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DS2-200p3c1Sol.py
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DS2-200p3c1Sol.py
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import matplotlib.pyplot as plt
plt.figure(figsize=(15,15))
plt.plot(0.0521327,0.793548,'ro',color='#000000')
plt.plot(0.0521327,0.729032,'ro',color='#000000')
plt.plot(0.056872,0.696774,'ro',color='#000000')
plt.plot(0.0853081,0.716129,'ro',color='#000000')
plt.plot(0.109005,0.741935,'ro',color='#000000')
plt.plot(0.113744,0.767742,'ro',color='#000000')
plt.plot(0.113744,0.851613,'ro',color='#000000')
plt.plot(0.0947867,0.851613,'ro',color='#000000')
plt.plot(0.0853081,0.819355,'ro',color='#000000')
plt.plot(0.0805687,0.793548,'ro',color='#000000')
plt.plot(0.104265,0.8,'ro',color='#000000')
plt.plot(0.0616114,0.748387,'ro',color='#000000')
plt.plot(0.042654,0.767742,'ro',color='#000000')
plt.plot(0.00473934,0.780645,'ro',color='#000000')
plt.plot(0.0189573,0.819355,'ro',color='#000000')
plt.plot(0.028436,0.832258,'ro',color='#000000')
plt.plot(0.07109,0.845161,'ro',color='#000000')
plt.plot(0.0995261,0.870968,'ro',color='#000000')
plt.plot(0.0900474,0.877419,'ro',color='#000000')
plt.plot(0.07109,0.890323,'ro',color='#000000')
plt.plot(0.0663507,0.890323,'ro',color='#000000')
plt.plot(0.0236967,0.896774,'ro',color='#000000')
plt.plot(0.00947867,0.877419,'ro',color='#000000')
plt.plot(0,0.851613,'ro',color='#000000')
plt.plot(0,0.8,'ro',color='#000000')
plt.plot(0,0.754839,'ro',color='#000000')
plt.plot(0,0.735484,'ro',color='#000000')
plt.plot(0.00473934,0.716129,'ro',color='#000000')
plt.plot(0.028436,0.716129,'ro',color='#000000')
plt.plot(0.0663507,0.709677,'ro',color='#000000')
plt.plot(0.0758294,0.716129,'ro',color='#000000')
plt.plot(0.123223,0.651613,'ro',color='#000000')
plt.plot(0.056872,0.580645,'ro',color='#000000')
plt.plot(0.161137,0.541935,'ro',color='#000000')
plt.plot(0.0805687,0.496774,'ro',color='#000000')
plt.plot(0.104265,0.425806,'ro',color='#000000')
plt.plot(0.175355,0.406452,'ro',color='#000000')
plt.plot(0.251185,0.445161,'ro',color='#000000')
plt.plot(0.0805687,0.348387,'ro',color='#000000')
plt.plot(0.156398,0.303226,'ro',color='#000000')
plt.plot(0.113744,0.283871,'ro',color='#000000')
plt.plot(0.118483,0.232258,'ro',color='#000000')
plt.plot(0.165877,0.180645,'ro',color='#000000')
plt.plot(0.199052,0.174194,'ro',color='#000000')
plt.plot(0.170616,0.270968,'ro',color='#000000')
plt.plot(0.236967,0.283871,'ro',color='#000000')
plt.plot(0.227488,0.316129,'ro',color='#000000')
plt.plot(0.203791,0.329032,'ro',color='#000000')
plt.plot(0.21327,0.270968,'ro',color='#000000')
plt.plot(0.208531,0.232258,'ro',color='#000000')
plt.plot(0.161137,0.23871,'ro',color='#000000')
plt.plot(0.151659,0.212903,'ro',color='#000000')
plt.plot(0.189573,0.154839,'ro',color='#000000')
plt.plot(0.194313,0.0903226,'ro',color='#000000')
plt.plot(0.222749,0.141935,'ro',color='#000000')
plt.plot(0.222749,0.16129,'ro',color='#000000')
plt.plot(0.241706,0.174194,'ro',color='#000000')
plt.plot(0.236967,0.193548,'ro',color='#000000')
plt.plot(0.21327,0.212903,'ro',color='#000000')
plt.plot(0.194313,0.232258,'ro',color='#000000')
plt.plot(0.14218,0.206452,'ro',color='#000000')
plt.plot(0.118483,0.2,'ro',color='#000000')
plt.plot(0.118483,0.141935,'ro',color='#000000')
