",
+    "print(some_str.find(\"A\")) \n",
+    "print(some_str.find(\"some\")) \n",
+    "\n",
+    "# TODO: try to find \"awe\": does it work? How can you make it work?\n",
+    "print(some_str.find(\"awe\"))\n",
+    "\n",
+    "# TODO: discuss: what method can you invoke prior to invoking find method to successfully find \"awe\"?\n",
+    "print(some_str.lower().find(\"awe\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "False\n",
+      "True\n",
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(some_str.startswith(\"220\"))\n",
+    "print(some_str.startswith(\"319\"))\n",
+    "print(some_str.endswith(\"some!\"))\n",
+    "print(some_str.endswith(\"Awesome\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Replace replaces all matching occurrence.\n",
+    "\n",
+    "`string_to_updated.replace(search_string, replacement_string)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "220 is AwEsomE!\n",
+      "220 is Awesome!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(some_str.replace(\"e\", \"E\"))\n",
+    "print(some_str.replace(\"3\", \"three\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "String methods can be called on literals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HELLO\n",
+      "Moot mo at tho biko racks\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello\".upper())\n",
+    "print(\"Meet me at the bike racks\".replace('e', 'o'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Format function enables us specify placeholders within the string, which can be replaced with some variable's value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dear Viyan, your grade for exam1 is A\n",
+      "Dear Meena, your grade for exam1 is A\n"
+     ]
+    }
+   ],
+   "source": [
+    "email = \"Dear {}, your grade for exam1 is {}\"\n",
+    "print(email.format(\"Viyan\", \"A\"))\n",
+    "\n",
+    "# TODO: give yourself or your friend some grade\n",
+    "print(email.format(\"Meena\", \"A\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "Replacement index 1 out of range for positional args tuple",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [19]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# TODO: what will happen when you pass only one argument to format method using email string?\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43memail\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mRogers\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n",
+      "\u001b[0;31mIndexError\u001b[0m: Replacement index 1 out of range for positional args tuple"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: what will happen when you pass only one argument to format method using email string?\n",
+    "print(email.format(\"Rogers\")) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sequence\n",
+    "\n",
+    "- Definition: a sequence is a collection of numbered/ordered values\n",
+    "- String: a sequence of one-character strings"
+   ]
+  },
+  {
+   "attachments": {
+    "sequences.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:sequences.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "220 is Awesome!\n",
+      "15\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: find length of some_str\n",
+    "print(some_str)\n",
+    "print(len(some_str))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Indexing\n",
+    "\n",
+    "- enables you to extract one item in your sequence, that is one character in a string\n",
+    "- Syntax: string_var`[index]`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Friday\n",
+      "r\n",
+      "y\n",
+      "y\n",
+      "a\n"
+     ]
+    },
+    {
+     "ename": "IndexError",
+     "evalue": "string index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [21]\u001b[0m, in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(day[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]) \u001b[38;5;66;03m# last\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(day[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m]) \u001b[38;5;66;03m# 2nd last\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mday\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m]\u001b[49m)\n",
+      "\u001b[0;31mIndexError\u001b[0m: string index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "day = \"Friday\"\n",
+    "print(day)\n",
+    "print(day[1])  # 2nd character\n",
+    "print(day[5])  # last\n",
+    "\n",
+    "print(day[-1]) # last\n",
+    "print(day[-2]) # 2nd last\n",
+    "print(day[50]) # this won't work"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Slicing\n",
+    "- enables you to extract a sub-sequence\n",
+    "- sub-sequence will be of same type as original sequence\n",
+    "- Syntax: string_var`[start_index:end_indx]`:\n",
+    "    - start_index is inclusive\n",
+    "    - end_index is exclusive\n",
+    "    - index need not be in range. Slicing will ignore indices which are not in range of `0` to `len(string_var) - 1`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Friday\n",
+      "ri\n",
+      "riday\n",
+      "riday\n",
+      "Fri\n",
+      "Friday\n",
+      "da\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(day)\n",
+    "print(day[1:3])    # include 1, exclude 3\n",
+    "print(day[1:100])  # slicing is forgiving\n",
+    "print(day[1:])     # can skip 2nd number\n",
+    "print(day[:3])     # can skip 1st number\n",
+    "print(day[:])      # this, too!\n",
+    "print(day[-3:-1])  # can use negative indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### for loops\n",
+    "\n",
+    "- can iterate over every item in a sequence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F\n",
+      "r\n",
+      "i\n",
+      "d\n",
+      "a\n",
+      "y\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print each letter of the string using while loop\n",
+    "index = 0\n",
+    "while index < len(day):\n",
+    "    print(day[index])\n",
+    "    index += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F\n",
+      "r\n",
+      "i\n",
+      "d\n",
+      "a\n",
+      "y\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print each letter of the string using for loop\n",
+    "# letter is a new variable that is the value of each iteration\n",
+    "\n",
+    "for letter in day:\n",
+    "    print(letter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'b' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [25]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# the 2nd variable must be defined\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# 2nd var b undefined\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m \u001b[43mb\u001b[49m: \n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28mprint\u001b[39m(a)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'b' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# the 2nd variable must be defined\n",
+    "# 2nd var b undefined\n",
+    "for a in b: \n",
+    "    print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F\n",
+      "r\n",
+      "i\n",
+      "d\n",
+      "a\n",
+      "y\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print each letter of the string using for loop with range built-in function call\n",
+    "# range enables us to iterate over every index in the string\n",
+    "\n",
+    "for idx in range(len(day)):\n",
+    "    print(day[idx])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "r\n",
+      "d\n",
+      "y\n"
+     ]
+    }
+   ],
+   "source": [
+    "# range built-in function: an optional 3rd number is the increment\n",
+    "# let's print every other character in the string\n",
+    "for idx in range(1, len(day), 2):  \n",
+    "    print(day[idx])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NCAA\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Practice: Write a for loop to generate a string that makes an acronym\n",
+    "\n",
+    "phrase = \"National Collegiate Athletic Association 2022\"\n",
+    "acro = \"\"\n",
+    "for letter in phrase:\n",
+    "    if letter.upper() == letter and letter.isalpha():\n",
+    "        #print(letter)\n",
+    "        # How can we make sure you don't consider spaces and numbers?\n",
+    "        # TODO: try isalpha method (update if condition)\n",
+    "        # TODO: now instead of printing the letter, concatenate the letter to acro\n",
+    "        acro += letter\n",
+    "\n",
+    "print(acro)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Other string methods: https://www.w3schools.com/python/python_ref_string.asp. Methods in Python have very intuitive names. Please don't memorize the methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Wordle\n",
+    "### Self-practice example\n",
+    "- read through the below program, to understand its functionality"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Welcome to PyWordle!\n",
+      "You have 6 guesses to guess a 5 character word.\n",
+      "X\tThe letter is not in the word.\n",
+      "_\tThe letter is in the word, but in the wrong place.\n",
+      "O\tThe letter is in the correct place!\n",
+      "Guess the word: rance\n",
+      "RANCE\t____O\n",
+      "Guess the word: nacre\n",
+      "NACRE\t____O\n",
+      "Guess the word: crane\n",
+      "CRANE\tOOOOO\n",
+      "You won in 3 guesses!\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_wordle_results(guess):\n",
+    "    wordle_result = \"\"\n",
+    "    for i in range(len(guess)):\n",
+    "        if guess[i] == word_of_the_day[i]:\n",
+    "            wordle_result += \"O\"\n",
+    "        elif word_of_the_day.find(guess[i]) != -1:\n",
+    "            wordle_result += \"_\"\n",
+    "        else:\n",
+    "            wordle_result += \"X\"\n",
+    "    return wordle_result\n",
+    "\n",
+    "max_num_guesses = 6\n",
+    "current_num_guesses = 1\n",
+    "word_of_the_day = \"CRANE\"\n",
+    "\n",
+    "print(\"Welcome to PyWordle!\")\n",
+    "print(\"You have 6 guesses to guess a 5 character word.\")\n",
+    "print(\"X\\tThe letter is not in the word.\")\n",
+    "print(\"_\\tThe letter is in the word, but in the wrong place.\")\n",
+    "print(\"O\\tThe letter is in the correct place!\")\n",
+    "\n",
+    "while current_num_guesses <= max_num_guesses:\n",
+    "    guess = input(\"Guess the word: \")\n",
+    "    guess = guess.upper()\n",
+    "\n",
+    "    wordle_results = get_wordle_results(guess)\n",
+    "    print(\"{}\\t{}\".format(guess, wordle_results))\n",
+    "    if guess == word_of_the_day:\n",
+    "        break\n",
+    "    current_num_guesses += 1\n",
+    "    \n",
+    "if current_num_guesses > max_num_guesses:\n",
+    "    print(\"Better luck next time!\")\n",
+    "    print(\"The word was: {}\".format(word_of_the_day))\n",
+    "else:\n",
+    "    print(\"You won in {} guesses!\".format(current_num_guesses))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/f22/meena_lec_notes/lec-14/__pycache__/project.cpython-39.pyc b/f22/meena_lec_notes/lec-14/__pycache__/project.cpython-39.pyc
deleted file mode 100644
index 416418170a6201b762a4161f63f214dc41f03df3..0000000000000000000000000000000000000000
Binary files a/f22/meena_lec_notes/lec-14/__pycache__/project.cpython-39.pyc and /dev/null differ
diff --git a/f22/meena_lec_notes/lec-15/.ipynb_checkpoints/lec_15_CSV-checkpoint.ipynb b/f22/meena_lec_notes/lec-15/.ipynb_checkpoints/lec_15_CSV-checkpoint.ipynb
index 326eb5c458c9ef57788b488174923862038e4fd8..8f66fb13286d45d211cb82a1563301c52ecab48b 100644
--- a/f22/meena_lec_notes/lec-15/.ipynb_checkpoints/lec_15_CSV-checkpoint.ipynb
+++ b/f22/meena_lec_notes/lec-15/.ipynb_checkpoints/lec_15_CSV-checkpoint.ipynb
@@ -217,11 +217,12 @@
       "text/plain": [
        "['Lecture',\n",
        " 'Age',\n",
-       " 'Primary major',\n",
-       " 'Other majors',\n",
+       " 'Major',\n",
        " 'Zip Code',\n",
+       " 'Latitude',\n",
+       " 'Longitude',\n",
        " 'Pizza topping',\n",
-       " 'Pet owner',\n",
+       " 'Pet preference',\n",
        " 'Runner',\n",
        " 'Sleep habit',\n",
        " 'Procrastinator']"
@@ -247,36 +248,39 @@
     {
      "data": {
       "text/plain": [
-       "[['LEC002',\n",
-       "  '19',\n",
-       "  'Engineering: Mechanical',\n",
-       "  '',\n",
-       "  '53711',\n",
-       "  'pepperoni',\n",
-       "  'Yes',\n",
+       "[['LEC001',\n",
+       "  '22',\n",
+       "  'Engineering: Biomedical',\n",
+       "  '53703',\n",
+       "  '43.073051',\n",
+       "  '-89.40123',\n",
+       "  'none (just cheese)',\n",
+       "  'neither',\n",
        "  'No',\n",
-       "  'night owl',\n",
+       "  'no preference',\n",
        "  'Maybe'],\n",
-       " ['LEC002',\n",
-       "  '20',\n",
-       "  'Science: Physics',\n",
-       "  'Astronomy-Physics, History',\n",
-       "  '53726',\n",
-       "  'pineapple',\n",
-       "  'Yes',\n",
-       "  'Yes',\n",
-       "  'night owl',\n",
-       "  'Yes'],\n",
-       " ['LEC001',\n",
-       "  '20',\n",
-       "  'Science: Chemistry',\n",
+       " ['LEC006',\n",
        "  '',\n",
-       "  '53703',\n",
-       "  'pepperoni',\n",
-       "  'Yes',\n",
+       "  'Undecided',\n",
+       "  '53706',\n",
+       "  '43.073051',\n",
+       "  '-89.40123',\n",
+       "  'none (just cheese)',\n",
+       "  'neither',\n",
+       "  'No',\n",
+       "  'no preference',\n",
+       "  'Maybe'],\n",
+       " ['LEC004',\n",
+       "  '18',\n",
+       "  'Engineering: Industrial',\n",
+       "  '53715',\n",
+       "  '43.073051',\n",
+       "  '-89.40123',\n",
+       "  'none (just cheese)',\n",
+       "  'neither',\n",
        "  'No',\n",
-       "  'early bird',\n",
-       "  'No']]"
+       "  'no preference',\n",
+       "  'Maybe']]"
       ]
      },
      "execution_count": 9,
@@ -309,7 +313,7 @@
     {
      "data": {
       "text/plain": [
-       "'night owl'"
+       "'no preference'"
       ]
      },
      "execution_count": 10,
@@ -318,7 +322,7 @@
     }
    ],
    "source": [
-    "cs220_data[1][8] # bad example: we hard-coded the column index"
+    "cs220_data[1][9] # bad example: we hard-coded the column index"
    ]
   },
   {
@@ -340,7 +344,7 @@
     {
      "data": {
       "text/plain": [
-       "'night owl'"
+       "'no preference'"
       ]
      },
      "execution_count": 11,
@@ -369,7 +373,7 @@
     {
      "data": {
       "text/plain": [
-       "'LEC004'"
+       "'LEC005'"
       ]
      },
      "execution_count": 12,
@@ -398,7 +402,7 @@
     {
      "data": {
       "text/plain": [
-       "[29, 30, 30]"
+       "[32, 28, 28]"
       ]
      },
      "execution_count": 13,
@@ -454,8 +458,10 @@
     "        return None\n",
     "    \n",
     "    # TODO: handle type conversions\n",
-    "    if col_name in [\"Age\",]:\n",
+    "    if col_name in [\"Age\", 'Zip Code',]:\n",
     "        return int(val)\n",
+    "    elif col_name in ['Latitude', 'Longitude']:\n",
+    "        return float(val)\n",
     "    \n",
     "    return val"
    ]
@@ -480,26 +486,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LEC001 average student age: 19.93\n",
-      "LEC002 average student age: 19.8\n",
-      "LEC003 average student age: 19.38\n",
-      "LEC004 average student age: 19.27\n"
+      "LEC001 average student age: 20.05\n",
+      "LEC002 average student age: 19.68\n",
+      "LEC003 average student age: 19.14\n",
+      "LEC004 average student age: 19.99\n",
+      "LEC005 average student age: 19.42\n",
+      "LEC006 average student age: 18.63\n"
      ]
     }
    ],
    "source": [
-    "# TODO: initialize 4 lists for the 4 lectures\n",
+    "# TODO: initialize 6 lists for the 6 lectures\n",
     "lec1_ages = []\n",
     "lec2_ages = []\n",
     "lec3_ages = []\n",
     "lec4_ages = []\n",
+    "lec5_ages = []\n",
+    "lec6_ages = []\n",
     "\n",
     "# Iterate over the data and populate the lists\n",
     "\n",
     "for row_idx in range(len(cs220_data)):\n",
     "    age = cell(row_idx, \"Age\")\n",
     "    \n",
-    "    if age != None:\n",
+    "    if age != None and age > 0 and age < 120:\n",
     "        lecture = cell(row_idx, \"Lecture\")\n",
     "        if lecture == \"LEC001\":\n",
     "            lec1_ages.append(age)\n",
@@ -508,13 +518,19 @@
     "        elif lecture == \"LEC003\":\n",
     "            lec3_ages.append(age)\n",
     "        elif lecture == \"LEC004\":\n",
-    "            lec4_ages.append(age)    \n",
+    "            lec4_ages.append(age) \n",
+    "        elif lecture == \"LEC005\":\n",
+    "            lec5_ages.append(age) \n",
+    "        elif lecture == \"LEC006\":\n",
+    "            lec6_ages.append(age) \n",
     "            \n",
     "# TODO: compute average age of each lecture\n",
     "print(\"LEC001 average student age:\", round(sum(lec1_ages) / len(lec1_ages), 2))\n",
     "print(\"LEC002 average student age:\", round(sum(lec2_ages) / len(lec2_ages), 2))\n",
     "print(\"LEC003 average student age:\", round(sum(lec3_ages) / len(lec3_ages), 2))\n",
-    "print(\"LEC004 average student age:\", round(sum(lec4_ages) / len(lec4_ages), 2))"
+    "print(\"LEC004 average student age:\", round(sum(lec4_ages) / len(lec4_ages), 2))\n",
+    "print(\"LEC005 average student age:\", round(sum(lec5_ages) / len(lec5_ages), 2))\n",
+    "print(\"LEC006 average student age:\", round(sum(lec6_ages) / len(lec6_ages), 2))"
    ]
   },
   {
@@ -534,43 +550,44 @@
     {
      "data": {
       "text/plain": [
-       "['53713',\n",
-       " '55416',\n",
-       " '53076',\n",
-       " '53703-1104',\n",
-       " '52816',\n",
-       " '53706-1203',\n",
-       " '53590',\n",
-       " '53705',\n",
-       " '59301',\n",
-       " '53706-1188',\n",
-       " '53706',\n",
-       " '5 3706',\n",
-       " '52706',\n",
-       " '10306',\n",
-       " '54636',\n",
-       " '53717',\n",
-       " '53726',\n",
-       " 'internation student',\n",
-       " '53708',\n",
-       " '53703',\n",
-       " '53706-1406',\n",
-       " '53719',\n",
-       " '43706',\n",
-       " '53704',\n",
-       " '19002',\n",
-       " '53089',\n",
-       " '53597',\n",
-       " '53706-1127',\n",
-       " '57305',\n",
-       " '53711',\n",
-       " '53562',\n",
-       " '53715',\n",
-       " '53175',\n",
-       " '92376',\n",
-       " '83001',\n",
-       " '53701',\n",
-       " '53575']"
+       "[5,\n",
+       " 53511,\n",
+       " 53132,\n",
+       " 50703,\n",
+       " 53532,\n",
+       " 53151,\n",
+       " 55088,\n",
+       " 54706,\n",
+       " 53555,\n",
+       " 53558,\n",
+       " 48823,\n",
+       " 53562,\n",
+       " 53051,\n",
+       " 53701,\n",
+       " 53703,\n",
+       " 53704,\n",
+       " 53705,\n",
+       " 53706,\n",
+       " 53066,\n",
+       " 53711,\n",
+       " 20815,\n",
+       " 53713,\n",
+       " 53714,\n",
+       " 53715,\n",
+       " 53716,\n",
+       " 53717,\n",
+       " 53590,\n",
+       " 53719,\n",
+       " 57303,\n",
+       " 53593,\n",
+       " 53718,\n",
+       " 53726,\n",
+       " 60521,\n",
+       " 89451,\n",
+       " 94707,\n",
+       " 57075,\n",
+       " 26617,\n",
+       " 60540]"
       ]
      },
      "execution_count": 16,
@@ -648,7 +665,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/f22/meena_lec_notes/lec-15/.ipynb_checkpoints/lec_15_CSV_template-checkpoint.ipynb b/f22/meena_lec_notes/lec-15/.ipynb_checkpoints/lec_15_CSV_template-checkpoint.ipynb
index 682093ff9031ac6efdbd0bf56229ef5b66019217..6bed3aabc7368f5a9a515858e0e96e0c804e6fb9 100644
--- a/f22/meena_lec_notes/lec-15/.ipynb_checkpoints/lec_15_CSV_template-checkpoint.ipynb
+++ b/f22/meena_lec_notes/lec-15/.ipynb_checkpoints/lec_15_CSV_template-checkpoint.ipynb
@@ -307,7 +307,7 @@
    },
    "outputs": [],
    "source": [
-    "# TODO: initialize 4 lists for the 4 lectures\n",
+    "# TODO: initialize 6 lists for the 6 lectures\n",
