In this lab we’re going to apply all we’ve learnt so far about the built-in data types and basic language features with some real-world data from the OpenNeuroDatasets.
Exercises 1
Here’s some data from a public experiment .
Code
= """ \ onset duration trial_type rewards 13 0 options_partner n/a 16 0 decision_risky_partner 25 20.072093 0 outcome_other_neg_partner 0 27.661415 0 options_solo 0 33.796415 0 decision_safe_solo 0 42.856121 0 outcome_safe_solo 0 45.391388 0 happiness_rating 34 56.883578 0 options_social n/a 65.835578 0 decision_risky_social 21 74.897329 0 outcome_other_pos_social 0 85.472048 0 options_solo 0 95.852048 0 decision_safe_solo 0 108.889945 0 outcome_safe_solo 0 111.425148 0 happiness_rating 32 125.419236 0 options_partner 20 128.419236 0 decision_safe_partner 20 132.457838 0 outcome_safe_partner 20 142.032034 0 options_social 10 145.179034 0 decision_safe_social 10 152.239768 0 outcome_safe_social 10 154.774905 0 happiness_rating 36 170.170194 0 options_solo 0 182.227194 0 decision_safe_solo 0 190.285498 0 outcome_safe_solo 0 197.857785 0 options_social -25 212.906785 0 decision_safe_social -25 218.957288 0 outcome_safe_social -25 221.492477 0 happiness_rating 34 232.567507 0 options_social 15 242.986507 0 decision_safe_social 15 250.031015 0 outcome_safe_social 15 257.603337 0 options_partner n/a 260.603337 0 decision_risky_partner 18.5 265.909711 0 outcome_other_pos_partner 0 268.444998 0 happiness_rating 34 277.402022 0 options_partner n/a 280.402022 0 decision_risky_partner 7 284.30704 0 outcome_other_neg_partner 0 290.878961 0 options_solo n/a 299.346961 0 decision_risky_solo 12.5 306.390476 0 outcome_self_neg_solo 0 308.925866 0 happiness_rating 34 319.617418 0 options_solo 30 329.170418 0 decision_safe_solo 30 336.230193 0 outcome_safe_solo 30 348.806325 0 options_social n/a 352.850325 0 decision_risky_social 10 362.900485 0 outcome_other_neg_social 0 365.43567 0 happiness_rating 36 378.229089 0 options_partner -15 381.229089 0 decision_safe_partner -15 387.035498 0 outcome_safe_partner -15 395.608814 0 options_solo -20 400.666814 0 decision_safe_solo -20 409.71943 0 outcome_safe_solo -20 412.254679 0 happiness_rating 36 425.698328 0 options_partner -10 428.698328 0 decision_safe_partner -10 432.586891 0 outcome_safe_partner -10 441.160085 0 options_solo 10 450.962085 0 decision_safe_solo 10 462.009382 0 outcome_safe_solo 10 464.544614 0 happiness_rating 36 475.570105 0 options_partner 0 478.570105 0 decision_safe_partner 0 484.826744 0 outcome_safe_partner 0 497.386636 0 options_partner n/a 500.386636 0 decision_risky_partner 24.5 505.04221 0 outcome_other_neg_partner 0 507.577445 0 happiness_rating 38 523.506321 0 options_solo -25 533.417321 0 decision_safe_solo -25 546.457133 0 outcome_safe_solo -25 554.029535 0 options_partner 10 557.029535 0 decision_safe_partner 10 560.934817 0 outcome_safe_partner 10 563.470099 0 happiness_rating 38 580.800034 0 options_partner 0 583.800034 0 decision_safe_partner 0 588.005449 0 outcome_safe_partner 0 597.579626 0 options_partner n/a 600.579626 0 decision_risky_partner 27 604.401331 0 outcome_other_neg_partner 0 606.936571 0 happiness_rating 36 618.979118 0 options_solo 0 625.361118 0 decision_safe_solo 0 633.406885 0 outcome_safe_solo 0 639.978503 0 options_social n/a 646.360503 0 decision_risky_social 8.5 652.404718 0 outcome_other_neg_social 0 654.939858 0 happiness_rating 40 668.467306 0 options_partner -30 671.467306 0 decision_safe_partner -30 676.439628 0 outcome_safe_partner -30 684.012275 0 options_partner n/a 687.012275 0 decision_risky_partner 42 692.068261 0 outcome_other_neg_partner 0 694.603454 0 happiness_rating 38 707.663534 0 options_social n/a 712.080534 0 decision_risky_social 4.5 725.126869 0 outcome_other_pos_social 0 732.699192 0 options_solo -25 739.904192 0 decision_safe_solo -25 749.962459 0 outcome_safe_solo -25 752.497674 0 happiness_rating 36 764.590285 0 options_partner -20 767.590285 0 decision_safe_partner -20 771.662329 0 outcome_safe_partner -20 779.234763 0 options_solo -25 785.175763 0 decision_safe_solo -25 792.228033 0 outcome_safe_solo -25 794.763228 0 happiness_rating 36 809.024403 0 options_partner n/a 812.024403 0 decision_risky_partner 13.5 816.046131 0 outcome_other_neg_partner 0 822.61811 0 options_social n/a 827.36811 0 decision_risky_social 54 