plt.plot(0.146919,0.141935,'ro',color='#000000')
plt.plot(0.170616,0.148387,'ro',color='#000000')
plt.plot(0.222749,0.167742,'ro',color='#000000')
plt.plot(0.241706,0.167742,'ro',color='#000000')
plt.plot(0.246445,0.219355,'ro',color='#000000')
plt.plot(0.260664,0.251613,'ro',color='#000000')
plt.plot(0.246445,0.277419,'ro',color='#000000')
plt.plot(0.184834,0.322581,'ro',color='#000000')
plt.plot(0.156398,0.322581,'ro',color='#000000')
plt.plot(0.127962,0.303226,'ro',color='#000000')
plt.plot(0.118483,0.270968,'ro',color='#000000')
plt.plot(0.0995261,0.232258,'ro',color='#000000')
plt.plot(0.118483,0.193548,'ro',color='#000000')
plt.plot(0.118483,0.154839,'ro',color='#000000')
plt.plot(0.123223,0.116129,'ro',color='#000000')
plt.plot(0.137441,0.109677,'ro',color='#000000')
plt.plot(0.170616,0.103226,'ro',color='#000000')
plt.plot(0.21327,0.135484,'ro',color='#000000')
plt.plot(0.236967,0.180645,'ro',color='#000000')
plt.plot(0.265403,0.180645,'ro',color='#000000')
plt.plot(0.260664,0.212903,'ro',color='#000000')
plt.plot(0.255924,0.16129,'ro',color='#000000')
plt.plot(0.236967,0.122581,'ro',color='#000000')
plt.plot(0.218009,0.103226,'ro',color='#000000')
plt.plot(0.227488,0.0774194,'ro',color='#000000')
plt.plot(0.265403,0.109677,'ro',color='#000000')
plt.plot(0.251185,0.0774194,'ro',color='#000000')
plt.plot(0.236967,0.0580645,'ro',color='#000000')
plt.plot(0.203791,0.0580645,'ro',color='#000000')
plt.plot(0.184834,0.0451613,'ro',color='#000000')
plt.plot(0.189573,0.0193548,'ro',color='#000000')
plt.plot(0.203791,0.00645161,'ro',color='#000000')
plt.plot(0.236967,0.00645161,'ro',color='#000000')
plt.plot(0.255924,0,'ro',color='#000000')
plt.plot(0.232227,0.0516129,'ro',color='#000000')
plt.plot(0.255924,0.0516129,'ro',color='#000000')
plt.plot(0.274882,0.0516129,'ro',color='#000000')
plt.plot(0.341232,0.709677,'ro',color='#000000')
plt.plot(0.28436,0.716129,'ro',color='#000000')
plt.plot(0.246445,0.651613,'ro',color='#000000')
plt.plot(0.298578,0.619355,'ro',color='#000000')
plt.plot(0.265403,0.56129,'ro',color='#000000')
plt.plot(0.298578,0.496774,'ro',color='#000000')
plt.plot(0.345972,0.516129,'ro',color='#000000')
plt.plot(0.417062,0.6,'ro',color='#000000')
plt.plot(0.469194,0.548387,'ro',color='#000000')
plt.plot(0.402844,0.670968,'ro',color='#000000')
plt.plot(0.364929,0.741935,'ro',color='#000000')
plt.plot(0.383886,0.787097,'ro',color='#000000')
plt.plot(0.521327,0.741935,'ro',color='#000000')
plt.plot(0.57346,0.683871,'ro',color='#000000')
plt.plot(0.630332,0.696774,'ro',color='#000000')
plt.plot(0.677725,0.703226,'ro',color='#000000')
plt.plot(0.725118,0.735484,'ro',color='#000000')
plt.plot(0.777251,0.76129,'ro',color='#000000')
plt.plot(0.834123,0.832258,'ro',color='#000000')
plt.plot(0.78673,0.864516,'ro',color='#000000')
plt.plot(0.701422,0.877419,'ro',color='#000000')
plt.plot(0.649289,0.864516,'ro',color='#000000')
plt.plot(0.578199,0.845161,'ro',color='#000000')
plt.plot(0.521327,0.812903,'ro',color='#000000')
plt.plot(0.559242,0.793548,'ro',color='#000000')
plt.plot(0.620853,0.806452,'ro',color='#000000')
plt.plot(0.587678,0.754839,'ro',color='#000000')
plt.plot(0.611374,0.767742,'ro',color='#000000')
plt.plot(0.630332,0.780645,'ro',color='#000000')
plt.plot(0.668246,0.780645,'ro',color='#000000')
plt.plot(0.71564,0.806452,'ro',color='#000000')