     "\n",
     "\n",
     "# Iterate over the data and populate the lists\n",
@@ -317,7 +317,9 @@
     "print(\"LEC001 average student age:\", round(sum(lec1_ages) / len(lec1_ages), 2))\n",
     "print(\"LEC002 average student age:\", round(sum(lec2_ages) / len(lec2_ages), 2))\n",
     "print(\"LEC003 average student age:\", round(sum(lec3_ages) / len(lec3_ages), 2))\n",
-    "print(\"LEC004 average student age:\", round(sum(lec4_ages) / len(lec4_ages), 2))"
+    "print(\"LEC004 average student age:\", round(sum(lec4_ages) / len(lec4_ages), 2))\n",
+    "print(\"LEC005 average student age:\", round(sum(lec5_ages) / len(lec5_ages), 2))\n",
+    "print(\"LEC006 average student age:\", round(sum(lec6_ages) / len(lec6_ages), 2))"
    ]
   },
   {
@@ -404,7 +406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/f22/meena_lec_notes/lec-15/lec_15_CSV.ipynb b/f22/meena_lec_notes/lec-15/lec_15_CSV.ipynb
index 8f66fb13286d45d211cb82a1563301c52ecab48b..384794fe6982a36c2aacbe663f7865fd19cc0b5d 100644
--- a/f22/meena_lec_notes/lec-15/lec_15_CSV.ipynb
+++ b/f22/meena_lec_notes/lec-15/lec_15_CSV.ipynb
@@ -200,7 +200,20 @@
    "execution_count": 7,
    "id": "d3c252b4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'cs220_survey_data.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# TODO: call the process_csv function and store the list of lists in cs220_csv\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m cs220_csv \u001b[38;5;241m=\u001b[39m \u001b[43mprocess_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcs220_survey_data.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36mprocess_csv\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprocess_csv\u001b[39m(filename):\n\u001b[1;32m      3\u001b[0m     \u001b[38;5;66;03m# open the file, its a text file utf-8\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m     example_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;66;03m# prepare it for reading as a CSV object\u001b[39;00m\n\u001b[1;32m      6\u001b[0m     example_reader \u001b[38;5;241m=\u001b[39m csv\u001b[38;5;241m.\u001b[39mreader(example_file)\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'cs220_survey_data.csv'"
+     ]
+    }
+   ],
    "source": [
     "# TODO: call the process_csv function and store the list of lists in cs220_csv\n",
     "cs220_csv = process_csv(\"cs220_survey_data.csv\")"
@@ -208,31 +221,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "5838ae5f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Lecture',\n",
-       " 'Age',\n",
-       " 'Major',\n",
-       " 'Zip Code',\n",
-       " 'Latitude',\n",
-       " 'Longitude',\n",
-       " 'Pizza topping',\n",
-       " 'Pet preference',\n",
-       " 'Runner',\n",
-       " 'Sleep habit',\n",
-       " 'Procrastinator']"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Store the header row into cs220_header, using indexing\n",
     "cs220_header = cs220_csv[0]\n",
@@ -241,53 +233,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "66fda88d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[['LEC001',\n",
-       "  '22',\n",
-       "  'Engineering: Biomedical',\n",
-       "  '53703',\n",
-       "  '43.073051',\n",
-       "  '-89.40123',\n",
-       "  'none (just cheese)',\n",
-       "  'neither',\n",
-       "  'No',\n",
-       "  'no preference',\n",
-       "  'Maybe'],\n",
-       " ['LEC006',\n",
-       "  '',\n",
-       "  'Undecided',\n",
-       "  '53706',\n",
-       "  '43.073051',\n",
-       "  '-89.40123',\n",
-       "  'none (just cheese)',\n",
-       "  'neither',\n",
-       "  'No',\n",
-       "  'no preference',\n",
-       "  'Maybe'],\n",
-       " ['LEC004',\n",
-       "  '18',\n",
-       "  'Engineering: Industrial',\n",
-       "  '53715',\n",
-       "  '43.073051',\n",
-       "  '-89.40123',\n",
-       "  'none (just cheese)',\n",
-       "  'neither',\n",
-       "  'No',\n",
-       "  'no preference',\n",
-       "  'Maybe']]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# TODO: Store all of the data rows into cs220_data, using slicing\n",
     "cs220_data = cs220_csv[1:]\n",
@@ -306,21 +255,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "4b8dbe8b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'no preference'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cs220_data[1][9] # bad example: we hard-coded the column index"
    ]
@@ -337,21 +275,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "f2e52e06",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'no preference'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cs220_data[1][cs220_header.index(\"Sleep habit\")]"
    ]
@@ -366,21 +293,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "3617b3de",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'LEC005'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cs220_data[3][cs220_header.index(\"Lecture\")]"
    ]
@@ -395,21 +311,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "45909f22",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[32, 28, 28]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ages_in_ten_years = []\n",
     "\n",
@@ -437,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "bba90038",
    "metadata": {},
    "outputs": [],
@@ -476,25 +381,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "f0a05e42",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LEC001 average student age: 20.05\n",
-      "LEC002 average student age: 19.68\n",
-      "LEC003 average student age: 19.14\n",
-      "LEC004 average student age: 19.99\n",
-      "LEC005 average student age: 19.42\n",
-      "LEC006 average student age: 18.63\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# TODO: initialize 6 lists for the 6 lectures\n",
     "lec1_ages = []\n",
@@ -543,58 +435,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "c28e77ce",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[5,\n",
-       " 53511,\n",
-       " 53132,\n",
-       " 50703,\n",
-       " 53532,\n",
-       " 53151,\n",
-       " 55088,\n",
-       " 54706,\n",
-       " 53555,\n",
-       " 53558,\n",
-       " 48823,\n",
-       " 53562,\n",
-       " 53051,\n",
-       " 53701,\n",
-       " 53703,\n",
-       " 53704,\n",
-       " 53705,\n",
-       " 53706,\n",
-       " 53066,\n",
-       " 53711,\n",
-       " 20815,\n",
-       " 53713,\n",
-       " 53714,\n",
-       " 53715,\n",
-       " 53716,\n",
-       " 53717,\n",
-       " 53590,\n",
-       " 53719,\n",
-       " 57303,\n",
-       " 53593,\n",
-       " 53718,\n",
-       " 53726,\n",
-       " 60521,\n",
-       " 89451,\n",
-       " 94707,\n",
-       " 57075,\n",
-       " 26617,\n",
-       " 60540]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# TODO: initialize list of keep track of zip codes\n",
     "zip_codes = []\n",
diff --git a/f22/meena_lec_notes/lec-16/.ipynb_checkpoints/lec_16_list_practice-checkpoint.ipynb b/f22/meena_lec_notes/lec-16/.ipynb_checkpoints/lec_16_list_practice-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e8eb15aa9e53b941ea4738b88eb7eb5558f75c82
--- /dev/null
+++ b/f22/meena_lec_notes/lec-16/.ipynb_checkpoints/lec_16_list_practice-checkpoint.ipynb
@@ -0,0 +1,1278 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "72348536",
+   "metadata": {},
+   "source": [
+    "# List Practice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2bf3c996",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b34b84ae",
+   "metadata": {},
+   "source": [
+    "### Warmup 1: min / max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b89c41e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-4\n",
+      "220\n"
+     ]
+    }
+   ],
+   "source": [
+    "some_list = [45, -4, 66, 220, 10]\n",
+    "\n",
+    "min_val = None\n",
+    "for val in some_list:\n",
+    "    if min_val == None or val < min_val:\n",
+    "        min_val = val\n",
+    "    \n",
+    "print(min_val)\n",
+    "\n",
+    "max_val = None\n",
+    "for val in some_list:\n",
+    "    if max_val == None or val > max_val:\n",
+    "        max_val = val\n",
+    "    \n",
+    "print(max_val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59a689b1",
+   "metadata": {},
+   "source": [
+    "### Warmup 2: median"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2fd5e101",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Median of [1, 2, 3, 4, 5] is 3\n",
+      "Median of [1, 2, 3, 4, 5, 6] is 3.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "def median(some_items):\n",
+    "    \"\"\"\n",
+    "    Returns median of a list passed as argument\n",
+    "    \"\"\"\n",
+    "    some_items.sort()\n",
+    "    n = len(some_items)\n",
+    "    \n",
+    "    if n % 2 == 1:\n",
+    "        return some_items[n // 2] \n",
+    "    else:\n",
+    "        first_middle = some_items[n//2 - 1]\n",
+    "        second_middle = some_items[n // 2]\n",
+    "        median = (first_middle + second_middle) / 2\n",
+    "        return median\n",
+    "    \n",
+    "nums = [5, 4, 3, 2, 1]\n",
+    "print(\"Median of\", nums, \"is\" , median(nums))\n",
+    "\n",
+    "nums = [6, 5, 4, 3, 2, 1]\n",
+    "print(\"Median of\", nums, \"is\" , median(nums))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cf14bf7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Median of [1, 2, 3, 4, 5, 6] is B\n"
+     ]
+    }
+   ],
+   "source": [
+    "vals = [\"A\", \"C\", \"B\"]\n",
+    "print(\"Median of\", nums, \"is\" , median(vals))\n",
+    "\n",
+    "vals = [\"A\", \"C\", \"B\", \"D\"]\n",
+    "# print(\"Median of\", nums, \"is\" , median(vals)) # does not work due to TypeError"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdbf6f75",
+   "metadata": {},
+   "source": [
+    "### set data structure\n",
+    "\n",
+    "- **not a sequence**\n",
+    "- no ordering of values:\n",
+    "    - this implies that you can only store unique values within a `set`\n",
+    "- very helpful to find unique values stored in a `list`\n",
+    "    - easy to convert a `list` to `set` and vice-versa.\n",
+    "    - ordering is not guaranteed once we use `set`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "52e80a6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{10, 20, 30, 40, 50}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_set = {10, 20, 30, 30, 40, 50, 10} # use a pair of curly braces to define it\n",
+    "some_set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2587184f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{40, 10, 50, 20, 30}\n",
+      "[40, 10, 50, 20, 30]\n"
+     ]
+    }
+   ],
+   "source": [
+    "some_list = [10, 20, 30, 30, 40, 50, 10] # Initialize a list containing duplicate numbers\n",
+    "\n",
+    "# TODO: to find unique values, convert it into a set\n",
+    "print(set(some_list))\n",
+    "\n",
+    "# TODO: convert the set back into a list\n",
+    "print(list(set(some_list)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a143e1c",
+   "metadata": {},
+   "source": [
+    "Can you index / slice into a `set`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ce43cb95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# some_set[1] # doesn't work - remember set has no order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cd6473f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# some_set[1:] # doesn't work - remember set has no order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9d936c1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inspired by https://automatetheboringstuff.com/2e/chapter16/\n",
+    "def process_csv(filename):\n",
+    "    # open the file, its a text file utf-8\n",
+    "    example_file = open(filename, encoding=\"utf-8\")\n",
+    "    # prepare it for reading as a CSV object\n",
+    "    example_reader = csv.reader(example_file)\n",
+    "    # use the built-in list function to convert this into a list of lists\n",
+    "    example_data = list(example_reader)\n",
+    "    # close the file to tidy up our workspace\n",
+    "    example_file.close()\n",
+    "    # return the list of lists\n",
+    "    \n",
+    "    return example_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89621c98",
+   "metadata": {},
+   "source": [
+    "### Student Information Survey data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d3c252b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: call the process_csv function and store the list of lists in cs220_csv\n",
+    "cs220_csv = process_csv(\"cs220_survey_data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5838ae5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Lecture',\n",
+       " 'Age',\n",
+       " 'Major',\n",
+       " 'Zip Code',\n",
+       " 'Latitude',\n",
+       " 'Longitude',\n",
+       " 'Pizza topping',\n",
+       " 'Pet preference',\n",
+       " 'Runner',\n",
+       " 'Sleep habit',\n",
+       " 'Procrastinator']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Store the header row into cs220_header, using indexing\n",
+    "cs220_header = cs220_csv[0]\n",
+    "cs220_header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "66fda88d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['LEC001',\n",
+       "  '22',\n",
+       "  'Engineering: Biomedical',\n",
+       "  '53703',\n",
+       "  '43.073051',\n",
+       "  '-89.40123',\n",
+       "  'none (just cheese)',\n",
+       "  'neither',\n",
+       "  'No',\n",
+       "  'no preference',\n",
+       "  'Maybe'],\n",
+       " ['LEC006',\n",
+       "  '',\n",
+       "  'Undecided',\n",
+       "  '53706',\n",
+       "  '43.073051',\n",
+       "  '-89.40123',\n",
+       "  'none (just cheese)',\n",
+       "  'neither',\n",
+       "  'No',\n",
+       "  'no preference',\n",
+       "  'Maybe'],\n",
+       " ['LEC004',\n",
+       "  '18',\n",
+       "  'Engineering: Industrial',\n",
+       "  '53715',\n",
+       "  '43.073051',\n",
+       "  '-89.40123',\n",
+       "  'none (just cheese)',\n",
+       "  'neither',\n",
+       "  'No',\n",
+       "  'no preference',\n",
+       "  'Maybe']]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TODO: Store all of the data rows into cs220_data, using slicing\n",
+    "cs220_data = cs220_csv[1:]\n",
+    "\n",
+    "# TODO: use slicing to display top 3 rows data\n",
+    "cs220_data[:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4267fe3e",
+   "metadata": {},
+   "source": [
+    "### What `Pizza topping` does the 13th student prefer? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4b8dbe8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'pineapple'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cs220_data[12][6] # bad example: we hard-coded the column index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f125240",
+   "metadata": {},
+   "source": [
+    "What if we decided to add a new column before sleeping habit? Your code will no longer work.\n",
+    "\n",
+    "Instead of hard-coding column index, you should use `index` method, to lookup column index from the header variable. This will also make your code so much readable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f2e52e06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'pineapple'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cs220_data[12][cs220_header.index(\"Pizza topping\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d298a4c",
+   "metadata": {},
+   "source": [
+    "### What is the Lecture of the 4th student?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3617b3de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'LEC005'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cs220_data[3][cs220_header.index(\"Lecture\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "059de363",
+   "metadata": {},
+   "source": [
+    "### What **unique** `age` values are included in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "45909f22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 37, 41, 53]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ages = []\n",
+    "\n",
+    "for row in cs220_data:\n",
+    "    age = row[cs220_header.index(\"Age\")]\n",
+    "    \n",
+    "    if age == '':\n",
+    "        continue\n",
+    "        \n",
+    "    age = int(age)\n",
+    "    if age < 0 or age > 118:\n",
+    "        continue\n",
+    "        \n",
+    "    ages.append(age)\n",
+    "    \n",
+    "ages = list(set(ages))\n",
+    "ages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e18663d",
+   "metadata": {},
+   "source": [
+    "### cell function\n",
+    "\n",
+    "- It would be very helpful to define a cell function, which can handle missing data and type conversions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "bba90038",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cell(row_idx, col_name):\n",
+    "    \"\"\"\n",
+    "    Returns the data value (cell) corresponding to the row index and \n",
+    "    the column name of a CSV file.\n",
+    "    \"\"\"\n",
+    "    # TODO: get the index of col_name\n",
+    "    col_idx = cs220_header.index(col_name) \n",
+    "    \n",
+    "    # TODO: get the value of cs220_data at the specified cell\n",
+    "    val = cs220_data[row_idx][col_idx]  \n",
+    "    \n",
+    "    # TODO: handle missing values, by returning None\n",
+    "    if val == '':\n",
+    "        return None\n",
+    "    \n",
+    "    # TODO: handle type conversions\n",
+    "    if col_name in [\"Age\", 'Zip Code',]:\n",
+    "        return int(val)\n",
+    "    elif col_name in ['Latitude', 'Longitude']:\n",
+    "        return float(val)\n",
+    "    \n",
+    "    return val"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7c8e726",
+   "metadata": {},
+   "source": [
+    "### Function `avg_age_per_lecture(lecture)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4894d0c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def avg_age_per_lecture(lecture):\n",
+    "    '''\n",
+    "    avg_age_per_lecture(lecture) returns the average age of \n",
+    "    the students in the given `lecture`; if there are no\n",
+    "    students in the given `lecture`, it returns `None`\n",
+    "    '''\n",
+    "    # To compute average you don't need to actually populate a list.\n",
+    "    # But here a list will come in handy. It will help you with the None return requirement.\n",
+    "    ages = []\n",
+    "    for row_idx in range(len(cs220_data)):\n",
+    "        curr_lecture = cell(row_idx, \"Lecture\")\n",
+    "        if lecture == curr_lecture:\n",
+    "            age = cell(row_idx, \"Age\")\n",
+    "            if age != None and age > 0 and age <= 118:\n",
+    "                ages.append(age)\n",
+    "                \n",
+    "    if len(ages) > 0:\n",
+    "        return sum(ages) / len(ages)\n",
+    "    else:\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f0a05e42",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.683615819209038"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_age_per_lecture(\"LEC002\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ec9af3da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(avg_age_per_lecture(\"LEC007\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94548bf4",
+   "metadata": {},
+   "source": [
+    "### `sort` method versus `sorted` function\n",
+    "\n",
+    "- `sort` (and other list methods) have an impact on the original list\n",
+    "- `sorted` function returns a new list with expected ordering\n",
+    "- default sorting order is ascending / alphanumeric\n",
+    "- `reverse` parameter is applicable for both `sort` method and `sorted` function:\n",