836.428364 0 outcome_other_pos_social 0 838.963641 0 happiness_rating 34 857.444384 0 options_social -20 868.535384 0 decision_safe_social -20 877.5931 0 outcome_safe_social -20 886.166317 0 options_solo -10 894.735317 0 decision_safe_solo -10 902.795647 0 outcome_safe_solo -10 905.330966 0 happiness_rating 34 919.60851 0 options_partner -30 922.60851 0 decision_safe_partner -30 928.932253 0 outcome_safe_partner -30 936.504693 0 options_social -30 939.278693 0 decision_safe_social -30 949.331248 0 outcome_safe_social -30 951.866476 0 happiness_rating 34 963.108331 0 options_social -20 973.383331 0 decision_safe_social -20 984.441376 0 outcome_safe_social -20 995.01607 0 options_social n/a 1001.53507 0 decision_risky_social 21 1009.593852 0 outcome_other_pos_social 0 1012.129125 0 happiness_rating 36 1027.791092 0 options_social -15 1032.734092 0 decision_safe_social -15 1043.786732 0 outcome_safe_social -15 1054.361298 0 options_social 30 1061.830298 0 decision_safe_social 30 1070.890669 0 outcome_safe_social 30 1073.425867 0 happiness_rating 38 1083.566977 0 options_social n/a 1090.877977 0 decision_risky_social 21 1101.93096 0 outcome_other_neg_social 0 1115.490485 0 options_partner -30 1118.490485 0 decision_safe_partner -30 1122.89696 0 outcome_safe_partner -30 1125.432252 0 happiness_rating 38 1136.907599 0 options_solo 0 1141.245599 0 decision_safe_solo 0 1150.301231 0 outcome_safe_solo 0 1161.876605 0 options_solo 0 1167.021605 0 decision_safe_solo 0 1177.071675 0 outcome_safe_solo 0 1179.606882 0 happiness_rating 36 1192.600313 0 options_solo n/a 1200.694313 0 decision_risky_solo -7.5 1211.748317 0 outcome_self_pos_solo 0 1217.318979 0 options_social n/a 1225.156979 0 decision_risky_social 0 1233.214511 0 outcome_other_pos_social 0 1235.749697 0 happiness_rating 34 1253.413602 0 options_solo 30 1265.045602 0 decision_safe_solo 30 1273.095128 0 outcome_safe_solo 30 1280.667331 0 options_partner 0 1283.667331 0 decision_safe_partner 0 1291.525599 0 outcome_safe_partner 0 1294.060885 0 happiness_rating 34 1301.933544 0 options_social n/a 1307.381544 0 decision_risky_social 9 1317.42882 0 outcome_other_pos_social 0 1326.001946 0 options_solo 25 1332.868946 0 decision_safe_solo 25 1339.929323 0 outcome_safe_solo 25 1342.464466 0 happiness_rating 32 1355.758003 0 options_partner 30 1358.758003 0 decision_safe_partner 30 1367.350136 0 outcome_safe_partner 30 1373.921441 0 options_solo -25 1380.220441 0 decision_safe_solo -25 1388.266282 0 outcome_safe_solo -25 1390.801237 0 happiness_rating 34 1408.081464 0 options_partner -15 1411.081464 0 decision_safe_partner -15 1417.688532 0 outcome_safe_partner -15 1427.263315 0 options_solo 0 1431.404315 0 decision_safe_solo 0 1439.455158 0 outcome_safe_solo 0 1441.990402 0 happiness_rating 34 1453.349179 0 options_social -15 1457.004179 0 decision_safe_social -15 1463.056746 0 outcome_safe_social -15 1471.629709 0 options_social n/a 1475.652709 0 decision_risky_social 6 1485.707198 0 outcome_other_neg_social 0 1488.24238 0 happiness_rating 36 1499.9513 0 options_solo -25 1504.8203 0 decision_safe_solo -25 1510.87643 0 outcome_safe_solo -25 1523.452588 0 options_social 10 1530.307588 0 decision_safe_social 10 1539.364813 0 outcome_safe_social 10 1541.89997 0 happiness_rating 36 """
Copy the data into a python file
Traverse and print the lines. Hint: Look at strings methods
Make a list of all the lines
Split each line into a tuple of 4 elements (representing onset, duration, trial_type and rewards)
Do the same but with dictionaries, that is, for each line you should get a dictionary that looks like this
"onset" : some- value,"duration" : some- value,"trial_type" : some- value,"rewards" : some- value,
Sort the list of dictionaries according to the rewards value . Hint: Discard missing reward values.
Repeat the same but in descendant order (larger values first).
Make a list of the unique “trial_type” values that appear in the data.
Make a dictionary that counts the number of appearances of each “trial_type”.
Make a list of only the entries where “trial_type” is of type “decision_SOMETHING”
Print for each line a string that follows this pattern:
"For the trial type: <TRIAL-TYPE> the reward is <REWARD-VALUE>"
Write a function called cleanup that takes the raw input string and returns a list of dictionaries (like exercise item 5)