plt.plot(0.791469,0.774194,'ro',color='#000000')
plt.plot(0.696682,0.774194,'ro',color='#000000')
plt.plot(0.71564,0.76129,'ro',color='#000000')
plt.plot(0.796209,0.774194,'ro',color='#000000')
plt.plot(0.881517,0.806452,'ro',color='#000000')
plt.plot(0.933649,0.819355,'ro',color='#000000')
plt.plot(0.962085,0.845161,'ro',color='#000000')
plt.plot(1,0.890323,'ro',color='#000000')
plt.plot(0.92891,0.929032,'ro',color='#000000')
plt.plot(0.848341,0.916129,'ro',color='#000000')
plt.plot(0.791469,0.903226,'ro',color='#000000')
plt.plot(0.767773,0.877419,'ro',color='#000000')
plt.plot(0.876777,0.909677,'ro',color='#000000')
plt.plot(0.914692,0.883871,'ro',color='#000000')
plt.plot(0.834123,0.864516,'ro',color='#000000')
plt.plot(0.767773,0.832258,'ro',color='#000000')
plt.plot(0.691943,0.825806,'ro',color='#000000')
plt.plot(0.611374,0.83871,'ro',color='#000000')
plt.plot(0.530806,0.825806,'ro',color='#000000')
plt.plot(0.530806,0.767742,'ro',color='#000000')
plt.plot(0.559242,0.76129,'ro',color='#000000')
plt.plot(0.606635,0.748387,'ro',color='#000000')
plt.plot(0.563981,0.722581,'ro',color='#000000')
plt.plot(0.616114,0.722581,'ro',color='#000000')
plt.plot(0.672986,0.748387,'ro',color='#000000')
plt.plot(0.7109,0.819355,'ro',color='#000000')
plt.plot(0.734597,0.832258,'ro',color='#000000')
plt.plot(0.772512,0.83871,'ro',color='#000000')
plt.plot(0.909953,0.832258,'ro',color='#000000')
plt.plot(0.962085,0.935484,'ro',color='#000000')
plt.plot(0.957346,0.954839,'ro',color='#000000')
plt.plot(0.976303,1,'ro',color='#000000')
plt.plot(0.895735,0.974194,'ro',color='#000000')
plt.plot(0.85782,0.974194,'ro',color='#000000')
plt.plot(0.805687,0.941935,'ro',color='#000000')
plt.plot(0.781991,0.909677,'ro',color='#000000')
plt.plot(0.720379,0.858065,'ro',color='#000000')
plt.plot(0.691943,0.864516,'ro',color='#000000')
plt.plot(0.71564,0.929032,'ro',color='#000000')
plt.plot(0.748815,0.929032,'ro',color='#000000')
plt.plot(0.739336,0.929032,'ro',color='#000000')
plt.plot(0.701422,0.909677,'ro',color='#000000')
plt.plot(0.611374,0.883871,'ro',color='#000000')
plt.plot(0.592417,0.825806,'ro',color='#000000')
plt.plot(0.64455,0.774194,'ro',color='#000000')
plt.plot(0.71564,0.787097,'ro',color='#000000')
plt.plot(0.777251,0.83871,'ro',color='#000000')
plt.plot(0.815166,0.832258,'ro',color='#000000')
plt.plot(0.843602,0.832258,'ro',color='#000000')
plt.plot(0.900474,0.845161,'ro',color='#000000')
plt.plot(0.933649,0.851613,'ro',color='#000000')
plt.plot(1,0.896774,'ro',color='#000000')
plt.plot(0.943128,0.929032,'ro',color='#000000')
plt.plot(0.886256,0.935484,'ro',color='#000000')
plt.plot(0.838863,0.909677,'ro',color='#000000')
plt.plot(0.734597,0.883871,'ro',color='#000000')
plt.plot(0.649289,0.883871,'ro',color='#000000')
plt.plot(0.601896,0.883871,'ro',color='#000000')
plt.plot(0.56872,0.825806,'ro',color='#000000')
plt.plot(0.57346,0.767742,'ro',color='#000000')
plt.plot(0.630332,0.767742,'ro',color='#000000')
plt.plot(0.720379,0.793548,'ro',color='#000000')
plt.plot(0.767773,0.812903,'ro',color='#000000')
plt.plot(0.810427,0.825806,'ro',color='#000000')
plt.plot(0.848341,0.870968,'ro',color='#000000')
plt.plot(0.867299,0.909677,'ro',color='#000000')
plt.plot(0.895735,0.948387,'ro',color='#000000')
plt.plot(0.886256,0.954839,'ro',color='#000000')
plt.plot(0.886256,0.993548,'ro',color='#000000')
plt.show()