+    "    - enables you to specify descending order by passing argument as `True`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "c1e555f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_list = [10, 4, 25, 2, -10] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "152297bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-10, 2, 4, 10, 25]\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: Invoke sort method\n",
+    "rv = some_list.sort()\n",
+    "print(some_list)\n",
+    "\n",
+    "# What does the sort method return? \n",
+    "# TODO: Capture return value into a variable rv and print the return value.\n",
+    "print(rv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c0d5e7d",
+   "metadata": {},
+   "source": [
+    "`sort` method returns `None` because it sorts the values in the original list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c06d8976",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-10, 2, 4, 10, 25]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: invoke sorted function and pass some_list as argument\n",
+    "# TODO: capture return value into sorted_some_list\n",
+    "sorted_some_list = sorted(some_list)\n",
+    "\n",
+    "# What does the sorted function return? \n",
+    "# It returns a brand new list with the values in sorted order\n",
+    "print(sorted_some_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ded0304c",
+   "metadata": {},
+   "source": [
+    "TODO: go back to `sort` method call and `sorted` function call and pass keyword argument `reverse = True`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3579e061",
+   "metadata": {},
+   "source": [
+    "Can you call `sort` method on a set?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "14d8a670",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# some_set.sort() \n",
+    "# doesn't work: no method named sort associated with type set\n",
+    "# you cannot sort a set because of the lack of ordering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fb64b44",
+   "metadata": {},
+   "source": [
+    "Can you pass a `set` as argument to `sorted` function? Python is intelligent :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "03b1183f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[10, 20, 30, 40, 50]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# works because Python converts the set into a list and then sorts the list\n",
+    "sorted(some_set) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efa2869e",
+   "metadata": {},
+   "source": [
+    "### Function: `find_majors(phrase)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "655f876d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_majors(phrase):\n",
+    "    \"\"\"\n",
+    "    find_majors(phrase) returns a list of all the room names that contain the \n",
+    "    substring (case insensitive match) `phrase`.\n",
+    "    \"\"\"\n",
+    "    # TODO: initialize the target list here\n",
+    "    majors = []\n",
+    "    \n",
+    "    # TODO: iterate over row indices\n",
+    "    for row_idx in range(len(cs220_data)):\n",
+    "        major = cell(row_idx, \"Major\")\n",
+    "        \n",
+    "        if phrase.lower() in major.lower():\n",
+    "            majors.append(major)\n",
+    "    \n",
+    "    return majors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed19265f",
+   "metadata": {},
+   "source": [
+    "### Find all  `major` that contain **either** `\"Computer\"` **or** `\"Science\"`.\n",
+    "\n",
+    "Your output **must** be a *list*. The order **does not** matter, but if a `major` contains **both** `\"Computer\"` and `\"Science\"`, then the room must be included **only once** in your list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ab656189",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Political Science',\n",
+       " 'Science: Other|Atmospheric and oceanic science',\n",
+       " 'Science: Other|Animal and Dairy Science',\n",
+       " 'Science: Other|Environmetal Science',\n",
+       " 'Science: Other|Geoscience',\n",
+       " 'Computer Science and Statistics',\n",
+       " 'Science: Other|animal sciences',\n",
+       " 'Science: Chemistry',\n",
+       " 'Environmental science',\n",
+       " 'Life Sciences Communication',\n",
+       " 'Communication Sciences and Disorder',\n",
+       " 'Science: Other|Atmospheric & Oceanic Sciences',\n",
+       " 'Science: Biology/Life',\n",
+       " 'Geoscience',\n",
+       " 'Science: Other|Political Science',\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences (AOS)',\n",
+       " 'Atmospheric Sciences',\n",
+       " 'Data Science',\n",
+       " 'Mathematics, Data Science',\n",
+       " 'Engineering: Other|Material Science Engineering',\n",
+       " 'Science: Other|Personal Finance',\n",
+       " 'Science: Other|Environmental Science',\n",
+       " 'Information science',\n",
+       " 'Science: Other',\n",
+       " 'Science: Other|Environmental science',\n",
+       " 'Engineering: Other|Computer Engineering',\n",
+       " 'Science: Other|Psychology',\n",
+       " 'Engineering: Other|Engineering: Computer',\n",
+       " 'Computer Science',\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences',\n",
+       " 'Science: Other|Politcal Science',\n",
+       " 'Science: Other|Science: Genetics and Genomics',\n",
+       " 'Engineering: Other|Computer engineering',\n",
+       " 'Science: Physics',\n",
+       " 'Science: Other|Biophysics PhD']"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "computer_majors = find_majors(\"Computer\")\n",
+    "science_majors = find_majors(\"Science\")\n",
+    "\n",
+    "computer_and_science_majors = computer_majors + science_majors\n",
+    "# TODO: Now find just the unique values\n",
+    "computer_and_science_majors = list(set(computer_and_science_majors))\n",
+    "computer_and_science_majors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64fd0945",
+   "metadata": {},
+   "source": [
+    "### Order the `major` that contain **either** `\"Computer\"` **or** `\"Science\"` using ascending order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d4e2e6fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Atmospheric Sciences',\n",
+       " 'Communication Sciences and Disorder',\n",
+       " 'Computer Science',\n",
+       " 'Computer Science and Statistics',\n",
+       " 'Data Science',\n",
+       " 'Engineering: Other|Computer Engineering',\n",
+       " 'Engineering: Other|Computer engineering',\n",
+       " 'Engineering: Other|Engineering: Computer',\n",
+       " 'Engineering: Other|Material Science Engineering',\n",
+       " 'Environmental science',\n",
+       " 'Geoscience',\n",
+       " 'Information science',\n",
+       " 'Life Sciences Communication',\n",
+       " 'Mathematics, Data Science',\n",
+       " 'Political Science',\n",
+       " 'Science: Biology/Life',\n",
+       " 'Science: Chemistry',\n",
+       " 'Science: Other',\n",
+       " 'Science: Other|Animal and Dairy Science',\n",
+       " 'Science: Other|Atmospheric & Oceanic Sciences',\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences',\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences (AOS)',\n",
+       " 'Science: Other|Atmospheric and oceanic science',\n",
+       " 'Science: Other|Biophysics PhD',\n",
+       " 'Science: Other|Environmental Science',\n",
+       " 'Science: Other|Environmental science',\n",
+       " 'Science: Other|Environmetal Science',\n",
+       " 'Science: Other|Geoscience',\n",
+       " 'Science: Other|Personal Finance',\n",
+       " 'Science: Other|Politcal Science',\n",
+       " 'Science: Other|Political Science',\n",
+       " 'Science: Other|Psychology',\n",
+       " 'Science: Other|Science: Genetics and Genomics',\n",
+       " 'Science: Other|animal sciences',\n",
+       " 'Science: Physics']"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# VERSION 1\n",
+    "# Be very careful: if you use sorted, make sure your return value \n",
+    "# variable matches with the variable for that project question\n",
+    "sorted_computer_and_science_majors = sorted(computer_and_science_majors)\n",
+    "sorted_computer_and_science_majors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c28e77ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Atmospheric Sciences',\n",
+       " 'Communication Sciences and Disorder',\n",
+       " 'Computer Science',\n",
+       " 'Computer Science and Statistics',\n",
+       " 'Data Science',\n",
+       " 'Engineering: Other|Computer Engineering',\n",
+       " 'Engineering: Other|Computer engineering',\n",
+       " 'Engineering: Other|Engineering: Computer',\n",
+       " 'Engineering: Other|Material Science Engineering',\n",
+       " 'Environmental science',\n",
+       " 'Geoscience',\n",
+       " 'Information science',\n",
+       " 'Life Sciences Communication',\n",
+       " 'Mathematics, Data Science',\n",
+       " 'Political Science',\n",
+       " 'Science: Biology/Life',\n",
+       " 'Science: Chemistry',\n",
+       " 'Science: Other',\n",
+       " 'Science: Other|Animal and Dairy Science',\n",
+       " 'Science: Other|Atmospheric & Oceanic Sciences',\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences',\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences (AOS)',\n",
+       " 'Science: Other|Atmospheric and oceanic science',\n",
+       " 'Science: Other|Biophysics PhD',\n",
+       " 'Science: Other|Environmental Science',\n",
+       " 'Science: Other|Environmental science',\n",
+       " 'Science: Other|Environmetal Science',\n",
+       " 'Science: Other|Geoscience',\n",
+       " 'Science: Other|Personal Finance',\n",
+       " 'Science: Other|Politcal Science',\n",
+       " 'Science: Other|Political Science',\n",
+       " 'Science: Other|Psychology',\n",
+       " 'Science: Other|Science: Genetics and Genomics',\n",
+       " 'Science: Other|animal sciences',\n",
+       " 'Science: Physics']"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# VERSION 2\n",
+    "computer_and_science_majors.sort()\n",
+    "computer_and_science_majors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e354b781",
+   "metadata": {},
+   "source": [
+    "### Order the `major` that contain **either** `\"Computer\"` **or** `\"Science\"` using descending order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "ca887135",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Science: Physics',\n",
+       " 'Science: Other|animal sciences',\n",
+       " 'Science: Other|Science: Genetics and Genomics',\n",
+       " 'Science: Other|Psychology',\n",
+       " 'Science: Other|Political Science',\n",
+       " 'Science: Other|Politcal Science',\n",
+       " 'Science: Other|Personal Finance',\n",
+       " 'Science: Other|Geoscience',\n",
+       " 'Science: Other|Environmetal Science',\n",
+       " 'Science: Other|Environmental science',\n",
+       " 'Science: Other|Environmental Science',\n",
+       " 'Science: Other|Biophysics PhD',\n",
+       " 'Science: Other|Atmospheric and oceanic science',\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences (AOS)',\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences',\n",
+       " 'Science: Other|Atmospheric & Oceanic Sciences',\n",
+       " 'Science: Other|Animal and Dairy Science',\n",
+       " 'Science: Other',\n",
+       " 'Science: Chemistry',\n",
+       " 'Science: Biology/Life',\n",
+       " 'Political Science',\n",
+       " 'Mathematics, Data Science',\n",
+       " 'Life Sciences Communication',\n",
+       " 'Information science',\n",
+       " 'Geoscience',\n",
+       " 'Environmental science',\n",
+       " 'Engineering: Other|Material Science Engineering',\n",
+       " 'Engineering: Other|Engineering: Computer',\n",
+       " 'Engineering: Other|Computer engineering',\n",
+       " 'Engineering: Other|Computer Engineering',\n",
+       " 'Data Science',\n",
+       " 'Computer Science and Statistics',\n",
+       " 'Computer Science',\n",
+       " 'Communication Sciences and Disorder',\n",
+       " 'Atmospheric Sciences']"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# VERSION 1\n",
+    "# Be very careful: if you use sorted, make sure your return value \n",
+    "# variable matches with the variable for that project question\n",
+    "reverse_sorted_computer_and_science_majors = sorted(computer_and_science_majors, reverse = True)\n",
+    "reverse_sorted_computer_and_science_majors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1606075f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Science: Physics',\n",
+       " 'Science: Other|animal sciences',\n",
+       " 'Science: Other|Science: Genetics and Genomics',\n",
+       " 'Science: Other|Psychology',\n",
+       " 'Science: Other|Political Science',\n",
+       " 'Science: Other|Politcal Science',\n",
+       " 'Science: Other|Personal Finance',\n",
+       " 'Science: Other|Geoscience',\n",
+       " 'Science: Other|Environmetal Science',\n",
+       " 'Science: Other|Environmental science',\n",
+       " 'Science: Other|Environmental Science',\n",
+       " 'Science: Other|Biophysics PhD',\n",
+       " 'Science: Other|Atmospheric and oceanic science',\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences (AOS)',\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences',\n",
+       " 'Science: Other|Atmospheric & Oceanic Sciences',\n",
+       " 'Science: Other|Animal and Dairy Science',\n",
+       " 'Science: Other',\n",
+       " 'Science: Chemistry',\n",
+       " 'Science: Biology/Life',\n",
+       " 'Political Science',\n",
+       " 'Mathematics, Data Science',\n",
+       " 'Life Sciences Communication',\n",
+       " 'Information science',\n",
+       " 'Geoscience',\n",
+       " 'Environmental science',\n",
+       " 'Engineering: Other|Material Science Engineering',\n",
+       " 'Engineering: Other|Engineering: Computer',\n",
+       " 'Engineering: Other|Computer engineering',\n",
+       " 'Engineering: Other|Computer Engineering',\n",
+       " 'Data Science',\n",
+       " 'Computer Science and Statistics',\n",
+       " 'Computer Science',\n",
+       " 'Communication Sciences and Disorder',\n",
+       " 'Atmospheric Sciences']"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# VERSION 2\n",
+    "computer_and_science_majors.sort(reverse = True)\n",
+    "computer_and_science_majors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c495a293",
+   "metadata": {},
+   "source": [
+    "### For `major` containing `\"other\"`, extract the details that come after `\"|\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "ab46c152",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Engineering: Computer',\n",
+       " 'Political Science',\n",
+       " 'Real Estate',\n",
+       " 'Engineering Physics: Scientific Computing',\n",
+       " 'Accounting',\n",
+       " 'business analytics',\n",
+       " 'animal sciences',\n",
+       " 'Science: Genetics and Genomics',\n",
+       " 'Business: Accounting',\n",
+       " 'Computer Engineering',\n",
+       " 'Computer engineering',\n",
+       " 'Material Science Engineering',\n",
+       " 'Environmental Science',\n",
+       " 'Chemical Engineering',\n",
+       " 'Biophysics PhD',\n",
+       " 'Technology Strategy/ Product Management',\n",
+       " 'Marketing',\n",
+       " 'Consumer Behavior and Marketplace Studies',\n",
+       " 'Psychology',\n",
+       " 'Civil and Environmental Engineering',\n",
+       " 'MHR',\n",
+       " 'Personal Finance',\n",
+       " 'Real Estate',\n",
+       " 'Environmental Science',\n",
+       " 'Psychology',\n",
+       " 'accounting',\n",
+       " 'Environmetal Science',\n",
+       " 'Atmospheric and Oceanic Sciences (AOS)',\n",
+       " 'Business Analytics',\n",
+       " 'Politcal Science',\n",
+       " 'Geoscience',\n",
+       " 'Marketing',\n",
+       " 'Atmospheric and oceanic science',\n",
+       " 'Environmental Science',\n",
+       " 'Marketing',\n",
+       " 'Engineering Mechanics',\n",
+       " 'Environmental science',\n",
+       " 'Atmospheric and Oceanic Sciences',\n",
+       " 'Civil- Intelligent Transportation System',\n",
+       " 'Animal and Dairy Science',\n",
+       " 'Atmospheric & Oceanic Sciences',\n",
+       " 'Accounting',\n",
+       " 'Environmental Science']"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "other_majors = find_majors(\"other\")\n",
+    "other_major_details = []\n",
+    "\n",
+    "for other in other_majors:\n",
+    "    details = other.split(\"|\")\n",
+    "    if len(details) > 1:\n",
+    "        other_major_details.append(details[1])\n",
+    "        \n",
+    "other_major_details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31a381fe",
+   "metadata": {},
+   "source": [
+    "## Self-practice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd43b7b3",
+   "metadata": {},
+   "source": [
+    "### Function: `find_fav_locations_within(lat_min, lat_max, long_min, long_max)` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "a403a92c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_fav_locations_within(lat_min, lat_max, long_min, long_max):\n",
+    "    \"\"\"\n",
+    "    find_prices_within(lat_min, lat_max, long_min, long_max) returns a nested list.\n",
+    "    First inner list contains latitudes of favourite places within the geographical \n",
+    "    location between and including\n",
+    "    the latitudes lat_min and lat_max and longitudes long_min and long_max.\n",
+    "    Second inner list contains longitudes of favourite places within the geographical \n",
+    "    location between and including\n",
+    "    the latitudes lat_min and lat_max and longitudes long_min and long_max.\n",
+    "    \"\"\"\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2699919",
+   "metadata": {},
+   "source": [
+    "### What are the favourite places within United States?\n",
+    "\n",
+    "```\n",
+    "top = 49.3457868 # north lat\n",
+    "bottom =  24.7433195 # south lat\n",
+    "left = -124.7844079 # west long\n",
+    "right = -66.9513812 # east long\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ac26620",
+   "metadata": {},
+   "source": [
+    "### How many students are both a procrastinator and a pet owner?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "172141ea",
+   "metadata": {},
+   "source": [
+    "### What percentage of 18-year-olds have their major declared as \"Other\"?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9a7a2b1",
+   "metadata": {},
+   "source": [
+    "### How old is the oldest basil/spinach-loving Business major?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fcc04f2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/f22/meena_lec_notes/lec-16/.ipynb_checkpoints/lec_16_list_practice_template-checkpoint.ipynb b/f22/meena_lec_notes/lec-16/.ipynb_checkpoints/lec_16_list_practice_template-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7f958a755ab04b451b20e851146c7405030da1eb
--- /dev/null
+++ b/f22/meena_lec_notes/lec-16/.ipynb_checkpoints/lec_16_list_practice_template-checkpoint.ipynb
@@ -0,0 +1,748 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "72348536",
+   "metadata": {},
+   "source": [
+    "# List Practice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d21a94b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8a434c",
+   "metadata": {},
+   "source": [
+    "### Warmup 1: min / max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "baa730ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_list = [45, -4, 66, 220, 10]\n",
+    "\n",
+    "min_val = None\n",
+    "for val in some_list:\n",
+    "    if min_val == None or val < min_val:\n",
+    "        min_val = val\n",
+    "    \n",
+    "print(min_val)\n",
+    "\n",
+    "max_val = None\n",
+    "for val in some_list:\n",
+    "    if max_val == None or val > max_val:\n",
+    "        max_val = val\n",
+    "    \n",
+    "print(max_val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3502c700",
+   "metadata": {},
+   "source": [
+    "### Warmup 2: median"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "414ae09e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def median(some_items):\n",
+    "    \"\"\"\n",
+    "    Returns median of a list passed as argument\n",
+    "    \"\"\"\n",
+    "    pass\n",
+    "    \n",
+    "nums = [5, 4, 3, 2, 1]\n",
+    "print(\"Median of\", nums, \"is\" , median(nums))\n",
+    "\n",
+    "nums = [6, 5, 4, 3, 2, 1]\n",
+    "print(\"Median of\", nums, \"is\" , median(nums))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73fa337e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vals = [\"A\", \"C\", \"B\"]\n",
+    "print(\"Median of\", nums, \"is\" , median(vals))\n",
+    "\n",
+    "vals = [\"A\", \"C\", \"B\", \"D\"]\n",
+    "# print(\"Median of\", nums, \"is\" , median(vals)) # does not work due to TypeError"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050fd57c",
+   "metadata": {},
+   "source": [
+    "### set data structure\n",
+    "\n",
+    "- **not a sequence**\n",
+    "- no ordering of values:\n",
+    "    - this implies that you can only store unique values within a `set`\n",
+    "- very helpful to find unique values stored in a `list`\n",
+    "    - easy to convert a `list` to `set` and vice-versa.\n",
+    "    - ordering is not guaranteed once we use `set`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d4a693f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_set = {10, 20, 30, 30, 40, 50, 10} # use a pair of curly braces to define it\n",
+    "some_set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "baef596c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_list = [10, 20, 30, 30, 40, 50, 10] # Initialize a list containing duplicate numbers\n",
+    "\n",
+    "# TODO: to find unique values, convert it into a set\n",
+    "print(set(some_list))\n",
+    "\n",
+    "# TODO: convert the set back into a list\n",
+    "print(list(set(some_list)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2be52d13",
+   "metadata": {},
+   "source": [
+    "Can you index / slice into a `set`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f622a5eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_set[1] # doesn't work - remember set has no order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e679d3a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_set[1:] # doesn't work - remember set has no order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d936c1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inspired by https://automatetheboringstuff.com/2e/chapter16/\n",
+    "def process_csv(filename):\n",
+    "    # open the file, its a text file utf-8\n",
+    "    example_file = open(filename, encoding=\"utf-8\")\n",
+    "    # prepare it for reading as a CSV object\n",
+    "    example_reader = csv.reader(example_file)\n",
+    "    # use the built-in list function to convert this into a list of lists\n",
+    "    example_data = list(example_reader)\n",
+    "    # close the file to tidy up our workspace\n",
+    "    example_file.close()\n",
+    "    # return the list of lists\n",
+    "    \n",
+    "    return example_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89621c98",
+   "metadata": {},
+   "source": [
+    "### Student Information Survey data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3c252b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: call the process_csv function and store the list of lists in cs220_csv\n",
+    "cs220_csv = process_csv(???)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5838ae5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Store the header row into cs220_header, using indexing\n",
+    "cs220_header = ???\n",
+    "cs220_header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66fda88d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Store all of the data rows into cs220_data, using slicing\n",
+    "cs220_data = ???\n",
+    "\n",
+    "# TODO: use slicing to display top 3 rows data\n",
+    "cs220_data???"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4267fe3e",
+   "metadata": {},
+   "source": [
+    "### What `Pizza topping` does the 13th student prefer? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b8dbe8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bad example: we hard-coded the column index\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f125240",
+   "metadata": {},
+   "source": [
+    "What if we decided to add a new column before sleeping habit? Your code will no longer work.\n",
+    "\n",
+    "Instead of hard-coding column index, you should use `index` method, to lookup column index from the header variable. This will also make your code so much readable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2e52e06",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d298a4c",
+   "metadata": {},
+   "source": [
+    "### What is the Lecture of the 4th student?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3617b3de",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "059de363",
+   "metadata": {},
+   "source": [
+    "### What **unique** `age` values are included in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45909f22",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e18663d",
+   "metadata": {},
+   "source": [
+    "### cell function\n",
+    "\n",
+    "- It would be very helpful to define a cell function, which can handle missing data and type conversions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bba90038",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cell(row_idx, col_name):\n",
+    "    \"\"\"\n",
+    "    Returns the data value (cell) corresponding to the row index and \n",
+    "    the column name of a CSV file.\n",
+    "    \"\"\"\n",
+    "    # TODO: get the index of col_name\n",
+    "    \n",
+    "    # TODO: get the value of cs220_data at the specified cell\n",
+    "    \n",
+    "    # TODO: handle missing values, by returning None\n",
+    "    \n",
+    "    # TODO: handle type conversions\n",
+    "    \n",
+    "    return val"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7c8e726",
+   "metadata": {},
+   "source": [
+    "### Function `avg_age_per_lecture(lecture)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa5598e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def avg_age_per_lecture(lecture):\n",
+    "    '''\n",
+    "    avg_age_per_lecture(lecture) returns the average age of \n",
+    "    the students in the given `lecture`; if there are no\n",
+    "    students in the given `lecture`, it returns `None`\n",
+    "    '''\n",
+    "    # To compute average you don't need to actually populate a list.\n",
+    "    # But here a list will come in handy. It will help you with the None return requirement.\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0a05e42",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "avg_age_per_lecture(\"LEC002\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f2c7e6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(avg_age_per_lecture(\"LEC007\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94548bf4",
+   "metadata": {},
+   "source": [
+    "### `sort` method versus `sorted` function\n",
+    "\n",
+    "- `sort` (and other list methods) have an impact on the original list\n",
+    "- `sorted` function returns a new list with expected ordering\n",
+    "- default sorting order is ascending / alphanumeric\n",
+    "- `reverse` parameter is applicable for both `sort` method and `sorted` function:\n",
+    "    - enables you to specify descending order by passing argument as `True`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1e555f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_list = [10, 4, 25, 2, -10] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "152297bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Invoke sort method\n",
+    "rv = ???\n",
+    "print(some_list)\n",
+    "\n",
+    "# What does the sort method return? \n",
+    "# TODO: Capture return value into a variable rv and print the return value.\n",
+    "print(rv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c0d5e7d",
+   "metadata": {},
+   "source": [
+    "`sort` method returns `None` because it sorts the values in the original list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c06d8976",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: invoke sorted function and pass some_list as argument\n",
+    "# TODO: capture return value into sorted_some_list\n",
+    "???\n",
+    "\n",
+    "# What does the sorted function return? \n",
+    "# It returns a brand new list with the values in sorted order\n",
+    "print(sorted_some_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ded0304c",
+   "metadata": {},
+   "source": [
+    "TODO: go back to `sort` method call and `sorted` function call and pass keyword argument `reverse = True`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35894ef5",
+   "metadata": {},
+   "source": [
+    "Can you call `sort` method on a set?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc08879e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_set.sort() \n",
+    "# doesn't work: no method named sort associated with type set\n",
+    "# you cannot sort a set because of the lack of ordering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99161c42",
+   "metadata": {},
+   "source": [
+    "Can you pass a `set` as argument to `sorted` function? Python is intelligent :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2549df29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# works because Python converts the set into a list and then sorts the list\n",
+    "sorted(some_set) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c7f3489",
+   "metadata": {},
+   "source": [
+    "### Function: `find_majors(phrase)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6adbfe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_majors(phrase):\n",
+    "    \"\"\"\n",
+    "    find_majors(phrase) returns a list of all the room names that contain the \n",
+    "    substring (case insensitive match) `phrase`.\n",
+    "    \"\"\"\n",
+    "    # TODO: initialize the target list here\n",
+    "    \n",
+    "    # TODO: iterate over row indices\n",
+    "    for row_idx in range(len(cs220_data)):\n",
+    "        major = cell(row_idx, \"Major\")\n",
+    "        \n",
+    "        # TODO: write the actual logic here\n",
+    "    \n",
+    "    return majors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b7f671f",
+   "metadata": {},
+   "source": [
+    "### Find all  `major` that contain **either** `\"Computer\"` **or** `\"Science\"`.\n",
+    "\n",
+    "Your output **must** be a *list*. The order **does not** matter, but if a `major` contains **both** `\"Computer\"` and `\"Science\"`, then the room must be included **only once** in your list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed895a3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "computer_majors = ???\n",
+    "science_majors = ???\n",
+    "\n",
+    "computer_and_science_majors = ???\n",
+    "# TODO: Now find just the unique values\n",
+    "computer_and_science_majors = ???\n",
+    "computer_and_science_majors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64fd0945",
+   "metadata": {},
+   "source": [
+    "### Order the `major` that contain **either** `\"Computer\"` **or** `\"Science\"` using ascending order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efcdf514",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# VERSION 1\n",
+    "# Be very careful: if you use sorted, make sure your return value \n",
+    "# variable matches with the variable for that project question\n",
+    "sorted_computer_and_science_majors = sorted(computer_and_science_majors)\n",
+    "sorted_computer_and_science_majors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c28e77ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# VERSION 2\n",
+    "computer_and_science_majors.sort()\n",
+    "computer_and_science_majors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e354b781",
+   "metadata": {},
+   "source": [
+    "### Order the `major` that contain **either** `\"Computer\"` **or** `\"Science\"` using descending order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca887135",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# VERSION 1\n",
+    "# Be very careful: if you use sorted, make sure your return value \n",
+    "# variable matches with the variable for that project question\n",
+    "reverse_sorted_computer_and_science_majors = sorted(computer_and_science_majors, reverse = ???)\n",
+    "reverse_sorted_computer_and_science_majors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6c61532",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# VERSION 2\n",
+    "computer_and_science_majors.sort(reverse = ???)\n",
+    "computer_and_science_majors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2862160c",
+   "metadata": {},
+   "source": [
+    "### For `major` containing `\"other\"`, extract the details that come after `\"|\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "600fae6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_majors = find_majors(\"other\")\n",
+    "other_major_details = []\n",
+    "\n",
+    "for other in other_majors:\n",
+    "    print(other)\n",
+    "    \n",
+    "    # TODO: complete the rest of the logic\n",
+    "        \n",
+    "other_major_details"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31a381fe",
+   "metadata": {},
+   "source": [
+    "## Self-practice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe5b9303",
+   "metadata": {},
+   "source": [
+    "### Function: `find_fav_locations_within(lat_min, lat_max, long_min, long_max)` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8443ad2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_fav_locations_within(lat_min, lat_max, long_min, long_max):\n",
+    "    \"\"\"\n",
+    "    find_prices_within(lat_min, lat_max, long_min, long_max) returns a nested list.\n",
+    "    First inner list contains latitudes of favourite places within the geographical \n",
+    "    location between and including\n",
+    "    the latitudes lat_min and lat_max and longitudes long_min and long_max.\n",
+    "    Second inner list contains longitudes of favourite places within the geographical \n",
+    "    location between and including\n",
+    "    the latitudes lat_min and lat_max and longitudes long_min and long_max.\n",
+    "    \"\"\"\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e0d63eb",
+   "metadata": {},
+   "source": [
+    "### What are the favourite places within United States?\n",
+    "\n",
+    "```\n",
+    "top = 49.3457868 # north lat\n",
+    "bottom =  24.7433195 # south lat\n",
+    "left = -124.7844079 # west long\n",
+    "right = -66.9513812 # east long\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ac26620",
+   "metadata": {},
+   "source": [
+    "### How many students are both a procrastinator and a pet owner?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "172141ea",
+   "metadata": {},
+   "source": [
+    "### What percentage of 18-year-olds have their major declared as \"Other\"?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9a7a2b1",
+   "metadata": {},
+   "source": [
+    "### How old is the oldest basil/spinach-loving Business major?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fcc04f2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/f22/meena_lec_notes/lec-16/lec_16_list_practice.ipynb b/f22/meena_lec_notes/lec-16/lec_16_list_practice.ipynb
index fa9aa93a1fa39ca707810f319e3499dc6ea2d339..e8eb15aa9e53b941ea4738b88eb7eb5558f75c82 100644
--- a/f22/meena_lec_notes/lec-16/lec_16_list_practice.ipynb
+++ b/f22/meena_lec_notes/lec-16/lec_16_list_practice.ipynb
@@ -791,41 +791,41 @@
     {
      "data": {
       "text/plain": [
-       "['Science: Other|Animal and Dairy Science',\n",
-       " 'Mathematics, Data Science',\n",
-       " 'Science: Physics',\n",
-       " 'Science: Other|Biophysics PhD',\n",
+       "['Political Science',\n",
+       " 'Science: Other|Atmospheric and oceanic science',\n",
+       " 'Science: Other|Animal and Dairy Science',\n",
        " 'Science: Other|Environmetal Science',\n",
-       " 'Science: Other|Environmental Science',\n",
        " 'Science: Other|Geoscience',\n",
-       " 'Data Science',\n",
-       " 'Science: Other|Political Science',\n",
-       " 'Science: Other|Atmospheric and oceanic science',\n",
-       " 'Geoscience',\n",
-       " 'Atmospheric Sciences',\n",
-       " 'Communication Sciences and Disorder',\n",
-       " 'Computer Science',\n",
-       " 'Engineering: Other|Computer engineering',\n",
-       " 'Engineering: Other|Material Science Engineering',\n",
-       " 'Science: Other|Politcal Science',\n",
+       " 'Computer Science and Statistics',\n",
        " 'Science: Other|animal sciences',\n",
        " 'Science: Chemistry',\n",
-       " 'Computer Science and Statistics',\n",
-       " 'Science: Other|Atmospheric & Oceanic Sciences',\n",
-       " 'Science: Other|Atmospheric and Oceanic Sciences',\n",
-       " 'Political Science',\n",
+       " 'Environmental science',\n",
        " 'Life Sciences Communication',\n",
+       " 'Communication Sciences and Disorder',\n",
+       " 'Science: Other|Atmospheric & Oceanic Sciences',\n",
        " 'Science: Biology/Life',\n",
+       " 'Geoscience',\n",
+       " 'Science: Other|Political Science',\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences (AOS)',\n",
+       " 'Atmospheric Sciences',\n",
+       " 'Data Science',\n",
+       " 'Mathematics, Data Science',\n",
+       " 'Engineering: Other|Material Science Engineering',\n",
        " 'Science: Other|Personal Finance',\n",
+       " 'Science: Other|Environmental Science',\n",
+       " 'Information science',\n",
+       " 'Science: Other',\n",
+       " 'Science: Other|Environmental science',\n",
        " 'Engineering: Other|Computer Engineering',\n",
        " 'Science: Other|Psychology',\n",
-       " 'Science: Other',\n",
+       " 'Engineering: Other|Engineering: Computer',\n",
+       " 'Computer Science',\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences',\n",
+       " 'Science: Other|Politcal Science',\n",
        " 'Science: Other|Science: Genetics and Genomics',\n",
-       " 'Science: Other|Environmental science',\n",
-       " 'Science: Other|Atmospheric and Oceanic Sciences (AOS)',\n",
-       " 'Information science',\n",
-       " 'Environmental science',\n",
-       " 'Engineering: Other|Engineering: Computer']"
+       " 'Engineering: Other|Computer engineering',\n",
+       " 'Science: Physics',\n",
+       " 'Science: Other|Biophysics PhD']"
       ]
      },
      "execution_count": 27,
@@ -1188,7 +1188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 33,
    "id": "a403a92c",
    "metadata": {},
    "outputs": [],
diff --git a/f22/meena_lec_notes/lec18_dictionaries2.ipynb b/f22/meena_lec_notes/lec18_dictionaries2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4e0ce2a91967b47c890ad5a7fffdd1ce0e8019f3
--- /dev/null
+++ b/f22/meena_lec_notes/lec18_dictionaries2.ipynb
@@ -0,0 +1,12013 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dictionaries 2 - Combining Dictionaries and Lists (nested data structures)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 1: Answer these questions about dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Keys can be what type? :     Any type that is ____immutable____________\n",
+    "# Values can be what type? :   any type (including other dictionaries)\n",
+    "# Indexing? .... yes/no        No\n",
+    "# Slicing? ..... yes/no        No\n",
+    "# Mutable?......yes/no         Yes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inspired by https://automatetheboringstuff.com/2e/chapter16/\n",
+    "def process_csv(filename):\n",
+    "    exampleFile = open(filename, encoding=\"utf-8\")  \n",
+    "    exampleReader = csv.reader(exampleFile) \n",
+    "    exampleData = list(exampleReader)        \n",
+    "    exampleFile.close()  \n",
+    "    return exampleData\n",
+    "\n",
+    "survey_data = process_csv('cs220_survey_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 2a: Split csv data into header and data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Lecture',\n",
+       " 'Age',\n",
+       " 'Major',\n",
+       " 'Zip Code',\n",
+       " 'Latitude',\n",
+       " 'Longitude',\n",
+       " 'Pizza topping',\n",
+       " 'Pet preference',\n",
+       " 'Runner',\n",
+       " 'Sleep habit',\n",
+       " 'Procrastinator']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cs220_header = survey_data[0]\n",
+    "cs220_data = survey_data[1:]\n",
+    "cs220_header"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 2b: Display the first 3 data rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['LEC001',\n",
+       "  '22',\n",
+       "  'Engineering: Biomedical',\n",
+       "  '53703',\n",
+       "  '43.073051',\n",
+       "  '-89.40123',\n",
+       "  'none (just cheese)',\n",
+       "  'neither',\n",
+       "  'No',\n",
+       "  'no preference',\n",
+       "  'Maybe'],\n",
+       " ['LEC006',\n",
+       "  '',\n",
+       "  'Undecided',\n",
+       "  '53706',\n",
+       "  '43.073051',\n",
+       "  '-89.40123',\n",
+       "  'none (just cheese)',\n",
+       "  'neither',\n",
+       "  'No',\n",
+       "  'no preference',\n",
+       "  'Maybe'],\n",
+       " ['LEC004',\n",
+       "  '18',\n",
+       "  'Engineering: Industrial',\n",
+       "  '53715',\n",
+       "  '43.073051',\n",
+       "  '-89.40123',\n",
+       "  'none (just cheese)',\n",
+       "  'neither',\n",
+       "  'No',\n",
+       "  'no preference',\n",
+       "  'Maybe']]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cs220_data[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cell(data, header, row_idx, col_name):\n",
+    "    \"\"\"\n",
+    "    Returns the data value (cell) corresponding to the row index and \n",
+    "    the column name of a CSV file.\n",
+    "    \"\"\"\n",
+    "    col_idx = header.index(col_name) \n",
+    "    val = data[row_idx][col_idx]  \n",
+    "    \n",
+    "    # handle missing values, by returning None\n",
+    "    if val == '':\n",
+    "        return None\n",
+    "    \n",
+    "    # handle type conversions\n",
+    "    if col_name in [\"Age\", 'Zip Code',]:\n",
+    "        return int(val)\n",
+    "    elif col_name in ['Latitude', 'Longitude']:\n",
+    "        return float(val)\n",
+    "    \n",
+    "    return val"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 3: Make a dictionary of frequency of `Major`\n",
+    "\n",
+    "- Initialize empty `dict` into a variable called `major_freq`\n",
+    "- Iterate over the data:\n",
+    "    - Extract required column's data\n",
+    "    - Make sure to handle missing data\n",
+    "    - Check if current value of the column is a key in your `dict`:\n",
+    "        - yes, update the count\n",
+    "        - no, insert new key-value pair"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Engineering: Biomedical': 45,\n",
+       " 'Undecided': 23,\n",
+       " 'Engineering: Industrial': 58,\n",
+       " 'Engineering: Other|Engineering: Computer': 1,\n",
+       " 'Data Science': 164,\n",
+       " 'Mathematics/AMEP': 34,\n",
+       " 'Engineering: Other': 13,\n",
+       " 'Economics': 53,\n",
+       " 'Psychology': 7,\n",
+       " 'Science: Biology/Life': 37,\n",
+       " 'Engineering: Mechanical': 198,\n",
+       " 'Economics (Mathematical Emphasis)': 7,\n",
+       " 'Computer Science': 115,\n",
+       " 'Science: Other|Political Science': 1,\n",
+       " 'Business: Other': 11,\n",
+       " 'Business: Other|Real Estate': 2,\n",
+       " 'Engineering: Other|Engineering Physics: Scientific Computing': 1,\n",
+       " 'Business: Finance': 30,\n",
+       " 'Business: Information Systems': 24,\n",
+       " 'Statistics': 26,\n",
+       " 'Business: Actuarial': 22,\n",
+       " 'Science: Physics': 8,\n",
+       " 'Science: Other': 9,\n",
+       " 'Business: Other|Accounting': 2,\n",
+       " 'Business: Other|business analytics': 1,\n",
+       " 'Science: Other|animal sciences': 1,\n",
+       " 'Mathematics': 2,\n",
+       " 'Health Promotion and Health Equity': 2,\n",
+       " 'Art': 1,\n",
+       " 'Mathematics, Data Science': 1,\n",
+       " 'Science: Other|Science: Genetics and Genomics': 1,\n",
+       " 'Statistics (actuarial route)': 1,\n",
+       " 'Business: Other|Business: Accounting': 1,\n",
+       " 'Engineering: Other|Computer Engineering': 1,\n",
+       " 'Engineering: Other|Computer engineering': 1,\n",
+       " 'Engineering: Other|Material Science Engineering': 1,\n",
+       " 'Civil engineering - hydropower engineering': 1,\n",
+       " 'Science: Chemistry': 6,\n",
+       " 'Communication arts': 1,\n",
+       " 'Business andministration': 1,\n",
+       " 'Education': 2,\n",
+       " 'Pre-business': 1,\n",
+       " 'Science: Other|Environmental Science': 4,\n",
+       " 'History': 2,\n",
+       " 'Information science': 2,\n",
+       " 'consumer behavior and marketplace studies': 1,\n",
+       " 'Conservation Biology': 1,\n",
+       " 'Engineering: Other|Chemical Engineering': 1,\n",
+       " 'Science: Other|Biophysics PhD': 1,\n",
+       " 'Business: Other|Technology Strategy/ Product Management': 1,\n",
+       " 'Political Science': 6,\n",
+       " 'Graphic Design': 1,\n",
+       " 'Business: Other|Marketing': 3,\n",
+       " 'Cartography and GIS': 1,\n",
+       " 'Sociology': 2,\n",
+       " 'Business: Other|Consumer Behavior and Marketplace Studies': 1,\n",
+       " 'Atmospheric Sciences': 1,\n",
+       " 'Languages': 4,\n",
+       " 'Engineering Mechanics (Aerospace Engineering)': 1,\n",
+       " 'Science: Other|Psychology': 2,\n",
+       " 'Engineering: Other|Civil and Environmental Engineering': 1,\n",
+       " 'International Studies': 2,\n",
+       " 'Agricultural and Applied Economics': 1,\n",
+       " 'Business: Other|MHR': 1,\n",
+       " 'Medicine': 1,\n",
+       " 'Science: Other|Personal Finance': 1,\n",
+       " 'Environmental science': 1,\n",
+       " 'Geoscience': 1,\n",
+       " 'Business: Other|accounting': 1,\n",
+       " 'Design Studies': 1,\n",
+       " 'Science: Other|Environmetal Science': 1,\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences (AOS)': 1,\n",
+       " 'Business: Other|Business Analytics': 1,\n",
+       " 'Journalism': 2,\n",
+       " 'Science: Other|Politcal Science': 1,\n",
+       " 'Communication Sciences and Disorder': 1,\n",
+       " 'Science: Other|Geoscience': 1,\n",
+       " 'Science: Other|Atmospheric and oceanic science': 1,\n",
+       " 'Engineering: Other|Engineering Mechanics': 1,\n",
+       " 'Pre-Business': 1,\n",
+       " 'Industrial Engineering': 1,\n",
+       " 'Mechanical Engineering': 1,\n",
+       " 'Science: Other|Environmental science': 1,\n",
+       " 'Life Sciences Communication': 1,\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences': 1,\n",
+       " 'Rehabilitation Psychology': 1,\n",
+       " 'Accounting': 1,\n",
+       " 'Engineering: Other|Civil- Intelligent Transportation System': 1,\n",
+       " 'Science: Other|Animal and Dairy Science': 1,\n",
+       " 'Interior Architecture': 1,\n",
+       " 'Science: Other|Atmospheric & Oceanic Sciences': 1,\n",
+       " 'Computer Science and Statistics': 1,\n",
+       " 'Business analytics': 1,\n",
+       " 'Legal Studies': 1,\n",
+       " 'Journalism: Strategic Comm./Advertising': 1,\n",
+       " 'Master of Public Affairs': 1,\n",
+       " 'Environment & Resources': 1,\n",
+       " 'Environmental Studies': 1}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TODO: iterate over each student's data from cs220_data\n",
+    "# TODO: extract \"Major\" column's value \n",
+    "# TODO: check if current student's major already a key in major_freq\n",
+    "#            - if yes, increase the corresponding value by 1\n",
+    "#            - if no, insert a new key-value pair\n",
+    "\n",
+    "major_freq = {} # KEY: unique major; VALUE: count of unique major\n",
+    "\n",
+    "for row in cs220_data:\n",
+    "    major = row[cs220_header.index(\"Major\")]\n",
+    "    if major in major_freq:\n",
+    "        major_freq[major] += 1\n",
+    "    else:\n",
+    "        major_freq[major] = 1\n",
+    "    \n",
+    "major_freq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is the most common `Major` among CS220 / CS319 students?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The major \"Engineering: Mechanical\" appeared 198 times.\n"
+     ]
+    }
+   ],
+   "source": [
+    "most_used_key = None  \n",
+    "max_value = None\n",
+    "\n",
+    "for major in major_freq:\n",
+    "    if max_value == None or major_freq[major] > max_value:\n",
+    "        max_value = major_freq[major]\n",
+    "        most_used_key = major\n",
+    "\n",
+    "print(\"The major \\\"{}\\\" appeared {} times.\".format(str(most_used_key), max_value))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning Objectives:\n",
+    " - Handle key errors with get and pop using default values\n",
+    " - Understand the idea of nesting data structures\n",
+    " - Use a dictionary of lists to put rows of data into \"buckets\"\n",
+    " - Use a list of dictionaries to represent a table of data.\n",
+    " - Create a dictionary of dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Default values with `get` and `pop` methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "suffix = {1: \"st\", 2: 'nd', 3: \"rd\"}\n",
+    "suffix.get(1)\n",
+    "\n",
+    "# TODO: what happens when you try to get a key that is not there? Try it.\n",
+    "print(suffix.get(10)) # Returns None\n",
+    "\n",
+    "# TODO: what happens whey you try to pop a key that is not there? Try it.\n",
+    "# suffix.pop(10) # KeyError"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`get` and `pop` methods accept a second argument, which will be the default value if the first argument (key) does not exist.\n",
+    "\n",
+    "Syntax:\n",
+    "- `some_dict.get(some_key, default_value)`\n",
+    "- `some_dict.pop(some_key, default_value)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rd\n",
+      "th\n",
+      "th\n",
+      "nd\n",
+      "{1: 'st', 3: 'rd'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get(key, default value) \n",
+    "print(suffix.get(3, 'th'))\n",
+    "print(suffix.get(5, 'th')) #default value, but does not add the key-value pair to the dict\n",
+    "\n",
+    "# pop(key, default value)\n",
+    "print(suffix.pop(7, 'th')) # no key-value pair to remove\n",
+    "print(suffix.pop(2, 'th'))\n",
+    "print(suffix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What are nested data structures?\n",
+    "A data structure containing another data structure as item is called as nest data structure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Nesting part 1: Bucketizing/Binning"
+   ]
+  },
+  {
+   "attachments": {
+    "Buckets.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Buckets.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "attachments": {
+    "Binning_step1.png": {
+     "image/png": 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peGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6VXNlckNvbW1lbnQ+U2NyZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+CptmxDwAAAAcaURPVAAAAAIAAAAAAAABbwAAACgAAAFvAAABbwAAbXUtIuMEAABAAElEQVR4AezdB7xT5f3H8d+d7L2HbFDZCDIVUFBRrAUURaXubeuirmqFtrbuat0469ai4P4jbhQBwYkiMpWN7M3d//M7kNzMc3OTJ8lJ8jl93SY54znPeZ9Ebr73GVll1iIsCCCAAAIIIIAAAggggAACCCCAAAJxF8giiIm7MSdAAAEEEEAAAQQQQAABBBBAAAEEbAGCGN4ICCCAAAIIIIAAAggggAACCCCAQIIECGISBM1pEEAAAQQQQAABBBBAAAEEEEAAAYIY3gMIIIAAAggggAACCCCAAAIIIIBAggQIYhIEzWkQQAABBBBAAAEEEEAAAQQQQAABghjeAwgggAACCCCAAAIIIIAAAggggECCBAhiEgTNaRBAAAEEEEAAAQQQQAABBBBAAAGCGN4DCCCAAAIIIIAAAggggAACCCCAQIIECGISBM1pEEAAAQQQQAABBBBAAAEEEEAAAYIY3gMIIIAAAggggAACCCCAAAIIIIBAggQIYhIEzWkQQAABBBBAAAEEEEAAAQQQQAABghjeAwgggAACCCCAAAIIIIAAAggggECCBAhiEgTNaRBAAAEEEEAAAQQQQAABBBBAAAGCGN4DCCCAAAIIIIAAAggggAACCCCAQIIECGISBM1pEEAAAQQQQAABBBBAAAEEEEAAAYIY3gMIIIAAAggggAACCCCAAAIIIIBAggQIYhIEzWkQQAABBBBAAAEEEEAAAQQQQAABghjeAwgggAACCCCAAAIIIIAAAggggECCBAhiEgTNaRBAAAEEEEAAAQQQQAABBBBAAAGCGN4DCCCAAAIIIIAAAggggAACCCCAQIIECGISBM1pEIhVYPOeMpm7plS+31Aqv2wvkw27ymRPkUhxWawlZ+7xuVki1fNEmtTMkjZ1sqR7k2zp1yJbGlS3NrAggAACCCCAAAIIpKzAhg0b5IsvvpDPPvtMrrvuOmnatGnKXgsVTz8Bgpj0u6dcUZoJfLu+VF75sUTmryuVUkKXuN/dbCuD6dMsW07rkiM9m2bH/XycAAEEEEAAAQQQQCB2gV9//dUOXWbOnGk/Llq0yFvoK6+8Iqeeeqr3NU8QSLYAQUyy7wDnRyCMwOodZXL/l8XylRXAsCRHoLcVyFzRN1da1qaFTHLuAGdFAAEEEEAAAQSCBcrKymThwoV24KItXj755BNZu3Zt8I4H1vzxj3+UBx54IOx2NiCQaAGCmESLcz4EIhCYvrTEDmEKSiLYmV3iKlAlR+wwZkQH6wkLAggggAACCCCAQMIFioqK5JtvvvFr8bJ169aI69G9e3f57rvvIt6fHRGItwBBTLyFKR+BSgo8+U2xvPgDCUwl2eK++xldc+T8XrlxPw8nQAABBBBAAAEEMl1g9+7dMnfuXG+LFx3rZe/evVGzZGdny5YtW6ROnTpRl8GBCJgUIIgxqUlZCMQoQAgTI2CcDyeMiTMwxSOAAAIIIIBARgps2rRJZs+e7W3x8tVXX0lxcbFRi7fffltGjhxptEwKQyBaAYKYaOU4DgHDAtod6a7ZZv/BMVxFirMErh2QK3RT4q2AAAIIIIAAAghEL7B69Wpvaxcd4+XHH38UHfclnsv1118vt99+ezxPQdkIRCxAEBMxFTsiED8BHZj3orcLhTFh4mdsqmQdM+axE/MZwNcUKOUggAACCCCAQNoL/Pzzz97gRQfWXblyZcKveeDAgTJr1qyEn5cTIhBKgCAmlArrEEiwwHUfFDE7UoLNYzmdzqZ05/C8WIrgWAQQQAABBBBAIC0FtEuRDoyrLV30R6eT1q5HyV7y8/Nl+/btUrVq1WRXhfMjIAQxvAkQSLLAN9b01H+2ghiW1BK455g86dk0O7UqTW0RQAABBBBAAAHDAjqI7rx587zBi7Y62bVrl+GzmClOW+MMGTLETGGUgkAMAgQxMeBxKAImBG74sEjmrS01URRlJFDg8ObZcvswWsUkkJxTIYAAAggggIALBLZt2yY6i5GnxcuXX34pOr10Kiz/+Mc/5Oabb06FqlLHNBcgiEnzG8zluVtg854yGTe1UErjOzaZuxFStHbZWSIvj8mXBtWtJywIIIAAAgjEKKBfbt944w1Zt26d1K1bVw455BDp379/hd0otDWCDnyqP5s3b7YHPK1Xr5506NBB2rRpE2OtOBwBkbVr19pjq3iCF+12FO+BdePlfswxx8iMGTPiVTzlIhCxAEFMxFTsiIB5gXeXlMg9c5gpybxsYkqc0D9XTuhojd7LggACCCCAQAwCOm5F7969ZdmyZX6l6FgWffr0kVatWomOb6GtDnbu3GkHLhs3bpTffvtNNMAJtzRs2FAGDx4sQ4cOtX+6du0qWVn8ASGcF+v3CyxdutTb2kXDF32dLkutWrVk69atkpPD72/pck9T9ToIYlL1zlHvtBC4/fMieX8F3ZJS9WYe0zZbbjiC7kmpev+oNwIIIOAWgWnTpsmYMWPiXp0GDRrYwYyOkaHhTLdu3SQ7m/HO4g7v4hOUlpbKggULvMHLp59+Khs2bHBxjWOvmo5nowEnCwLJFCCISaY+5854gUveKZQlW+iXlKpvhI71s+TRkfmpWn3qjQACCCDgEoF3331XRo4cmfDaaBcmbTHjCWZ69OhBMJPwu5DYExYUFMj8+fO9wcvnn38uO3bsSGwlkny2f//733L11VcnuRacPtMFCGIy/R3A9SdVYPQrBbKjMKlV4OQxCNS2Mphpp1WJoQQORQABBBBAQOypfZs0aSLaOiGZi45Nc+SRR3qDmZ49e9KFI5k3xMC5NWSZM2eON3jR5xrGZPIyevRomTp1aiYTcO0uECCIccFNoAqZK3Dc8wVSTIOYlH0D5Frd7N8bTxCTsjeQiiOAAAIuEhgxYoS89957LqqRSJ06deSII46wuzFpV6ZevXoRzLjqDgVXRscN0lYunoF1v/32WykpKQneMYPXNGrUyB5fKYMJuHQXCBDEuOAmUIXMFRj2XGb/RSId7vyHfyCISYf7yDUggAACyRbQL8/aGsXNS+3ateXoo4+WsWPHyqhRo6R69epurm5G1O2XX37xhi4avixatCgjrjvWi/zpp5/smcliLYfjEYhWgCAmWjmOQ8CAAEGMAcQkF0EQk+QbwOkRQACBNBK4/PLL5eGHH06JK9KBfy+99FK56qqrRJ+zxF9Ap4xeuHChzJw50w5f9HHNmjXxP3EanuGxxx6TCy+8MA2vjEtKFQGCmFS5U9QzLQUIYlL/thLEpP495AoQQAABtwgUFxfL+PHj5ZVXXnFLlSqsh7aSufbaa+Waa66hhUyFWpXbQacr//rrr/1avOjUyyyxC+jn7Lnnnou9IEpAIEoBgpgo4TgscoEdBWXyyo8lcl7PXMlhhkQ/OIIYP46UfJHOQQyf3ZR8S1JpBBBIcQEdsPf222+XSZMmiX4RT5WlZcuWcscdd8gZZ5yRKlV2XT13794tc+fO9bZ40YF19+zZ47p6pkOFWrduLdqtiwWBZAkQxCRLPoPOu31fmYyZUig9m2TLzUfmSr1q1ginLLYAQUzqvxHSOYjhs5v670+uAAEEUlfg559/lhtuuEHeeOMN0S4pqbLoODcPPfSQdOvWLVWqnLR6bt68WWbNmuVt8fLVV1+JtopiSYzAqlWrRANEFgSSIUAQkwz1DDun58ucXnbDaiITh+RJ50Y0jVEPghhVSO0lE4IYvUN8dlP7fUrtEUAgdQV0UNGnn35aXn75ZdEvjqmw5OTkyB//+Ef5+9//Ltp1iWW/gN4/HZTZM8aLjveSSiFbut3HF154gRZc6XZTU+h6MiqI0X/ECgsLg27PsGHDpEOHDt71ke7nPYAnjgK+QYzuqFP+Xn54rpx0cI7jcZmwkSAm9e9ypgQxeqf47Kb++5UrQACB1BXQL+xffPGFTJ8+3f4iP3/+fNd3W2natKnceeed9rg3WVmZ1yJaZzDyTCOtj3SFcdfn75JLLpFHHnnEXZWiNhkjkFFBjKbz2u82cJk8ebJcdNFF3tWR7uc9gCeOAoFBjGfn4W2z5er+eVI117Mm8x4JYlL/nmdSEOO5W3x2PRI8IoAAAskT0N9plyxZYk9XrF/wf/jhB5kyZYps3749eZUKc+b+/fvLAw88IH369AmzR+qvLikpkW+//dY7m5EGL5s2bUr9C0vjK+jSpYv9uUnjS+TSXCxAEGPdHIKY+L5DwwUxetZ2dbPkb0PzpHmtzPsriV4/QYwqpPaSiUGM3rFM/+ym9ruW2iOAQLoK7N27V1599VV56qmn5NNPP3VVtxdtEaMD+f7zn/8UHSg11Re1/vLLL70tXrS10q5du1L9sjKq/vqe1LCsfv36GXXdXKw7BAhirPtAEBPfN6NTEKNnrpkncuMRedK/ZeaNG5MKQUzVfJEe1r3pVC9Lqlmtl6pZ/VNW7yqT95aUyC4G8pdMDWIy/bMb3/9qUjoCCCAQu8Dy5cvtsWX++9//yurVq2Mv0FAJVapUkcsuu0yuv/56adKkiaFS41/Mtm3b7K5h2tJFx3jRrmGhhjyIf004g0kBHQz7pJNOMlkkZSEQkQBBjMVEEBPReyXqnSoKYrRgbQ8zvluOnNUjV7IzqHGM24OYTs2z5O8D86RRiJmubv6iSGYvC+7qF/UbJUUPzOQgRm9Zpn52U/TtSrURQCADBbQL04wZM+xWMvql0y3hQbVq1UTH6LjmmmtcOXPNunXrvK1dNHxZsGBByCEOMvAtlVaXPGHCBLn77rvT6pq4mNQQIIix7hNBTHzfrJEEMZ4a9GmWLTdZU1zXrpIZaYybg5gWDbPkmRH5Em5sPYKY/e/aTA9iMvWz67luHhFAAIFUEtDpkp9//nm55ZZbZMeOHa6oem5urowdO1auvPJK6devX9LqtHTpUm/woi1eli1blrS6cOLECfTt21fmzp2buBNyJgQOCKRMELN79267GeCKFSvkt99+s/vz6aC62qevR48eoh+i5s2bO97YSAfhjXQ/x5Ox0StQmSBGD2pSI0smWVNcd2qQ/mGMW4MYlb/3+Dzp1jB8dzGCmP1vcYKY/Q76/5n02S2/ap4hgAACqSfwr3/9S2666SbXVfzQQw+Vs88+2x5L5qCDDopb/bSV0Pfff+8NXrTFy/r16+N2Pgp2r4AGgdrtrEaNGu6tJDVLSwFXBzE6TZ8OOKYtVvQ/kE5NKXWwpVNOOUX0Hxbfqah971qkAUuk+/mWzfPwApUNYrSkPOv7/1X9cmVEh/Se4tqtQUwTazyYF0+0BofxWfYWi7y/skRmryu1Z7qav7JU9uzz2SFDnxLE+N/4TPns+l81rxBAAIHUEtizZ4+0b9/e1eGD/qH1hBNOkGOPPVZ69+4ttWrVihq5oKDAHtNFv0/oz6xZs1w5u1TUF8iBMQl88MEHMmzYsJjK4GAEKivg2iBG+2Gef/75Mm/evEpdk7aK0WNCtY6JNGCJdL9KVSyDd44miPFwnWAFMVf0zZW8NM1j3BrE9GqdJXcP9g9i/vhRofy0psxza3g8IEAQE/qtkC6fXQ35WRBAAIFUENA/YEa66Iw/Oj7Ls88+G+khSd1P/1t88MEH263gW7VqJS1atJBmzZqJDvyr2/RHwyWdAWflypUycOBAqVq1qrfFi85utG8ffz1K6k108cknTpwokyZNcnENqVo6Crg2iLnvvvvk6quvjsp8yJAh8sknnwQdG2nAEul+QSdgRUiBWIIYLbBT/f1dlZrUTL8vRG4NYk7qli1X9rSmszqwlFq/2x33QoHoI4u/AEGMv4fvq3T47BLE+N5RniOAgJsFKgpiSkpK5MMPP5QXXnhBpk2bJjt37nTz5cRUt+zsbAbWjUkwsw4++uij7c9GZl01V5tsgZQKYho3bizdunWTBg0aiE7Jp9PGhVr0P77bt2+XmjVr+m2ONGCJdD+/wnkRViDWIEYLrm01zrjpyDzp0zz8mCVhK+DiDW4LYqpYznWqZ9kzWI1sU94MqahE5Kx3C23JzTvKpCTEZEl1rRbDLetmSYMDMyztsnbfYE1zvWZzmYT7I10tqztutbzygG3b7jIpLNp/w9o3zZJmVvhWaJ1r4fpS106VTRDj/AFL9c8uQYzz/WUrAgi4RyBcEKOtQTR8eeWVV2TDhg3uqTA1QcAlAtWrV7fHicnLK/8jpEuqRjXSWCAlgpju3bvLbbfdJscdd5xoSOJZ/vSnP8mDDz7oeen3qKOdH3nkkX7rIg1YIt3Pr3BehBUwEcRo4fp1/dyeuXJG15ywM/mErYRLN7gtiLmwX46M65TrqHXL7CKZtbQ8ieneKkvO65IbdmDfjXvL5LWlJTL1+5KgAOe/o/PlIJ+WTk8tLJb/fVcik47Klf5Nyz/rxdbpHl1QLG8sKHFdqxyCGMe3i70xlT+7vkHM8steqPhi2QMBBBBIoEC7h8/0ns03iFm8eLG8+OKLdgCjswGxIICAs8CcOXOSOmuXc+3Ymo4Crg9iNGy566677D6ggTfgnXfekRNPPDFwtf36rbfeCtoWacAS6X4hT8zKIAFTQYyn4AEts+XGQXlSw38IE8/mlHp0WxBzkRXEnFZBEDPRCmI+t4KYbOvb9Z8G5spJ7coDEyf8FTtK5bqPi2SLz2yZz1hBTEufIOaZn4qlbe0sGdwidJkXvl8oy9e7q38UQYzTXfffloqfXYIY/3vIKwQQcJeAbxCzbt06efnll+3wJVyrcXfVntog4B6BO++8U6699lr3VIiapL2Aa4MY7cOqA4kFBi06c5Jue+2112TKlCmyY4fPtzqf20UQ44OR5Kemgxi9nBa19o8b086a3SeVl1QOYi6wQpvTKwhtAu/NKqur0sVvF0rBge5HgUHMrqIyqenTVcn3+M37ymTcq4W0iPFFifNzPrtWSzyfwXppERPnNxzFI4BApQV8gxj9Q6KOA8OCAAIVC1SrVk369Olj96DQXhRHHHFE0LAWFZfCHghEL+DaICbwknSaOR3Z/X//+5/dhy9we+BrgphAkeS9jseXOb2aKlajiWv658rwCFtkJE8g/JndFsTUsYZVal4nS049OMevVYp2Dbrqk/1jxKzYWCbNrADsiWODmyQ9+WOxvLukxA5aOjXOkhv75kmjA2PGeBT+t6RYJs/Z/4tiYBDj2Ucf9ZyFJWVS/UAwo12Tpnzrvl8waRHje9cie55Kn12CmMjuKXshgED8BKyR1mTL3p2ybtdmWas/Ozfbz1dsWycf/PJ1/E5MyQikkUDdunVlwIAB3uClb9++kp8f/LtsGl0yl+JyAdcHMdOnT5crrrhClixZUilKgphKccV153gFMZ5Kj7ZCg0v65EpuCo7j67YgxmM6tqdl2q18rBgd50Vbo3iWG4bkyjGt/LsP3fVVkUxfWD52jO5bo5rIy7/P94Ypum5fscjoVwvsQXnDBTH3fVMk//dTqTXjgcixh2bbdfmD1ZJm524twV0LQUz09yMVPrsEMdHfX45EAIHIBLYX7LaDlf1Byxbvc8/r9bu2WH+YONCUNLIi2QuBjBdo3ry5PY25tnbRn549e/q1cs14IACSLuDaIEYHHLv11lvtOd1L9dtYwHLUUUfJBRdcIGeeWT5Ime8uBDG+Gsl9Hu8gRq+uc8MsmTgkTxpaM/6k0pKqQcyb4/Klhk8XojXWbEfnTAvdbejkHjlyWffyUEfvz2UfFsrPa8skVBDz8uJieXyu+1q+hHtfEcSEk4lsvds/uwQxkd1H9kIAgdACuwr3eluyaKCyzvrRVi3rd5cHLnuKCkIfzFoEEIhYoF27djJ48GBvi5eOHTtGfCw7IpAMAdcGMTrF3rhx44JMtD+fjgI/atQo2bx5szRs2DBoH11BEBOSJSkrExHE6IXVqyry18F50qNJ6jSNScUgpoo1s9+746r4vZfeXF4i/5llNXUJsRzcPEseHubf9PNvc4tk5uLSoCCmyMpfRr5cEDS7UohiXbOKICb2W+Hmzy5BTOz3lxIQSFeBHQV7vIGKhiwartghy4HARVu07C7al66Xz3UhkDSB7Oxs6dq1qzd0GTp0qDRp0iRp9eHECEQj4Nog5owzzpCXXnop6Jp8R7R2CmJ0IN9TTjnF7/hIZ0OKdD+/wnkRViBRQYxWIMdqEHPhYbkytrN/t5mwlUvyhlQMYmrXEJk2xj+IefeXErnns9BBTEurtdIzx/sHMfd8XSTv/hgcxMzfUCrXz0it5tcEMWY+RG797BLEmLm/lIJAKgkUl5bIxj3bZMPurdaPPmrIos+tx11b7cBFn9OSJZXuKnVNZYEqVarIYYcd5tfipXbt2ql8SdQdAXFtEFOvXr2Qg/L6BjEfffSRDBs2LORtvP766+X888+XXbt2Sa9evex9Ig1YIt0v5IlZGSSQyCDGc/LBrbLluoF5Us1qveHmJRWDmFwr43rvDP8g5r1fS+TOmaGDmO6tsuTeIf5BzE1fFMmcZcFBzLPW9NXPzE+dbkn63iKIMfsJc9tnlyDG7P2lNATcKLCvuFCeWTBD3l8xX3QA3G37dlvD45a5sarUCYGMEKhVq5b079/f2+JFB9nVMIYFgXQScG0Q06JFC1m7dm2QdePGjeXss8+WjRs3ynPPPVfhNH3jx4+399OCIg1YIt0vqHKsCCmQjCBGK9Kqdpb8bWietLJmAXLrkopBjFq+emq+1KtS7rqzsExOnVpoD8AbaH3FoFz5fcDMVhfMKJQVG4LHiHl6YbE8/xVBTKBhsl7z2WX66mS99zgvAokS2Lpvp5z++q2yeMvqRJ2S8yCAQICAfr8bOHCgt8WL/hFdv4+xIJDOAq4NYsaOHSuvvvpqzPYEMc6ET34TuhWD81GV21pgneK1Rcn5cl3dGiP2WqtlzODW7hw3JlWDmAv75ci4Tv4D8Oq01I9Z01L7/g2xjTWFtU5znVWe2cjWAiu0mbJ/YN/AwXoJYiL/bPHZjdwqlj1ToUXM64s/l6/WLbYvM1uy5S+DzpAquf6t0JwMfI932s93W/2qteTqfmN9V4V9vnr7b/Lpqu9lzpqF8pvVvWOTNQ1vSVmJNKtRX1rUbiQH1WpkDbjeSga36hFRvdfs2CTP//i+fc0b92yXWlVqSPdGbeWc7sdJh/otwtbDd4OJMrS8xZtXyXM/vO8teuwhQ6R7k/be17E8KbBaafxr1otizR8ndaxr/HP/02IpzntsLHUuKi6W136eKd/+tkx+2rRSdhbulkMbtJZujaxBMlt3t+5ja+95onkSr2t2qsuV7z8oby2Z7bQL2xBAwLBA69atva1ddEajQw891PAZKA4B9wu4NohZsGCBHH300bJp06awijpQU/369R33IYgJy2dvcGsQ4Fzrym891Roz5oJeuZLjsjzGrf4VTV/duG6WPD8yP8jz87Ul8vrSEnuK6h6Ns+XczsHTij/yfbG8+t3+YI4gpvLvZc8Rbn3veOpn6jHZn91UCGKunvGQvLH0Cy/51+c+KnWr1fK+ruhJ4PEV7a/bG1arI1+e+7Djrt9vWCb3zntVPl35veN+no3Vc6vK8Da9ZGSH/jLECmXyc4P7lr6xeJbc8PHjUhBiKt/srGyZeMRZ8odux3iKDPloogxPwdOXfSmXvfcfz0u5bcgFclqXo7yvY3myzQqsDnv6EruIulVrytfnTY6lOO+x0db5x40r5NqPHpNFm1d6y/J9ov7X9j9VLu55ojYl890U8fN4XXO4Cui00X2eusQKBoNn5wx3DOsRQKByAvrvqAYtGrjorEZDhgwR7fnAgkCmC7g2iNEb891338nll18u8+bNk8LCQu+9qlGjhvTr10/uuOMOO4jRPoTaVcl3adWqlZx66qly7rnnSufOne1NVatWlYKC4CkCn3rqKXs/z/GR7ufZP5UfM+XLnN6jntZsSjcfmSv1qkX3C2I87rNb/SsKYtRiRGfrl+7ewV+UnJy+/m3/YLylB5rNEMQ4aTlvc+t7x7nW0W1N5meXICb0PXMKYkpKSuRfX7woTy+YHvrgCNZedfjJcsXhY/z2fM8KPS71CT38Nvq8uOuoi+XkQwf7rCl/aqKM8tJEog01fMsI9zxeoUQ0df52/RIZO+0fdkumcPX1rB/e5jB57Phrogpj4nXNnroFPn67YamMeW1i4GpeI4BADAJ5eXn2+JwavHjCFx37kwUBBPwFXB3EeKq6b98+mT9/vuzdu1c6deokGrL4/nKs6zWs2b17tzRv3tzezgfeo+f8mElf5lSiYTWRiUPypHMjdzSNcat/JEGMeh5nhTFX98yTvAi68b6/skTunV0sBeWZatD01XRNUtXIFre+dyKrfeX3StZn1/ffmuWXvVD5iifgiMAWLbG2iNFuJlVy/LseBl5Gw+p15eERVwaull2Fe+XCd+6WuesWBW3TFdnWX0bb1G4qjWrUlbU7N8uaXRultMy3Q+P+wwKDGJ3B5qjnJ8ie4vKpgLWsTvUPkqVb14jOcuNZqubky/un32l3e/Ks00cTZfiWp8+jCTUCywj3Ol6hRGXrvK+oUEb+7y+yYvs6b1Vzs3Pk8KYHS22ry9QPVkuZNbv8Wy/fM+xSGX3wEd79I30Sr2sOd/4l1nvnuJeuC7eZ9QggEIGA/oG8b9++3tBFB9atXr16BEeyCwKZLZASQUxm36L4Xn2mfZlTzVyrQcylfXJl1CERpAfx5Re3+p/Y1QpYepW3dlm5s0zOfd0nQfFxaVBHrF+4c+TEtjlSK9+/tVGJ1dr7M6u70jSru9IPq4K/bD16Yp50rFceij1sdVt67UC3JZ9TuPppsmZNcut7J543Kxmf3UwMYiob5HjuubaEOe/du+SzVQs8q+xHDUzO63G8jOo0SDrUbeHX5UjHHFm5c4N1zA/yzPfvya87NtjHBAYx98+bJvdZ3Zw8S8/G7eXxEyZIg+p1ZPu+XXLZ9P/I7LULPZvlkl6/k+sGjPO+1icmyvAr0HpR2VAj8Hin1/EKJSpb58e+eVtun/2St6qtajeWR4+/Wg5p0Mpep/f97i//J5Ot/TxLYyuo++iMe6R6flXPqoge43XN4U6uMyMNevYKa0rqLeF2YT0CCAQINGjQwB5Y19PipU+fPpKb6xzeBxTBSwQQsAQIYjL8bZCJX+Y8t3x4Wyts6J8nVZP4b0c6+WdbGUx1q8VRvRpZkmtlK9v2lMnOPSLF5X+o9tCn1SNBTOJvZyI/uwQxkd/f26zuSI9/+47fAV0btZHbhl4gXazBdCtcrJYxH/36jTxtBTJDrTFizu95gn2IftE/8rmrZP2e/V+Wa+fXkHdO/ZfV4qWht0j9Ij3i5RtkhzV4rC46mPDssx6UvANfDkyU4T2Zz5PKhho+h1b4NF6hRGXrfPH/3WtP6+yp8JTRE6V3s06el97H89++Sz5e+a339R1DL5SxnYd6X0fyJF7X7HTuN5d8IVe9/5DTLmxDIKMFdDwXT+iiY7x06dLFr2dCRuNw8QjEIEAQEwNeOhyaTkFANPejnTXorE5x3byWf0uOaMqK5phM94/GzG3HEMQk544k6rNLEBPZ/V2xdZ0c+/L1fmOIHN26pzVWyATRgfVjWQLHdbn8sN/LBGtQ2MDlrjmvyCNfv+ldfe/wy+T3ViscXUyU4S3Y50llQw2fQyt8Gq9QorJ1PvLZK71dj1pYM1x99of7QtZ9/rqf5dRpf/duO7/HCXLToDO9ryN5Eq9rrujcry761B7XaJvVuspti7Yoy7IGQs4WfbR+rApq+9LCEANWu63u1Cc1BQ4++GBv8KID6+oMRywIIGBegCDGvGlKlUgQIFLT6oFzw6A8GXBQbF8Wornx+Eej5q5jCGKSdz8S8dkliIns/k744GGZZs1o5Fm0Rcp74+6wuw551kX7eNWMB+XNpeXTC08dM0l6Nu0YVNy8tYvktNf/4V1/TNs+MtnqQqOLiTK8Bfs8qWyo4XNohU/jFUpUts49Hr9AdhbttevbsX5L+76GqvyOgj3S88kLvZs0iHti5LXe15E8idc1R3LufdZ04Romrd212Z5FSYMPnQlKg8QcKwDJycqxZgrU59Y660cfPa/3P1rbD+yfe+DR3sfe78C2A8fsP17L9JTvU651Dvvc1uP+2CV07Ye/+GdZvq183J7Qe7EWAWeBnJwc6dGjhzd40RYvjRo1cj6IrQggYESAIMYIY+oWQhCw/97pX5jO7JYjZ/fItX7BStz9xD9x1vE6E0FMvGQjKzfen12CmIrvQ2FxkfR++lLZfeDLuh4xecTVcky7PhUfHMEep079m8xfv9jes0G12jLvnIdDzshTWloqvZ682AoNrD6R1tKlYWt5y+rCpIuJMuyCAv6vsqFGwOGOL+MVSlS2zmNfmyRfbVhi11UDhE+ssV9a1mkcVHcd1Lfz4+d617er00w+OPNu7+tInsTrmiM5d6rtc/rrt8rctT+lWrWpb5IFdGZYz8C62t1o0KBBUrNmzSTXitMjkJkCBDGZed+9V00Q4KWwn/Rpli03WVNc166SmDQGf3//VHxFEOOOuxavzy5BTMX3d87qH+WMN/cHHrp3q9pN5JPx/674wAj38O0ac1iTDvLqyX8Le+RJU26yZvH5xd5uhzbnPmI/N1FGqJNWNtQIVUa4dfEKJSpb51s+fVqe//EDbzV7WAMlP221dKlbrZZ3nYZgus+kz57xruveuJ28fkp5CyXvBocn8bpmh1OmzKb1u7fKok2/yk+bV8pP1qMOir29YP+YSClzEVQ04QJ169YVncXIM8aLhjD5+fkJrwcnRACBYAGCmGCTjFpDEBB8u5tYg81Osqa47tQg/mEM/sH+qbaGIMY9dywen91MDGJOO/QoqZ5bxfHGDmzZRYa1Pcze538LP5EbPnncu/+Idn1DTm3t3aEyT6wBfDs+erZ37JnhbQ6Tx6zZksItZ711m3xuzcDkWRZd9F/Jt6bijrmMXKsPa4ilsqFGiCLCropXKFHZOuuX/pOm/NV7D7TCGnKNs94nLWo1lFU7N1pj8MyT5T7TW+s+o63xee6xxumpzBKva65MHZK9b1FpsSzZssYbuPy0aaUs2vyrbHXh+DXJtuL8wQLNmjWTI444whu8dO/ePeZxuoLPwhoEEDAhQBBjQjGFyyAICH3z8qzhYq7omysndIzvFNf4h/ZPpbUEMe66W6Y/u5kYxERyR8/ueqxMHHy2vet9c1+V+7+a5j1sQt9T5fI+v/e+juXJxj3bpN9/L/cWccohQ+TOoy/yvg58csV7D8jby+Z4V3965r+lal6VmMs4qE4Tb5m+TyobavgeW9HzeIUS0dT5P1++Jv+ZP7WiKvttj+Z9EK9r9quYi15s3rvDbt2yv5WL1dLFClyWbV0rxaVpPt2gi+5Bqlelffv23tBFW7107Bg8flaqXyP1RyBdBQhi0vXORnhdBAGhoUx/mQt9FhH8w8mkznqCGHfdK9OfXYKY0PfXN4i525qt6GGf2YpuG3KBnNblqNAHVnLtqu0bZMgL13iPOqPzMLl16Hne14FP/vzhozL158+8q2eMu1OqWC1iYi2jQ/0W3jJ9n0QTavge7/Q8XqFENHUuLikWnZ786QXvOVXZb9vDx10pI9r39VtX0Yt4XXNF54339hIrWNGBdT3dijzBiwaNLAhEKqADR3fr1s0bvOjAuk2bNo30cPZDAAGXCRDEuOyGJLo6BAHB4o2r75/Smq5JwTasCRYgiAk2SdaaeHx2MzGI6VC3heTn5jrexpMPPlLO7XG8vc8LP3wgf535tHf/c7sfL389Yrz3dSxPAgeAHdm+nzxw3BVhi7zo3Xvkg1++9m7/9vzHJT87128Q2WjKqF2lurdM3yfRhBq+xzs9j1coEUudZ678Th79+m350RqHxzMocq286tKlURuZs3ah93J0hqFZZ/1HGteo510XyZN4XXMk5za1z7aCXdZYLqvs1i2ebkWLra5GTDdtSjhzytGxXPr06eMNXrTLUZ06dTIHgCtFIM0FCGLS/AZXdHkEMf5C8Rrw0/8s5a/wL7dI1WcEMe64c/H67GZiEPP1uY/6DcRa0R3WL+fnvH2nd7d+zQ+Vl0bd7H0d65OeT1wkOwr3D0o6oHlneWHUTWGL9J3hR8e5+eGip+x9TZQR6qSxhBqhyvNdF69QwkidrbF71u7aIjXzq1qD29eQwO5pY6yg7u5hl/heTkTP43XNEZ28kjuVWga/bl9/oJXL/m5FGryss6a/ZkEgGoFatWr5Dazbr18/0VmOWBBAID0FCGLS875GfFUEAfupdFje07vmyLk9mb464jcPO9oCBDHJfSPE+7NLEFPx/V2zY6Mc+fxV3h21hcTssx+Q6taXdBPLiJevl8VbVttFHVz/IPm/cbeHLXbo89fIyh0b7O2+0yebKCPUSY2EGqEKttbFK5QwXect1jgnw1+6Trbt2+m9kumn3W4NeH+Q93WkT+J1zZGeP9x+u4v2Wa1cysMW7Vr0s/Wzt7gw3CGsR6BCgUaNGvkNrNurVy/JyYnv2IQVVoodEEAgYQIEMQmjdueJCGJEaliTYdw4KE8GHGSN0JvgBf8Eg8fhdAQxcUCNsMhEfHYJYiK7GadP+4fMXbfIu3NFY7l4d4zgydlv3W5P1au7Zln/m3/uw1LPmrUncFlvtdAY+OyfvKsHtugiz//+L/ZrE2V4C/Z5YjrU8Ck6ZYKYi//vXnl/xXxv1Y9q1VOePPFa7+vKPHFDELPamgVKW7bowLn2ozVr1CorbCyz/seCQCwCrVu3Fh3XxTOV9CGHHBJLcRyLAAIpLkAQk+I3MNbqZ3oQ0LauNR6MNVV1i9rxn6o61L3KdP9QJqm2jiAmOXcsUZ9dgpjI7u+7S+fIH2c84Lfzkyf8WY5q08tvXTQv7rIGA37EZzBg7fKiXV8Cl8Cxav7Ue5Rc3W+svZuJMgLPp68zPYh5dsEMmfTZM16a7KwsmTJ6ovRqGt3MLYkOYnR2ok+trnVfrP5Rftz0ix287Czc470eniAQrYD+29G5c2dv6KIBTMuWLaMtjuMQQCANBQhi0vCmVuaSMjkIGN42W67unydVncekrAxnpfdNpH/7plnS0idw+mWb1b/9N/7CV+mbFnAAQUwASAJeJvKzSxAT2Q3VWXVGvHyDLN++zntAg2p15P5jLpMBLbt611X0pKSkRF5f8oWs3blJ/nT4aHv31dt/k8EvXO09dJBV3nMn3eh9rU/Kykpl7NS/ydcbltrrNRCYOf4/0rxWA/u1iTLsggL+L5ODmFBTWl/b7zS5tPdJAUqRv0xkEPP5qgVy4ydPyBrrvcaCQKwCeXl5ol2LPC1edGDd+vXrx1osxyOAQBoLEMSk8c2N5NISGQREUp9E7JNrNX65uHeujDk0+f1wE+n/z2NypX/T8muesbJE7vi0OBHkaX0OgpjE3d5kfHYJYiK/v4s3r5JRr94i+0r8x80Yc/ARctPAM0N2J/KUXmCNtTHlp5ky+Zu3ZM2uTRI44O85b90hM1d979ldbjniLDmn+3He1w/Mmyb3znvV+3p4m8PksRMmeF/rExNl+BVovcjEIEZDt7988qS8+vNMPw7b/HhrqnErBIt2MR3EFBYXSb41fXlgnWau/F7Of+cuKbECPBYEohGoXr269O/f39viZcCAAaLrWBBAAIFIBQhiIpVK0/0SGQS4gbBBNZGJg/OkS+PEjwcT6voT6U8QE+oOxL6OICZ2w0hKSNZnNxWDGG2VUDPf+o9dBcthTTpY/y1sK1fPeEjeWPqFd++/DhovOuBuRcux7fvYM+b47jf158/kzx8+6rvKfl47v4b0bX6IdKrfQjrWbykNrTFeVlrjbizbulaWb1sr3/+2XLb4DPYaGMS8v3y+XDz9Xm+5udk5MqrTEdLVmjb5O6sVzBtWKxqdxcazPHPi9XJkq+6el/ajiTL8CrReZFoQs6twr1w6/T6ZtfoHP4pWtZvIm2Nvtd4PFb9v/A4MeGEqiNGw6HKrq9zHv34jLWs1lH8NOV/6t+xin21fUYEc9vQlso+BdgP0eekkoK1bBg0a5G3xcthhh4m2gmFBAAEEohUgiIlWLk2OS2QQkGyy7lb4csvgXOuvstH/tc70NSTSnyDG9N3bXx5BTHxcfUtN5mc3FYMYXzun55cf9nuZ0P/UoCDG6RjfbVPHTJKeIcYCeX/5PCuMmSw7i/b67l6p54FBjNX3SM6zWjB8Yo3nUdEysn0/eeC4K4J3M1FGQKmZFsQEDsqsHC1qNpSnrMF5NWCLdTEVxASOWdS7SUeZcvIku3r3ffmq3D9/WqxVTejxVXPzpU2dptKubjNpa/3sf9TXzaXnkxcmtC6ZcjIdz8UzqK4+dunSxWpY5Z7fHzPlPnCdCKSzAEFMOt/dCK4tkUFABNWJ2y6nWN2QLjosV3Lc0RDGe52J9CeI8bIbfUIQY5QzqLBkf3Z9f/FeftkLQfVzw4rAFi2R1ileQYyef+W2DXLdx5Ply3U/R1od734awpzXfYQc066Pd50+2b5vl5z99h126xm/DT4v+jU7RJ4Yea3UCDN1tokyfE6XcS1i3l4yW654/0EvwREHdZUHjvmT1Kla07suliemgpgXf/hQbp75lLcqra0WOx+P/7f9esIHj8i0xZ97t7nliY5r1MJqvVMetHgCl2bSrGZ9e8awUHVt9/CZoVazrpICBx98sB28eMZ4adOmTSVLYHcEEECgcgIEMZXzSru9n/wm/mOEFFineG1RSVLsqlldw68bmCeDW7ssgTmgQRCTlLeF0ZMmK4jhs2v0NoYtLBWCGO0KpF2CKrtc2WeMXNn3ZGu8jydk6qLKfzF97eSJ0qVRW8fTLtmyWl5Z+IlMX/6lrN+9xa/7kOfABlY3Jf2ieXSrXnLKIdbMInUaezYFPeqYHw999bq88OOHfl2ZGloDA5/V7Vi5tNfvJCenfCysoAKsFSbK8JQb2N3prqMulpMPHezZHNOjqVAisBKx1vl+azye+6zxeC477CSZYM1KlZVl7t9XU9e8s2CP/G7KzVb3tw2Sn50ntw4+V07pPMSmeG3RTLn2o8mBLAl7Xc8KrbQlS9u6/i1cWtdpYo1nE3lXl4KSIvnk1++srmLlXfYSdhEpfiL9b0TPnj39Wrw0atQoxa+K6iOAQKoJEMSk2h1Lwfpu31cmY6b4D96YiMtoZc0Q9LehedKqjnubkhLEJOKdEN9zJCuIie9V7S+dz66O8Vn+3w+3tohJxHvBxDlKS0tlw+6tdiBTZE0b3KxGPWlSo77k50b+5dNTD51dad2uzXb3p9r51a0gp4FkZ1cuEDBRhqc+8Xg0FUrEo27a4qlV3SbGizZ6zVZXtO82LJO29Zr5jWW0cc82GfjsFVJivQfjtVSxAhUNVtrVswIX7VLkfWwmdatE33pI6/yZNT6PtkyasWK+6Jg9LJUTuPfee+X888+XWrVqVe5A9kYAAQQMCxDEGAaluGCBZHyZG9wqW661WsJUr/zv98EXEMc1bg5itBtXq0ZZ0qhGlmjLolJrHMxNe8pk5ZYy2V2J3/2iLadWDZFqeeVfgrfuKrO+aIm0bpglDaxxfvKsP3qv2Fom662fovg37Ar7LiCICUsT1Qa3fXYJYqK6jRxkQMBoKGGgPokoIlHXfMvM/8rzP7wf0yVlWZ2FdHr08q5E+8ds0ZYuza1xc7SrkYlFB6Geu/YnO3zRlmVbrS56LNELPPnkk3LeeedFXwBHIoAAAoYECGIMQVJMeIFEBjHZ1u89F/TKldO6ODdND1/bxG5xYxBTtYrI+J45cmKbHKmVH/yLpAYys9eVyBPfl8jKjeWzlATKxVrOf0fny0E1/c+/1wpcNBQKXKYtK5bJX5YkJZAhiAm8G9G9dutnlyAmuvvJUbELJCqUiL2m5kpI1DXrjEmXv3e/PatSRbWvU6VGwCC5+8du0RYvOohuPJYyKZNv1i+Vt5fOlneXzpXfrFY8LNEJNGvWTDp27Cjt27eXtm3bysiRI0VnPGJBAAEEki0QcxCzefNmmTNnTtB1aP/L4447zm7WXWal+e+99558+eWXsmXLFjnkkEPk2GOPlXbt2gUd57ti7dq18vPPP8uiRYtkyZIlUrVqVdHBtPR4faxbt67v7n7PP/30U9m1K/ivBnrOQw891G9fffHjjz/KL7/84l3fsGFD6devn/e158mKFStk4cKFnpfex2rVqsnRRx/tfe15sn37dvnmm2/k66+/lmXLlkn16tWldevW0qtXL+nTp49UqWJ9603zJVFBTL2qIjcfmWfN4mE1m0iRxW1BTJsmWXKnNb13g6r+AUgoTutjLZN/KJYp3wY37zZRzjNWENMyIIgJVQ/PulVWi5kJHxXK5u2eNYl5JIiJ3dnNn12CmNjvLyVEJ5CoUCK62sXnqERes4Yd/7fsS3nL6ubzy/b19vgsza2xitrWsYIWqzuT57Fe1cR1Yflx0y92y5e3l86RNTs3xQc5TUvV36979OghvXv3th+7du0qnTt3ltq1a6fpFXNZCCCQ6gIxBzFTp06Vk08+OaTDzp07RYOIP/zhD/Lxxx/77ZObmyuXX3653HfffX7r9cUrr7wif/7zn2X16tVB23xXaJDx4IMPhgxMdNtXX33lu7v9fMCAAfLFF18Ere/evbssWLDAu177jq5bt05q1LD6R/gsp556qkyZMsVnzf6nGu74BjSLFy+WM888066DBlGhlnr16sm4ceNkwoQJdlIfap90WJeIIKaz1V1l4pA8aVi94gDBTaZuCmLqWb9rPn1CfshWME5mt80rkg8WlXp3MVVOZYMYrcD3m0rlmv8rsn69TtxCEBObtds/uwQxsd1fjo5eIJGhRPS1NHtkJl7zkq1rvOHLim3rzIKmcWn6+/lRRx0lw4cPl0GDBtmD7+p3CxYEEEAgVQTiGsSsWrVKjjnmGLtFSyiQvn37yty5c72bNLzQcOaDDz7wrqvoif6SrINu3XHHHVK/fn3v7o8//rhcdNFF3te+T7R1TYcOHbyr5s2bJ1qXwCWwH6kGS02aNJG9e4MHyLjnnnvkmmuusYt444035KyzzpIdO3YEFhny9b///W+5+uqrQ25Lh5XxDmJO6pQjlx+eK7mp0xDGe1vdFMTccWye9Gnij7hyZ5ncNrdIVm4uk5rVxO7yNaa9/y86JVYGM2Zagezas/+yTJUTKoh595cSeWlhiewrKpNuVsun63rnWU3DvZz2k5u/KJLZy8qDIf+t5l8RxERvmgqfXYKY6O8vR8YmkImhRKZc88rtG6xuR3PkLavr0c+bV8X2Rsmgo/WPpGPGjJHTTz/dDmHy8+PTNSyDSLlUBBBIokBcg5gTTjhB3n333bCX99BDD8lll11mb9eWM9qMsKJWMOEK08BnxowZ3s3aLal58+ai4UngMnHiRJk0aZJ39SWXXCKTJwdPZRjYeua5556zAxbvgQee6D8Ea9asEe3OtHv3btEp8EKFNYHHeV4TxHgkKvdYxRoG5pr+uTK8XWqMBxPq6twSxDSumyUv/c7/F5oN1sC857xZKIVF/jUf3ztHzu3sn348umB/FyVT5egZA4OYlxcXy+Nz/btBaReoJ4/1r/e3G0tlwvSASvtfgtFXBDGV50ylzy5BTOXvL0eYEciUUMJXK52vea01y9c7VviiAcyC35b7XjbPKxDQ8V30j53awp7ZjirAYjMCCKSMQFyDGCcFDS+064+nFYu2annqqaecDqlwm7aCueCCC7z7acjzyCOPeF97nuh/0JcuXWq/3LNnj+hAXuFar+jYMdrHVJfjjz9epk+fbj/3/b/TTjtNXn75ZXvVa6+9JqeccorvZu9zDYYOP/xw+e2332T+/PlSVLT/yyJBjJco4ifNrbFDdGrqdvVSqytS4AW6JYg55tBsuaGP/xRTE2cXyedLg1uW5Fu7vXZyvjUjVbn9AqtL0FVWlyBT5ahTYBDz9MJief4r/yBG97t1eK4MaFYexu0sLJNRryRuunSCGL0LkS+p9tkliIn83rKnWQGdmviUqZOk1BohvUG1WvLS6L+aPYELS0u3a9ZBdnWwXQ1gvl6/xOo2m8iOsy68wZWskg6ye9NNN9ld/el2VEk8dkcAAdcLJC2IGT16tOj4Mrp89NFHMmzYsLBYOgbN7373O9HQRLsLhRr7RQ+uU6eOHbBoyxRdvvvuO7vPqP0i4P90nBht8fLss8/K2WefHbC1/KV2GdKgZOPGjXYLm+Li4HlytSuVp/4XX3yxPPbYY+UFHHimwZO20snL2/9ld+vWrfa5b7vtNrn++uvpmhQkFn7FgJZWaDAoT2r6N4QIf4CLt7gliDm7T46cdah/KxftbrQ9eLxrW/O+4/OkW8Pybkxb9pXJ2CmFYqocPUmkQcyo7tnypx7+IdKJUwpk777E3HiCmMidU/GzSxAT+f1lTwQQENmyd4c9CLC2fJm3bpHo9NMslRNo06aN/O1vf7MDGJ38gwUBBBBIR4GEBDEaPuhMR+vXr7cDDYV8/fXX5fe//71tet1118ldd90V0veqq66Se++917utoKDAHpjr888/967zffLmm2/aoY1nnYYtoWZ1uvTSS+Xhhx+WIUOGyMyZMz27Bz1qqKPdjp544gl7/JrAHbR1jY454/ll/S9/+YtouBJqOeecc+SWW26xp8/zbNcuWfv27bPHnvGsS7dHU2PEaPuLs3vkyPhuuZZ3eii5JYj548AcGR0w9svxLxcEdUvyqP99WK4Mal7+y1Gx1XDmuBcKxFQ5ep5Ig5ijDs6Wm/v6BzHj3iqUjdsS88svQYznXRH+MZU/u57/tuvVLb/shfAXyRYEEMhYga37dsmM5fPsbkdz1iyUkrLg1qSJxjnooIPsFtj6e3OqLPo7t7aA0RbtjP+SKneNeiKAQLQCcQ9iNICZNm2aHcRoJTXQuPXWW+3wwtM6JFyXH50SWseM8XRf8lyktkDRMWFCLf/85z9FwxDP8swzz4gGIIFLgwYNRKe41nFpKlp0Fqf7779fZs2aFbTrv/71L7nxxhu963VMnJEjR3pfBz7Jzs4WnXnp5ptvli5dugRuTsvXJoKY2lbrl5usqan7NC9vhZEOWG4JYsb1ypELu/q3iHEKMyb/Lk861C2/F+t2l8n4qYViqhy9t5EGMaf2zJGLrXDOd/md1SJmDy1ifEmies5nV7whuwISxET1NuIgBNJSYHvBbnl/xXyr29Fc+Xz1D1JSGtx1NtEXrr8bjxgxwv7RbvX6h0zPRBKJrktlzlezZk27ZbjOmMp005WRY18EEEhlgbgGMVWrVrW7EXnGWAkHpal9qEF6e/bsKd98803QYTogrv5HO9Si00G/9NJL3k06aG6LFi1EuwIFLhoSLVq0KHB10Gvto6pjygROQ639VXVmqKZNm/odo92o3n77bb91gS/0r6za5Ur/kWzZsmXg5rR6HeuXuU71s2SSNTV1E2tcmHRb3BLEDOmULbf0829V8uB3xTLt++BfLHV66ldOqiI55TmMzN9QKtfPKBJT5eh9jiSI0ZZRj1mhULs65ZUpsqp8/IsFCeuJT4uY8J/KdPjs+raICX+lbEEAAQSSLxD4e6q+1rEJw3XpT3aNq1evLtpCXbvo60QXLAgggEAmCcQ1iNHxUh599NEKPTXQKCkJ/sI3dOhQ+fjjj0MeH+6XY21dEzhTk3Zv+s9//hOynMCVGv58++23gatDvh41apTd2idwozYD1RYvOrZMaalz81QNebRrVGCYE1hmKr+OJYg5vkOOXNk3V/LKe8GkMkVQ3d0SxNSwpqZ+/eQqku2Tde2xpog++91C2eIzC7tuv2lorgxt6X9DbptXJB8sKhVT5ShUJEHMSd2y5cqe/gHSBytL5LZPg8dyCsI3tIIgJjRkunx2w/1bE/qqWYsAAggkT8A3iNHW4//4xz8cu98nq6Z169YVnbFUHtx8MQAALTVJREFUW+sQwCTrLnBeBBBItkBcg5gffvghou432iJEx2EJXDSkWLx4ceBqu89rkyZNgtbrisAWMbpu4cKFEdVDBwRbtmyZHHHEESFb6GhZvss777wjOkV3uEW7Pp133nmyfPnycLvY6/v37y+zZ8923CeVN0YTxORZDRyusAKYEzr6f+FPZYdQdXdLEKN1u3Zwroxo7e+tYcwj1tTUS7eUSd1qWTL+kBzp0qC89Ykep7MUnTatUAoOTFRkqpzAIEbrMmVpicxZWypVrZ5Ix1p1Pb6Nf321Ppd+UCiL1yVmfBg9H0GMKpQv6fbZJYgpv7c8QwABdwtoEKN/jNQAJtT4iMmuvY6rePnll9sznDINdbLvBudHAIFkC8Q1iNm5c2fYLkS+Fx5uQF3dR1un9OjRw3d3u5WNNmUMtVx77bVy5513Bm2qaFBePcDTwuXvf/+7TJw4MagM3xXaneqXX34RHfPFadFZlqZMmSJ33HGHPYtTqH31F/0tW7aI/oUgHZfKBjFNauzvitSpgU/zjHSEsa7JTUFMVWscnsdPzJfmln+ki04G8aePC+WnNeXBh6lyAoOYSOrkaZkTyb6m9iGIKZfMpM9u+VXzDAEE0lFAW2TrmCuhWmzH83q1hYhnrJdjjz02oskcNIDRSTB0DMavv/46ntWrdNk66K6Onait5PV6CLcrTcgBCCCQpgKuCGI09NDwI9SiIc1bb70lOriuLtrK5rjjjpO1a9eG2l3ef/99e1alwI0vvviiPQ1e4Hrf19OnT/eW3bp1awk1VbVn/0mTJoUMa3RMG53R6cILLxQdI8ez6D/kOohwqJBI9/nss8/sljie/dPpsTJBTJ9m2dagvLlSu0rkYUAqW7kpiFHHOtbQS5OsQZG7+0xNHc53e0GZTJpTJN+vLA9hPPuaKKeyQcwD3xXJ6987dwX01M/kI0HMfs1M++yafA9RFgIIuE+gV69eEXdVj6X22j1ff9fV3231p3fv3hGHFdr9XSeU0Ikj9Pdjtyz6R0q9pjPOOMNuqR446YZb6kk9EEAAgWQKuCKI0ZYl7dq1CxoM1wOj3ZD0HyZdPvnkE9mzZ49nk99jmzZt7G5AodJ2HbdFu0Bt2rTJ7xjPCz2/DsjrOVYH0p06dapns9+j/gOjddZWMYGLJ/Bp3ry56OjvQ4cOtbtFFRYWyj333CMa4IRatCmpjm+TjkskQYzGLmd0zZFzeub6jVOSjh6+15TIIObmo3LlKJ+xXd5YXiL3zwoeS0XHgRnYPltGW+Pz9GwU3OJr/R7rL2/LSuSdRSWOMxPFWk6oIGa31T2pRp5/SLdoS6k8Z9VlzrLEhzB6LzM9iMnUz67v55jnCCCQXgK//fZbRC1Ror1q/WOfhi46w9GwYcMqPVOQ/qHw+eefl9tuuy1kF/5o6xXLcVWqVBFtfT569Gi7hXk6j30YixPHIoAAAh4BVwQxWpkJEybYg9t6KhbNo06Trd2Lwi3XXXed3HXXXSE3a9ch3e5ZnKbI1nFhdHyYUIsniPHdpsGNNhv1HUTNd7v+NUT/0a9Xr57v6rR5XlEQU8Maa/XGQXky4KDgL/1pgxDmQhIZxISpguPqfOve1LG6KtW2GnftLRLZZk1TvdeaFjq4DYxjMRJNOYFBzNMLi+Wlr0ukjjVrU93qWbLPqs/WXVZ9CpzPHe+tmRzEZPJnN97vK8pHAIHkCegf29q2bWusAjo7kIYUnvDl4IMPjqps/aPiU089ZXd3//XXX6Mqw9RB+ofLbt26ydFHH223RNc/PNaoUcNU8ZSDAAIIpL2Aa4IYTfeHDx8uOsBtNIt2+/nnP//peOiSJUtE//ELDES0/6pOn+07crvuo/vqMYGLU+ATKogJPD7w9SmnnGKPIxO4Pl1eOwUx7eruHw+mRW39u3rmLW4PYpJ5R0IFMc9/FTy7WjLrqOfO1CAm0z+7yX7fcX4EEIifgHb50d8Jdfy+aBcNKTzdjY488kjRFiPRLtoSfPLkyXL33XeH7ZofbdmRHlenTh27dXq/fv1k4MCB9g9djiLVYz8EEEAgWMA1QYxWTbvv3HTTTXYXnsCwJLjq+9foqOsPPvignHXWWeF28VuvA6Bpaxff5fTTTxcNUAIX7Uqk3Yt8F21quWrVKtFWLKGWygYxXbp0kVmzZon+A5euS7ggZnjbbLm6f549A066XntF10UQE16IICa8TaK28NlNlDTnQQABtwnowLd//etfI66WjmWov2Nq+KKD0moX9ViX7du3y0MPPST33ntv2K71sZ4j8Hhtxa1d/bt27Wr/9OzZU/SnQ4cO3u77gcfwGgEEEECg8gIxBzE6kO5JJ50UdGb9D7nOmqTNMSu76ExJzzzzjD0A2bp160Ierv9AnHnmmXYAU5l/7F599VUZO3asX5naCmfw4MF+6/SF/iWkRYsWsm+f1RfjwHLjjTfag6J5Xgc+6qC8Oljvm2++aQ8yHKpFjR6j49Vcf/31ctFFF4m2yEnnJfDLXK7V+OXSPrkyypoKOdMXgpjw7wCCmPA2idrCZzdR0pwHAQTcJqC/z5133nny7LPPhqxaXp7VpdoakFZDF/3RsQz1d18Ti45nqOGLhjAaxsR70W5Feq0auHTs2NFvsol4n5vyEUAAgUwViDmIiSectorRf4w0jFm/fr3oP3rNmjWzf1KlBYn259X66yxP+o+phkb6l4ZUqb+J++v7Za5hNZGJQ/Kkc4hBYE2cK9XKIIgJf8cIYsLbJGoLn91ESXMeBBBwq8CHH35o/2Fw+fLldveizp072+O9aHhRs6Y11aDBRbvJa/ejxx9/POzEFKZOp92KtDW5/kHw0EMPNVUs5SCAAAIIRCjg6iAmwmtgN5cLeL7MdW+cLbcMzpV61TJzPJhQt4kgJpTK/nUEMeFtErWFz26ipDkPAghkssDixYvlzjvvlOeee87uph8vCx1gVwOk888/X3R20KpVrZH4WRBAAAEEkiJAEJMU9sw66Y6CMnlxQYlceFiu5JhptZs2gAQx4W8lQUx4m0Rt4bObKGnOgwACmSgwb948O4CZOnWq6ADB8Vq0m/0555wj5557rrRv3z5ep6FcBBBAAIFKCBDEVAKLXREwLUAQE160XdMsqVu1vPXUis2lsnVn+P2TtSWdZ01KlinnRQABBNJZ4Oeff5arrrpKpk+fHrfL1FmadAxHDWB0AOGcHMblixs2BSOAAAJRCBDERIHGIQiYEiCIMSWZvHIIYpJnz5kRQACBVBOYPXu2jBgxQnbs2BGXqh9++OF2+KIzgtarVy8u56BQBBBAAIHYBQhiYjekBASiFiCIiZrONQcSxLjmVlARBBBAwNUCOoGDzkq0atUqo/XUmTg9M4nqYMIsCCCAAALuFyCIcf89ooZpLEAQk/o3lyAm9e8hV4AAAggkQuCdd96RE0880cipdMamMWPG2DMfHXXUUcamzjZSOQpBAAEEEKhQgCCmQiJ2QCB+Asc9XyDFZfErn5LjK5BrDWHz3vgq8T0JpSOAAAIIpIXA5MmT5ZJLLon6WnScl2OPPVbGjx8vo0aNkurVq0ddFgcigAACCCRXgCAmuf6cPcMFRr9SIDsKMxwhhS+/dr7ItNMIYlL4FlJ1BBBAIGECH374oQwfPrzS59NxXzR8GTdunDRu3LjSx3MAAggggID7BAhi3HdPqFEGCVzyTqEs2UKTmFS95R3rZ8mjI600hgUBBBBAAIEKBEpKSqR79+6ycOHCCvYUe5ppHfdFfzp16lTh/uyAAAIIIJBaAgQxqXW/qG2aCdz+eZG8v6I0za4qcy7nmLbZcsMReZlzwVwpAggggEBMAosWLbKnk165cmVQOfXr17dbvWjrlwEDBgRtZwUCCCCAQPoIEMSkz73kSlJQ4N0lJXLPnOIUrDlVVoEJ/XPlhI45YCCAAAIIIBCxgE5d/cQTT8inn34qu3fvlnbt2skJJ5xg/+Tn08oyYkh2RAABBFJYgCAmhW8eVU99gc17ymTc1EIppXdSyt3MbGug3pfH5EuD6tYTFgQQQAABBBBAAAEEEEAgQgGCmAih2A2BeAnc8GGRzFtL96R4+car3L7Ns+W2YXRLipcv5SKAAAIIIIAAAgggkK4CBDHpeme5rpQR+HZ9qUx4vyhl6ktF9wvcc0ye9GyaDQcCCCCAAAIIIIAAAgggUCkBgphKcbEzAvERuO6DIvlqHa1i4qNrvtTezbLlzuG0hjEvS4kIIIAAAggggAACCKS/AEFM+t9jrjAFBFbvKJOL3i6UgpIUqGyGV7GKNTbvYyfmS8vajA2T4W8FLh8BBBBAAAEEEEAAgagECGKiYuMgBMwLTF9aInfNZgYl87JmS7x2QK6M6MBMSWZVKQ0BBBBAAAEEEEAAgcwRIIjJnHvNlaaAwJPfFMuLP9Asxq236oyuOXJ+r1y3Vo96IYAAAggggAACCCCAQAoIEMSkwE2iipklQBjjzvtNCOPO+0KtEEAAAQQQQAABBBBINQGCmFS7Y9Q3IwS0m9L9XxYzZowL7raOCXNFX7ojueBWUAUEEEAAAQQQQAABBNJCgCAmLW4jF5GOAjqAr4YxzKaUvLursyNpCMPAvMm7B5wZAQQQQAABBBBAAIF0EyCISbc7yvWkncA31rTWrywssQOZ0rK0uzzXXVC2NRmSBjCndc6RXtYjCwIIIIAAAggggAACCCBgUoAgxqQmZSEQR4HNe8pk7ppS+X5DqfyyvUw27CqTPUUixYQzUavnWqFL9TyRJjWzpE2dLOneJFv6tciWBtWZmjpqVA5EAAEEEEAAAQQQQAABRwGCGEceNiKAAAIIIIAAAggggAACCCCAAALmBAhizFlSEgIIIIAAAggggAACCCCAAAIIIOAoQBDjyMNGBBBAAAEEEEAAAQQQQAABBBBAwJwAQYw5S0pCAAEEEEAAAQQQQAABBBBAAAEEHAUIYhx52IgAAggggAACCCCAAAIIIIAAAgiYEyCIMWdJSQgggAACCCCAAAIIIIAAAggggICjAEGMIw8bEUAAAQQQQAABBBBAAAEEEEAAAXMCBDHmLCkJAQQQQAABBBBAAAEEEEAAAQQQcBQgiHHkYSMCCCCAAAIIIIAAAggggAACCCBgToAgxpwlJSGAAAIIIIAAAggggAACCCCAAAKOAgQxjjxsRAABBBBAAAEEEEAAAQQQQAABBMwJEMSYs6QkBBBAAAEEEEAAAQQQQAABBBBAwFGAIMaRh40IIIAAAggggAACCCCAAAIIIICAOQGCGHOWlIQAAggggAACCCCAAAIIIIAAAgg4ChDEOPKwEQEEEEAAAQQQQAABBBBAAAEEEDAnQBBjzpKSEEAAAQQQQAABBBBAAAEEEEAAAUcBghhHHjYigAACCCCAAAIIIIAAAggggAAC5gQIYsxZUhICCCCAAAIIIIAAAggggAACCCDgKEAQ48jDRgQQQAABBBBAAAEEEEAAAQQQQMCcAEGMOUtKQgABBBBAAAEEEEAAAQQQQAABBBwFCGIcediIAAIIIIAAAggggAACCCCAAAIImBMgiDFnSUkIIIAAAggggAACCCCAAAIIIICAowBBjCMPGxFAAAEEEEAAAQQQQAABBBBAAAFzAgQx5iwpCQEEEEAAAQQQQAABBBBAAAEEEHAUIIhx5GEjAggggAACCCCAAAIIIIAAAgggYE6AIMacJSUhgAACCCCAAAIIIIAAAggggAACjgIEMY48bEQAAQQQQAABBBBAAAEEEEAAAQTMCRDEmLOkJAQQQAABBBBAAAEEEEAAAQQQQMBRgCDGkYeNCCCAAAIIIIAAAggggAACCCCAgDkBghhzlpSEAAIIIIAAAggggAACCCCAAAIIOAoQxDjysBEBBBBAAAEEEEAAAQQQQAABBBAwJ0AQY86SkhBAAAEEEEAAAQQQQAABBBBAAAFHAYIYRx42IoAAAggggAACCCCAAAIIIIAAAuYECGLMWVISAggggAACCCCAAAIIIIAAAggg4ChAEOPIw0YEEEAAAQQQQAABBBBAAAEEEEDAnABBjDlLSkIAAQQQQAABBBBAAAEEEEAAAQQcBQhiHHnYiAACCCCAAAIIIIAAAggggAACCJgTIIgxZ0lJCCCAAAIIIIAAAggggAACCCCAgKMAQYwjDxsRQAABBBBAAAEEEEAAAQQQQAABcwIEMeYsKQkBBBBAAAEEEEAAAQQQQAABBBBwFCCIceRhIwIIIIAAAggggAACCCCAAAIIIGBOgCDGnCUlIYAAAggggAACCCCAAAIIIIAAAo4CBDGOPGxEAAEEEEAAAQQQQAABBBBAAAEEzAkQxJizpCQEEEAAAQQQQAABBBBAAAEEEEDAUYAgxpGHjQgggAACCCCAAAIIIIAAAggggIA5AYIYc5aUhAACCCCAAAIIIIAAAggggAACCDgKEMQ48rARAQQQQAABBBBAAAEEEEAAAQQQMCdAEGPOkpIQQAABBBBAAAEEEEAAAQQQQAABRwGCGEceNiKAAAIIIIAAAggggAACCCCAAALmBAhizFlSEgIIIIAAAggggAACCCCAAAIIIOAoQBDjyMNGBBBAAAEEEEAAAQQQQAABBBBAwJwAQYw5S0pCAAEEEEAAAQQQQAABBBBAAAEEHAUIYhx52IgAAggggAACCCCAAAIIIIAAAgiYEyCIMWdJSQgggAACCCCAAAIIIIAAAggggICjAEGMIw8bEUAAAQQQQAABBBBAAAEEEEAAAXMCBDHmLCkJAQQQQAABBBBAAAEEEEAAAQQQcBQgiHHkYSMCCCCAAAIIIIAAAggggAACCCBgToAgxpwlJSGAAAIIIIAAAggggAACCCCAAAKOAgQxjjxsRAABBBBAAAEEEEAAAQQQQAABBMwJEMSYs6QkBBBAAAEEEEAAAQQQQAABBBBAwFGAIMaRh40IIIAAAggggAACCCCAAAIIIICAOQGCGHOWlIQAAggggAACCCCAAAIIIIAAAgg4ChDEOPKwEQEEEEAAAQQQQAABBBBAAAEEEDAnQBBjzpKSEEAAAQQQQAABBBBAAAEEEEAAAUcBghhHHjYigAACCCCAAAIIIIAAAggggAAC5gQIYsxZUhICCCCAAAIIIIAAAggggAACCCDgKEAQ48jDRgQQQAABBBBAAAEEEEAAAQQQQMCcAEGMOUtKQgABBBBAAAEEEEAAAQQQQAABBBwFCGIcediIAAIIIIAAAggggAACCCCAAAIImBMgiDFnSUkIIIAAAggggAACCCCAAAIIIICAowBBjCMPGxFAAAEEEEAAAQQQQAABBBBAAAFzAgQx5iwpCQEEEEAAAQQQQAABBBBAAAEEEHAUIIhx5GEjAggggAACCCCAAAIIIIAAAgggYE6AIMacJSUhgAACCCCAAAIIIIAAAggggAACjgIEMY48bEQAAQQQQAABBBBAAAEEEEAAAQTMCRDEmLOkJAQQQAABBBBAAAEEEEAAAQQQQMBRgCDGkYeNCCCAAAIIIIAAAggggAACCCCAgDkBghhzlpSEAAIIIIAAAggggAACCCCAAAIIOAoQxDjysBEBBBBAAAEEEEAAAQQQQAABBBAwJ0AQY86SkhBAAAEEEEAAAQQQQAABBBBAAAFHAYIYRx42IoAAAggggAACCCCAAAIIIIAAAuYECGLMWVISAggggAACCCCAAAIIIIAAAggg4ChAEOPIw0YEEEAAAQQQQAABBBBAAAEEEEDAnABBjDlLSkIAAQQQQAABBBBAAAEEEEAAAQQcBQhiHHnYiAACCCCAAAIIIIAAAggggAACCJgTIIgxZ0lJCCCAAAIIIIAAAggggAACCCCAgKMAQYwjDxsRQAABBBBAAAEEEEAAAQQQQAABcwIEMeYsKQkBBBBAAAEEEEAAAQQQQAABBBBwFCCIceRhIwIIIIAAAggggAACCCC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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Binning_step1.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "attachments": {
+    "Binning_step2.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Binning_step2.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bucketizing/Binning process objective: build dict of list of lists data structure\n",
+    "- Initialize an empty `dict`\n",
+    "- Iterate over every row in your dataset\n",
+    "    - Retrieve value of the column based on which you want to bucketize\n",
+    "    - Check if bucketizing column is already a key in your `dict`:\n",
+    "        - if no, insert a new key-value pair:\n",
+    "            - key: unique value of bucktizing column\n",
+    "            - value: initialize a new list, append current row as an item into the list, thereby creating a list of list data structure\n",
+    "        - if yes, append current row to the list of list data structure (value of the key).\n",
+    "\n",
+    "After this process, each row ends up in a bin, based on the value of the bucketize column.\n",
+    "Number of bins = number of unique values in the bucketize column\n",
+    "\n",
+    "Why bucketize data?\n",
+    "- A way to organize our data, without losing information in the process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Lecture',\n",
+       " 'Age',\n",
+       " 'Major',\n",
+       " 'Zip Code',\n",
+       " 'Latitude',\n",
+       " 'Longitude',\n",
+       " 'Pizza topping',\n",
+       " 'Pet preference',\n",
+       " 'Runner',\n",
+       " 'Sleep habit',\n",
+       " 'Procrastinator']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's take another look at our 'cs220_survey_data.csv'\n",
+    "cs220_header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's bucketize the data\n",
+    "buckets = dict() # Key: unique bucketize column value; Value: list of lists (rows having that unique column value)\n",
+    "\n",
+    "def bucketize(bucket_column):\n",
+    "    \"\"\"\n",
+    "    generates and returns bucketized data based on bucket_column\n",
+    "    \"\"\"\n",
+    "    # Key: unique bucketize column value; Value: list of lists (rows having that unique column value)\n",
+    "    buckets = dict()\n",
+    "    for row_idx in range(len(cs220_data)):\n",
+    "        col_value = cell(cs220_data, cs220_header, row_idx, bucket_column)\n",
+    "        if col_value not in buckets:\n",
+    "            buckets[col_value] = []\n",
+    "        buckets[col_value].append(cs220_data[row_idx])\n",
+    "        \n",
+    "    return buckets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LEC001': [['LEC001',\n",
+       "   '22',\n",
+       "   'Engineering: Biomedical',\n",
+       "   '53703',\n",
+       "   '43.073051',\n",
+       "   '-89.40123',\n",
+       "   'none (just cheese)',\n",
+       "   'neither',\n",
+       "   'No',\n",
+       "   'no preference',\n",
+       "   'Maybe'],\n",
+       "  ['LEC001',\n",
+       "   '',\n",
+       "   'Mathematics/AMEP',\n",
+       "   '53706',\n",
+       "   '31.230391',\n",
+       "   '121.473701',\n",
+       "   'basil/spinach',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'no preference',\n",
+       "   'Maybe'],\n",
+       "  ['LEC001',\n",
+       "   '20',\n",
+       "   'Economics (Mathematical Emphasis)',\n",
+       "   '53703',\n",
+       "   '48.86',\n",
+       "   '2.3522',\n",
+       "   'pepperoni',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'early bird',\n",
+       "   'Yes'],\n",
+       "  ['LEC001',\n",
+       "   '19',\n",
+       "   'Engineering: Mechanical',\n",
+       "   '53703',\n",
+       "   '24.7',\n",
+       "   '46.7',\n",
+       "   'mushroom',\n",
+       "   'dog',\n",
+       "   'Yes',\n",
+       "   'early bird',\n",
+       "   'Maybe'],\n",
+       "  ['LEC001',\n",
+       "   '23',\n",
+       "   'Computer Science',\n",
+       "   '53711',\n",
+       "   '43.073929',\n",
+       "   '-89.385239',\n",
+       "   'sausage',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC001',\n",
+       "   '22',\n",
+       "   'Engineering: Mechanical',\n",
+       "   '53719',\n",
+       "   '43.073051',\n",
+       "   '-89.40123',\n",
+       "   'none (just cheese)',\n",
+       "   'cat',\n",
+       "   'Yes',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC001',\n",
+       "   '18',\n",
+       "   'Computer Science',\n",
+       "   '53706',\n",
+       "   '26.2992',\n",
+       "   '87.2625',\n",
+       "   'mushroom',\n",
+       "   'dog',\n",
+       "   'Yes',\n",
+       "   'night owl',\n",
+       "   'No'],\n",
+       "  ['LEC001',\n",
+       "   '24',\n",
+       "   'Business: Information Systems',\n",
+       "   '53703',\n",
+       "   '43.073051',\n",
+       "   '-89.40123',\n",
+       "   'macaroni/pasta',\n",
+       "   'cat',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'No'],\n",
+       "  ['LEC001',\n",
+       "   '',\n",
+       "   'Computer Science',\n",
+       "   '53715',\n",
+       "   '34.052235',\n",
+       "   '-118.243683',\n",
+       "   'green pepper',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC001',\n",
+       "   '26',\n",
+       "   'Engineering: Mechanical',\n",
+       "   '53703',\n",
+       "   '41.902782',\n",
+       "   '12.496365',\n",
+       "   'pepperoni',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC001',\n",
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+       "   'Business: Actuarial',\n",
+       "   '53715',\n",
+       "   '43.073051',\n",
+       "   '-89.40123',\n",
+       "   'pineapple',\n",
+       "   'cat',\n",
+       "   'Yes',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Business: Actuarial',\n",
+       "   '53715',\n",
+       "   '60.391262',\n",
+       "   '5.322054',\n",
+       "   'pepperoni',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'early bird',\n",
+       "   'No'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Data Science',\n",
+       "   '53715',\n",
+       "   '23.697809',\n",
+       "   '120.960518',\n",
+       "   'pepperoni',\n",
+       "   'cat',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '18',\n",
+       "   'Data Science',\n",
+       "   '53706',\n",
+       "   '40.712776',\n",
+       "   '74.005974',\n",
+       "   'pineapple',\n",
+       "   'dog',\n",
+       "   'Yes',\n",
+       "   'early bird',\n",
+       "   'No'],\n",
+       "  ['LEC003',\n",
+       "   '21',\n",
+       "   'Statistics',\n",
+       "   '53703',\n",
+       "   '31.230391',\n",
+       "   '121.473701',\n",
+       "   'pineapple',\n",
+       "   'cat',\n",
+       "   'Yes',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Engineering: Mechanical',\n",
+       "   '53715',\n",
+       "   '65.68204',\n",
+       "   '-18.090534',\n",
+       "   'sausage',\n",
+       "   'cat',\n",
+       "   'No',\n",
+       "   'no preference',\n",
+       "   'No'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Engineering: Industrial',\n",
+       "   '53715',\n",
+       "   '41.73849',\n",
+       "   '-71.30418',\n",
+       "   'pepperoni',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '18',\n",
+       "   'Computer Science',\n",
+       "   '53706',\n",
+       "   '40.744678',\n",
+       "   '-73.758072',\n",
+       "   'mushroom',\n",
+       "   'cat',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Maybe'],\n",
+       "  ['LEC003',\n",
+       "   '18',\n",
+       "   'Undecided',\n",
+       "   '53706',\n",
+       "   '43.2967',\n",
+       "   '87.9876',\n",
+       "   'pepperoni',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'No'],\n",
+       "  ['LEC003',\n",
+       "   '18',\n",
+       "   'Data Science',\n",
+       "   '53706',\n",
+       "   '47.48',\n",
+       "   '-122.28',\n",
+       "   'basil/spinach',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'no preference',\n",
+       "   'Maybe'],\n",
+       "  ['LEC003',\n",
+       "   '20',\n",
+       "   'Science: Physics',\n",
+       "   '53715',\n",
+       "   '64.963051',\n",
+       "   '-19.020836',\n",
+       "   'mushroom',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '21',\n",
+       "   'Languages',\n",
+       "   '53511',\n",
+       "   '39.952583',\n",
+       "   '-75.165222',\n",
+       "   'pepperoni',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Engineering: Industrial',\n",
+       "   '53703',\n",
+       "   '11.89',\n",
+       "   '-85',\n",
+       "   'pepperoni',\n",
+       "   'dog',\n",
+       "   'Yes',\n",
+       "   'night owl',\n",
+       "   'Maybe'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Engineering: Mechanical',\n",
+       "   '53715',\n",
+       "   '33.873417',\n",
+       "   '-115.900993',\n",
+       "   'pepperoni',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'early bird',\n",
+       "   'No'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Engineering: Mechanical',\n",
+       "   '53715',\n",
+       "   '45.40857',\n",
+       "   '-91.73542',\n",
+       "   'sausage',\n",
+       "   'dog',\n",
+       "   'Yes',\n",
+       "   'no preference',\n",
+       "   'No'],\n",
+       "  ['LEC003',\n",
+       "   '18',\n",
+       "   'Engineering: Industrial',\n",
+       "   '53706',\n",
+       "   '20.798363',\n",
+       "   '-156.331924',\n",
+       "   'none (just cheese)',\n",
+       "   'dog',\n",
+       "   'Yes',\n",
+       "   'early bird',\n",
+       "   'No'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Undecided',\n",
+       "   '53715',\n",
+       "   '43.041069',\n",
+       "   '-87.909416',\n",
+       "   'mushroom',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'no preference',\n",
+       "   'Maybe'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Engineering: Mechanical',\n",
+       "   '53706',\n",
+       "   '43',\n",
+       "   '-88.27',\n",
+       "   'Other',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '20',\n",
+       "   'Business: Other|Accounting',\n",
+       "   '53726',\n",
+       "   '43',\n",
+       "   '-89',\n",
+       "   'pepperoni',\n",
+       "   'dog',\n",
+       "   'Yes',\n",
+       "   'early bird',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '18',\n",
+       "   'Data Science',\n",
+       "   '53562',\n",
+       "   '42.66544',\n",
+       "   '21.165319',\n",
+       "   'pepperoni',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '18',\n",
+       "   'Engineering: Mechanical',\n",
+       "   '53706',\n",
+       "   '33.748997',\n",
+       "   '-84.387985',\n",
+       "   'mushroom',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Business: Actuarial',\n",
+       "   '53706',\n",
+       "   '39.299236',\n",
+       "   '-76.609383',\n",
+       "   'pineapple',\n",
+       "   'dog',\n",
+       "   'Yes',\n",
+       "   'night owl',\n",
+       "   'No'],\n",
+       "  ['LEC003',\n",
+       "   '',\n",
+       "   'Engineering: Mechanical',\n",
+       "   '53706',\n",
+       "   '45.87128',\n",
+       "   '-89.711632',\n",
+       "   'pepperoni',\n",
+       "   'neither',\n",
+       "   'Yes',\n",
+       "   'no preference',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '',\n",
+       "   'Computer Science',\n",
+       "   '53703',\n",
+       "   '43.07',\n",
+       "   '-89.4',\n",
+       "   'pepperoni',\n",
+       "   'dog',\n",
+       "   'Yes',\n",
+       "   'no preference',\n",
+       "   'Maybe'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Engineering: Industrial',\n",
+       "   '53703',\n",
+       "   '40.712776',\n",
+       "   '-74.005974',\n",
+       "   'basil/spinach',\n",
+       "   'dog',\n",
+       "   'Yes',\n",
+       "   'night owl',\n",
+       "   'No'],\n",
+       "  ['LEC003',\n",
+       "   '18',\n",
+       "   'Data Science',\n",
+       "   '53706',\n",
+       "   '40.712776',\n",
+       "   '-74.005974',\n",
+       "   'Other',\n",
+       "   'dog',\n",
+       "   'Yes',\n",
+       "   'early bird',\n",
+       "   'No'],\n",
+       "  ['LEC003',\n",
+       "   '20',\n",
+       "   'Economics',\n",
+       "   '53703',\n",
+       "   '22.54',\n",
+       "   '114.05',\n",
+       "   'pineapple',\n",
+       "   'dog',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '18',\n",
+       "   'Data Science',\n",
+       "   '53706',\n",
+       "   '36.974117',\n",
+       "   '-122.030792',\n",
+       "   'pepperoni',\n",
+       "   'cat',\n",
+       "   'Yes',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '18',\n",
+       "   'Mathematics/AMEP',\n",
+       "   '53706',\n",
+       "   '42.99571',\n",
+       "   '-90',\n",
+       "   'sausage',\n",
+       "   'dog',\n",
+       "   'Yes',\n",
+       "   'night owl',\n",
+       "   'Yes'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Engineering: Industrial',\n",
+       "   '53715',\n",
+       "   '24.713552',\n",
+       "   '46.675297',\n",
+       "   'basil/spinach',\n",
+       "   'neither',\n",
+       "   'Yes',\n",
+       "   'early bird',\n",
+       "   'Maybe'],\n",
+       "  ['LEC003',\n",
+       "   '19',\n",
+       "   'Engineering: Mechanical',\n",
+       "   '53705',\n",
+       "   '46.589146',\n",
+       "   '-112.039108',\n",
+       "   'none (just cheese)',\n",
+       "   'cat',\n",
+       "   'No',\n",
+       "   'night owl',\n",
+       "   'Yes']]}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sec_buckets = bucketize(\"Lecture\")\n",
+    "sec_buckets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's convert the above code into a function called 'bucketize'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.61"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def col_avg(data, header, col_name, min_bound, max_bound):\n",
+    "    \"\"\"\n",
+    "    data: list of list data structure representing rows\n",
+    "    col_name: name of the column for which we want to compute average\n",
+    "    min_bound, max_bound: bounds for the data (data cleaning)\n",
+    "    Returns average of that column.\n",
+    "    \"\"\"\n",
+    "    total = 0\n",
+    "    count = 0\n",
+    "    for row_idx in range(len(data)):\n",
+    "        col_data = cell(data, header, row_idx, col_name)\n",
+    "        if col_data != None:\n",
+    "            # handle bounds checking\n",
+    "            if col_data < min_bound or col_data > max_bound:\n",
+    "                continue\n",
+    "            total += col_data\n",
+    "            count += 1\n",
+    "    \n",
+    "    if count != 0:\n",
+    "        return round(total / count, 2)\n",
+    "    else:\n",
+    "        return 0\n",
+    "  \n",
+    "min_age = 0\n",
+    "max_age = 118\n",
+    "col_avg(cs220_data, cs220_header, \"Age\", min_age, max_age)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Average per bucket"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def avg_per_bucket(buckets, avg_col_name):\n",
+    "    \"\"\"\n",
+    "    Computes and returns column average per bucket\n",
+    "    \"\"\"\n",
+    "    averages = {} # Key: bucket column; Value: average for that bucket\n",
+    "    for bucket_name in buckets:\n",
+    "        bucket_rows = buckets[bucket_name]\n",
+    "        averages[bucket_name] = col_avg(bucket_rows, cs220_header, avg_col_name, min_age, max_age)\n",
+    "    return averages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "scrolled": false
+   },
+   "source": [
+    "### What is the average student age per lecture?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LEC001': 20.05,\n",
+       " 'LEC006': 18.63,\n",
+       " 'LEC004': 19.99,\n",
+       " 'LEC005': 19.42,\n",
+       " 'LEC002': 19.68,\n",
+       " 'LEC003': 19.14}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg_per_bucket(sec_buckets, \"Age\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is the average student age in each major?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Engineering: Biomedical': 19.05,\n",
+       " 'Undecided': 18.22,\n",
+       " 'Engineering: Industrial': 18.86,\n",
+       " 'Engineering: Other|Engineering: Computer': 18.0,\n",
+       " 'Data Science': 19.09,\n",
+       " 'Mathematics/AMEP': 20.91,\n",
+       " 'Engineering: Other': 20.08,\n",
+       " 'Economics': 20.77,\n",
+       " 'Psychology': 20.57,\n",
+       " 'Science: Biology/Life': 20.22,\n",
+       " 'Engineering: Mechanical': 19.11,\n",
+       " 'Economics (Mathematical Emphasis)': 20.14,\n",
+       " 'Computer Science': 19.06,\n",
+       " 'Science: Other|Political Science': 21.0,\n",
+       " 'Business: Other': 21.12,\n",
+       " 'Business: Other|Real Estate': 19.5,\n",
+       " 'Engineering: Other|Engineering Physics: Scientific Computing': 20.0,\n",
+       " 'Business: Finance': 19.57,\n",
+       " 'Business: Information Systems': 20.29,\n",
+       " 'Statistics': 19.2,\n",
+       " 'Business: Actuarial': 19.24,\n",
+       " 'Science: Physics': 18.88,\n",
+       " 'Science: Other': 20.38,\n",
+       " 'Business: Other|Accounting': 20.5,\n",
+       " 'Business: Other|business analytics': 0,\n",
+       " 'Science: Other|animal sciences': 26.0,\n",
+       " 'Mathematics': 20.0,\n",
+       " 'Health Promotion and Health Equity': 20.5,\n",
+       " 'Art': 18.0,\n",
+       " 'Mathematics, Data Science': 21.0,\n",
+       " 'Science: Other|Science: Genetics and Genomics': 18.0,\n",
+       " 'Statistics (actuarial route)': 20.0,\n",
+       " 'Business: Other|Business: Accounting': 20.0,\n",
+       " 'Engineering: Other|Computer Engineering': 17.0,\n",
+       " 'Engineering: Other|Computer engineering': 0,\n",
+       " 'Engineering: Other|Material Science Engineering': 18.0,\n",
+       " 'Civil engineering - hydropower engineering': 0,\n",
+       " 'Science: Chemistry': 21.67,\n",
+       " 'Communication arts': 18.0,\n",
+       " 'Business andministration': 20.0,\n",
+       " 'Education': 19.5,\n",
+       " 'Pre-business': 18.0,\n",
+       " 'Science: Other|Environmental Science': 20.25,\n",
+       " 'History': 19.0,\n",
+       " 'Information science': 19.5,\n",
+       " 'consumer behavior and marketplace studies': 22.0,\n",
+       " 'Conservation Biology': 20.0,\n",
+       " 'Engineering: Other|Chemical Engineering': 22.0,\n",
+       " 'Science: Other|Biophysics PhD': 25.0,\n",
+       " 'Business: Other|Technology Strategy/ Product Management': 29.0,\n",
+       " 'Political Science': 19.33,\n",
+       " 'Graphic Design': 18.0,\n",
+       " 'Business: Other|Marketing': 20.0,\n",
+       " 'Cartography and GIS': 20.0,\n",
+       " 'Sociology': 20.5,\n",
+       " 'Business: Other|Consumer Behavior and Marketplace Studies': 20.0,\n",
+       " 'Atmospheric Sciences': 18.0,\n",
+       " 'Languages': 27.25,\n",
+       " 'Engineering Mechanics (Aerospace Engineering)': 18.0,\n",
+       " 'Science: Other|Psychology': 21.5,\n",
+       " 'Engineering: Other|Civil and Environmental Engineering': 24.0,\n",
+       " 'International Studies': 20.5,\n",
+       " 'Agricultural and Applied Economics': 20.0,\n",
+       " 'Business: Other|MHR': 21.0,\n",
+       " 'Medicine': 25.0,\n",
+       " 'Science: Other|Personal Finance': 21.0,\n",
+       " 'Environmental science': 18.0,\n",
+       " 'Geoscience': 31.0,\n",
+       " 'Business: Other|accounting': 19.0,\n",
+       " 'Design Studies': 32.0,\n",
+       " 'Science: Other|Environmetal Science': 0,\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences (AOS)': 20.0,\n",
+       " 'Business: Other|Business Analytics': 23.0,\n",
+       " 'Journalism': 19.0,\n",
+       " 'Science: Other|Politcal Science': 19.0,\n",
+       " 'Communication Sciences and Disorder': 32.0,\n",
+       " 'Science: Other|Geoscience': 25.0,\n",
+       " 'Science: Other|Atmospheric and oceanic science': 22.0,\n",
+       " 'Engineering: Other|Engineering Mechanics': 18.0,\n",
+       " 'Pre-Business': 19.0,\n",
+       " 'Industrial Engineering': 19.0,\n",
+       " 'Mechanical Engineering': 0,\n",
+       " 'Science: Other|Environmental science': 21.0,\n",
+       " 'Life Sciences Communication': 30.0,\n",
+       " 'Science: Other|Atmospheric and Oceanic Sciences': 19.0,\n",
+       " 'Rehabilitation Psychology': 19.0,\n",
+       " 'Accounting': 20.0,\n",
+       " 'Engineering: Other|Civil- Intelligent Transportation System': 37.0,\n",
+       " 'Science: Other|Animal and Dairy Science': 26.0,\n",
+       " 'Interior Architecture': 19.0,\n",
+       " 'Science: Other|Atmospheric & Oceanic Sciences': 20.0,\n",
+       " 'Computer Science and Statistics': 18.0,\n",
+       " 'Business analytics': 0,\n",
+       " 'Legal Studies': 20.0,\n",
+       " 'Journalism: Strategic Comm./Advertising': 20.0,\n",
+       " 'Master of Public Affairs': 26.0,\n",
+       " 'Environment & Resources': 27.0,\n",
+       " 'Environmental Studies': 18.0}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "major_buckets = bucketize(\"Major\")\n",
+    "avg_per_bucket(major_buckets, \"Age\")"
+   ]
+  },
+  {
+   "attachments": {
+    "table_rep.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Nesting part 2: Tables\n",
+    "### Use a list of dictionaries to represent a table of data.\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:table_rep.png\" width=\"600\"/>\n",
+    "</div>\n",
+    "\n",
+    "Steps (build a list of dictionaries)\n",
+    "- Start with an empty list\n",
+    "- Each row of data is one dictionary\n",
+    "    - keys are the column names\n",
+    "    - values are the data in each cell\n",
+    "\n",
+    "Why put data in table form?\n",
+    "- It seems redundant, but is used often in Web apps for storing info.\n",
+    "- Its a little easier to access subsets of the data without worrying about the header index method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Lecture',\n",
+       " 'Age',\n",
+       " 'Major',\n",
+       " 'Zip Code',\n",
+       " 'Latitude',\n",
+       " 'Longitude',\n",
+       " 'Pizza topping',\n",
+       " 'Pet preference',\n",
+       " 'Runner',\n",
+       " 'Sleep habit',\n",
+       " 'Procrastinator']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's put the student survey data into a list of dictionaries\n",
+    "cs220_header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def transform(header, data):\n",
+    "    \"\"\"\n",
+    "    Transform data into a list of dictionaries\n",
+    "    \"\"\"\n",
+    "    dict_list = [] #should be defined outside the for loop, because it stores the entire data\n",
+    "    \n",
+    "    for row in cs220_data:\n",
+    "        new_row = {} #should be defined inside the for loop, because it represents one row as a dictionary\n",
+    "        for i in range(len(cs220_header)):\n",
+    "            new_row[cs220_header[i]] = row[i]\n",
+    "        dict_list.append(new_row)\n",
+    "    return dict_list\n",
+    "        \n",
+    "transformed_data = transform(cs220_header, cs220_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What `Lecture` is the first student part of?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LEC001\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(transformed_data[0][\"Lecture\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is the `Major` of the last student?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Economics\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(transformed_data[-1][\"Major\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Nesting part 3: Dictionary of Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://www.w3schools.com/python/python_dictionaries_nested.asp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dict of dicts example:\n",
+    "\n",
+    "nested_english_dict = {\n",
+    "    \"shenanigans\": {\n",
+    "        \"definition\": \"silly or high-spirited behavior; mischief.\",\n",
+    "        \"usage\": \"widespread financial shenanigans had ruined the fortunes of many\",\n",
+    "        \"fun_to_say\": 7 # on a scale of 1-10\n",
+    "    },\n",
+    "    \"bamboozle\": {\n",
+    "        \"definition\": \"fool or cheat (someone).\",\n",
+    "        \"usage\": \"Tom Sawyer bamboozled the neighborhood boys into painting for him\",\n",
+    "        \"fun_to_say\": 8 # on a scale of 1-10\n",
+    "    },\n",
+    "    \"gubbins\": {\n",
+    "        \"definition\": \"(objects) of little to no value.\",\n",
+    "        \"usage\": \"I cleared all the gubbins off my desk before I started working\",\n",
+    "        \"fun_to_say\": 10 # on a scale of 1-10\n",
+    "    },\n",
+    "    \"malarkey\": {\n",
+    "        \"definition\": \"meaningless talk; nonsense.\",\n",
+    "        \"usage\": \"don't give me that malarkey\",\n",
+    "        \"fun_to_say\": 5 # on a scale of 1-10\n",
+    "    },\n",
+    "    \"gnarly\": {\n",
+    "        \"definition\": \"gnarled.\",\n",
+    "        \"usage\": \"twisted trees and gnarly roots\",\n",
+    "        \"fun_to_say\": 2 # on a scale of 1-10\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# TODO: pick a word and add an inner dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How can we use \"bamboozle\"?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Tom Sawyer bamboozled the neighborhood boys into painting for him'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_english_dict[\"bamboozle\"][\"usage\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a list of words with fun_to_say score greater than 7."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['bamboozle', 'gubbins']"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fun_to_say_words = []\n",
+    "\n",
+    "for word in nested_english_dict:\n",
+    "    fun_to_say = nested_english_dict[word][\"fun_to_say\"]\n",
+    "    if fun_to_say > 7:\n",
+    "        fun_to_say_words.append(word)\n",
+    "\n",
+    "fun_to_say_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/f22/meena_lec_notes/lec18_dictionaries2_template.ipynb b/f22/meena_lec_notes/lec18_dictionaries2_template.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..47542d9aa9a333048f2118ec2e1c0b8b8f075a22
--- /dev/null
+++ b/f22/meena_lec_notes/lec18_dictionaries2_template.ipynb
@@ -0,0 +1,598 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dictionaries 2 - Combining Dictionaries and Lists (nested data structures)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 1: Answer these questions about dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Keys can be what type? :     Any type that is ________________\n",
+    "# Values can be what type? :   \n",
+    "# Indexing? .... yes/no\n",
+    "# Slicing? ..... yes/no \n",
+    "# Mutable?......yes/no"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inspired by https://automatetheboringstuff.com/2e/chapter16/\n",
+    "def process_csv(filename):\n",
+    "    exampleFile = open(filename, encoding=\"utf-8\")  \n",
+    "    exampleReader = csv.reader(exampleFile) \n",
+    "    exampleData = list(exampleReader)        \n",
+    "    exampleFile.close()  \n",
+    "    return exampleData\n",
+    "\n",
+    "survey_data = process_csv('cs220_survey_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 2a: Split csv data into header and data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cs220_header = survey_data\n",
+    "cs220_data = survey_data\n",
+    "cs220_header"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 2b: Display the first 3 data rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cell(data, header, row_idx, col_name):\n",
+    "    \"\"\"\n",
+    "    Returns the data value (cell) corresponding to the row index and \n",
+    "    the column name of a CSV file.\n",
+    "    \"\"\"\n",
+    "    col_idx = header.index(col_name) \n",
+    "    val = data[row_idx][col_idx]  \n",
+    "    \n",
+    "    # handle missing values, by returning None\n",
+    "    if val == '':\n",
+    "        return None\n",
+    "    \n",
+    "    # handle type conversions\n",
+    "    if col_name in [\"Age\", 'Zip Code',]:\n",
+    "        return int(val)\n",
+    "    elif col_name in ['Latitude', 'Longitude']:\n",
+    "        return float(val)\n",
+    "    \n",
+    "    return val"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 3: Make a dictionary of frequency of `Major`\n",
+    "\n",
+    "- Initialize empty `dict` into a variable called `major_freq`\n",
+    "- Iterate over the data:\n",
+    "    - Extract required column's data\n",
+    "    - Make sure to handle missing data\n",
+    "    - Check if current value of the column is a key in your `dict`:\n",
+    "        - yes, update the count\n",
+    "        - no, insert new key-value pair"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: iterate over each student's data from cs220_data\n",
+    "# TODO: extract \"Major\" column's value \n",
+    "# TODO: check if current student's major already a key in major_freq\n",
+    "#            - if yes, increase the corresponding value by 1\n",
+    "#            - if no, insert a new key-value pair\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is the most common `Major` among CS220 / CS319 students?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "most_used_key = None  \n",
+    "max_value = None\n",
+    "\n",
+    "for major in major_freq:\n",
+    "    if max_value == None or major_freq[major] > max_value:\n",
+    "        max_value = major_freq[major]\n",
+    "        most_used_key = major\n",
+    "\n",
+    "print(\"The major \\\"{}\\\" appeared {} times.\".format(str(most_used_key), max_value))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning Objectives:\n",
+    " - Handle key errors with get and pop using default values\n",
+    " - Understand the idea of nesting data structures\n",
+    " - Use a dictionary of lists to put rows of data into \"buckets\"\n",
+    " - Use a list of dictionaries to represent a table of data.\n",
+    " - Create a dictionary of dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Default values with `get` and `pop` methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "suffix = {1: \"st\", 2: 'nd', 3: \"rd\"}\n",
+    "suffix.get(1)\n",
+    "\n",
+    "# TODO: what happens when you try to get a key that is not there? Try it.\n",
+    "\n",
+    "# TODO: what happens whey you try to pop a key that is not there? Try it.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`get` and `pop` methods accept a second argument, which will be the default value if the first argument (key) does not exist.\n",
+    "\n",
+    "Syntax:\n",
+    "- `some_dict.get(some_key, default_value)`\n",
+    "- `some_dict.pop(some_key, default_value)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get(key, default value) \n",
+    "print(suffix.get(3, 'th'))\n",
+    "print(suffix.get(5, 'th')) #default value, but does not add the key-value pair to the dict\n",
+    "\n",
+    "# pop(key, default value)\n",
+    "print(suffix.pop(7, 'th')) # no key-value pair to remove\n",
+    "print(suffix.pop(2, 'th'))\n",
+    "print(suffix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What are nested data structures?\n",
+    "A data structure containing another data structure as item is called as nest data structure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Nesting part 1: Bucketizing/Binning"
+   ]
+  },
+  {
+   "attachments": {
+    "Buckets.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Buckets.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "attachments": {
+    "Binning_step1.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Binning_step1.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "attachments": {
+    "Binning_step2.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Binning_step2.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bucketizing/Binning process objective: build dict of list of lists data structure\n",
+    "- Initialize an empty `dict`\n",
+    "- Iterate over every row in your dataset\n",
+    "    - Retrieve value of the column based on which you want to bucketize\n",
+    "    - Check if bucketizing column is already a key in your `dict`:\n",
+    "        - if no, insert a new key-value pair:\n",
+    "            - key: unique value of bucktizing column\n",
+    "            - value: initialize a new list, append current row as an item into the list, thereby creating a list of list data structure\n",
+    "        - if yes, append current row to the list of list data structure (value of the key).\n",
+    "\n",
+    "After this process, each row ends up in a bin, based on the value of the bucketize column.\n",
+    "Number of bins = number of unique values in the bucketize column\n",
+    "\n",
+    "Why bucketize data?\n",
+    "- A way to organize our data, without losing information in the process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's take another look at our 'cs220_survey_data.csv'\n",
+    "cs220_header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's bucketize the data\n",
+    "buckets = dict() # Key: unique bucketize column value; Value: list of lists (rows having that unique column value)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's convert the above code into a function called 'bucketize'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def col_avg(data, header, col_name, min_bound, max_bound):\n",
+    "    \"\"\"\n",
+    "    data: list of list data structure representing rows\n",
+    "    col_name: name of the column for which we want to compute average\n",
+    "    min_bound, max_bound: bounds for the data (data cleaning)\n",
+    "    Returns average of that column.\n",
+    "    \"\"\"\n",
+    "    pass\n",
+    "    \n",
+    "min_age = 0\n",
+    "max_age = 118\n",
+    "col_avg(cs220_data, cs220_header, \"Age\", min_age, max_age)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Average per bucket"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def avg_per_bucket(buckets, avg_col_name):\n",
+    "    \"\"\"\n",
+    "    Computes and returns column average per bucket\n",
+    "    \"\"\"\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "scrolled": false
+   },
+   "source": [
+    "### What is the average student age per lecture?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is the average student age in each major?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {
+    "table_rep.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Nesting part 2: Tables\n",
+    "### Use a list of dictionaries to represent a table of data.\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:table_rep.png\" width=\"600\"/>\n",
+    "</div>\n",
+    "\n",
+    "Steps (build a list of dictionaries)\n",
+    "- Start with an empty list\n",
+    "- Each row of data is one dictionary\n",
+    "    - keys are the column names\n",
+    "    - values are the data in each cell\n",
+    "\n",
+    "Why put data in table form?\n",
+    "- It seems redundant, but is used often in Web apps for storing info.\n",
+    "- Its a little easier to access subsets of the data without worrying about the header index method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's put the student survey data into a list of dictionaries\n",
+    "cs220_header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def transform(header, data):\n",
+    "    \"\"\"\n",
+    "    Transform data into a list of dictionaries\n",
+    "    \"\"\"\n",
+    "    \n",
+    "transformed_data = transform(cs220_header, cs220_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What `Lecture` is the first student part of?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is the `Major` of the last student?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Nesting part 3: Dictionary of Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://www.w3schools.com/python/python_dictionaries_nested.asp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dict of dicts example:\n",
+    "\n",
+    "nested_english_dict = {\n",
+    "    \"shenanigans\": {\n",
+    "        \"definition\": \"silly or high-spirited behavior; mischief.\",\n",
+    "        \"usage\": \"widespread financial shenanigans had ruined the fortunes of many\",\n",
+    "        \"fun_to_say\": 7 # on a scale of 1-10\n",
+    "    },\n",
+    "    \"bamboozle\": {\n",
+    "        \"definition\": \"fool or cheat (someone).\",\n",
+    "        \"usage\": \"Tom Sawyer bamboozled the neighborhood boys into painting for him\",\n",
+    "        \"fun_to_say\": 8 # on a scale of 1-10\n",
+    "    },\n",
+    "    \"gubbins\": {\n",
+    "        \"definition\": \"(objects) of little to no value.\",\n",
+    "        \"usage\": \"I cleared all the gubbins off my desk before I started working\",\n",
+    "        \"fun_to_say\": 10 # on a scale of 1-10\n",
+    "    },\n",
+    "    \"malarkey\": {\n",
+    "        \"definition\": \"meaningless talk; nonsense.\",\n",
+    "        \"usage\": \"don't give me that malarkey\",\n",
+    "        \"fun_to_say\": 5 # on a scale of 1-10\n",
+    "    },\n",
+    "    \"gnarly\": {\n",
+    "        \"definition\": \"gnarled.\",\n",
+    "        \"usage\": \"twisted trees and gnarly roots\",\n",
+    "        \"fun_to_say\": 2 # on a scale of 1-10\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# TODO: pick a word and add an inner dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How can we use \"bamboozle\"?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a list of words with fun_to_say score greater than 7."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}